Comparing HashiCorp Vault, Infisical, Doppler, and 1Password, the vendor-neutral options for database credentials that work across clouds and on-prem.

Best Secrets Manager for Database Credentials in 2026: Vault vs Infisical vs Doppler

DbUp runs your .NET migrations. Bytebase governs the database: change, access, and compliance in one platform. A practitioner comparison.

Bytebase vs. DbUp: from migration runner to unified governance

PopSQL shuts down on September 1, 2026. Compare Bytebase and PopSQL across query collaboration, supported databases, access control, data masking, audit logging, and pricing before you migrate.

Bytebase vs. PopSQL: a side-by-side comparison for migrating teams

The best multi-database MCP servers in 2026. Compare DBHub, Google's MCP Toolbox for Databases, and Bytebase on engine breadth, host neutrality, and governance for connecting AI agents to more than one database. Single-engine Postgres servers are covered in a companion post.

Top 3 Open Source Multi-Database MCP Servers in 2026

The best open source Postgres MCP servers in 2026. Compare Postgres MCP Pro, Supabase MCP, and pgconsole on host neutrality, access model, and governance for connecting AI agents to PostgreSQL. Multi-database MCP servers are covered in a companion post.

Bytebase vs. SchemaHero: a side-by-side comparison for database schema migration

CyberArk is a broad privileged access management (PAM) platform. Bytebase is a database-specific PAM for access control, masking, and SQL review. If you already run CyberArk, here is why you still need it for the database.

Bytebase vs. CyberArk: the database vertical in your PAM stack

PAM tools gate the database connection. Databases also need SQL review, a schema change workflow, and per-statement approval. Here is where PAM stops and why most teams run it alongside Bytebase.

Bytebase vs. PAM: Why Database Teams Need More Than Privileged Access Management

A side-by-side look at Bytebase and Atlas for database schema migration: how each one approaches migrations, their CI/CD and access control, supported databases, and what they cost. Updated for Atlas v1.1 and Bytebase 3.16.

Bytebase vs. Atlas: a side-by-side comparison for database schema migration

Convex and Supabase are both backend-in-a-box platforms, but built on different foundations. This post compares their architecture, real-time model, data model, schema management, and pricing to help you decide.

Convex vs Supabase: Which Backend Platform Should You Choose?

Compare Snowflake Dynamic Data Masking with database views and Bytebase fleet-wide masking on edition gate, policy management, and audit trail.

Snowflake Dynamic Data Masking (DDM) and Alternatives

Supabase and MongoDB take different paths to storing application data. This article compares them on data model, architecture, performance, pricing, and licensing to help you decide which one fits.

Supabase vs. MongoDB: a Complete Comparison in 2026

DataGrip is a SQL IDE built for individual developers; Bytebase is a web platform built for teams that need review and governance. This post compares their database support, features, and pricing to help you decide which fits.

Bytebase vs. DataGrip: a side-by-side comparison for database management

Supabase pricing starts free. We break down real costs for Pro ($25/mo) and Team ($599/mo) plans, explain overages, and compare each service head-to-head with AWS.

Supabase vs AWS: Feature and Pricing Comparison (2026)

Is Supabase free? Compare Supabase pricing vs AWS RDS and Aurora across free tier, Pro, and production plans. Includes Supabase free tier limits, storage costs, and compute pricing for 2026.

Supabase Pricing vs AWS RDS: Is Supabase Free? Full Cost Comparison (2026)

Evaluate Prisma and TypeORM, and help you to choose the right TypeScript ORM for 2026

Prisma vs TypeORM: The Better TypeScript ORM in 2026

An extensive comparison between DynamoDB and Cassandra on performance, usability, operability, ecosystem and more.

DynamoDB vs. Cassandra: a Complete Comparison in 2025

An extensive comparison between DynamoDB and MongoDB on performance, usability, operability, ecosystem and more.

RDS vs. DynamoDB: a Complete Comparison in 2025

How gh-ost and pt-online-schema-change compare on architecture, cut-over, foreign keys, and runtime control — for teams choosing a MySQL online schema migration tool.

gh-ost vs pt-online-schema-change

GitHub vs Bitbucket in 2026: how the two Git platforms compare on hosting, CI/CD, AI coding agents, security, and pricing, and which one to pick.

GitHub vs Bitbucket: a Complete Comparison in 2026

Drizzle ORM vs Prisma comparison: type safety, performance, bundle size, serverless support, migrations, and ecosystem. Find out which TypeScript ORM is right for your project in 2026.

Drizzle ORM vs Prisma: Which TypeScript ORM Should You Use in 2026?

An engineering perspective to evaluate Google AlloyDB and Cloud SQL, and help you to choose the right Google Cloud database for 2025

AlloyDB vs. Cloud SQL: engineering guide to choose the right Google Cloud database for 2025

A simple comparison of AWS Aurora versus RDS pricing in 2025, helping you understand key differences and choose the right database service for your needs and budget.

AWS Aurora vs. RDS Pricing: A Detailed Comparison 2025

An extensive comparison between GitLab CI and GitHub Actions on features, performance, and more.

GitLab CI vs. GitHub Actions: a Complete Comparison in 2025

An extensive comparison between Oracle and PostgreSQL on data model, query language, scalability, performance, and more.

Oracle vs. PostgreSQL: a Complete Comparison in 2026

An extensive comparison between MongoDB and MySQL on data model, query language, scalability, performance, and more.

MongoDB vs MySQL: a Complete Comparison in 2025

An extensive comparison between MongoDB and Redis on performance, usability, operability, ecosystem and more.

MongoDB vs Redis: a Complete Comparison in 2025

DynamoDB and PostgreSQL are both popular databases, but they are different. This post compares them based on their key differences, strengths, and ideal use cases.

DynamoDB vs PostgreSQL: A Concise Comparison

Supabase vs. Firebase compared across database, auth, real-time, serverless functions, AI, local dev, self-hosting, and pricing — updated for 2026 with Firebase Data Connect, Supabase Branching, and the latest product shifts.

Supabase vs. Firebase: a Complete Comparison in 2026

An extensive comparison between Snowflake and Redshift on features, architecture, development workflow, operability, pricing and more.

Snowflake vs. Redshift: a Complete Comparison in 2026

DynamoDB vs MongoDB: compare AWS DynamoDB and MongoDB on data model, query capabilities, scalability, pricing, global distribution, and security. Includes use case recommendations and real-world examples.

DynamoDB vs MongoDB: Which NoSQL Database Should You Choose? (2026)

A Strategic Comparison Between DevOps and Platform Engineering

Platform Engineering vs. DevOps: A Strategic Comparison in 2025

An extensive comparison between CockroachDB and PostgreSQL on license, architecture, performance, scalability, compatibility, deployment options.

CockroachDB vs. Postgres: a Complete Comparison in 2025

An extensive comparison between TiDB and MySQL on license, architecture, performance, scalability, online ddl, compatibility, deployment options.

TiDB vs. MySQL: a Complete Comparison in 2025

DevOps and DevSecOps are methodologies aimed at improving software development and delivery, but they differ in their focus and integration of security.

DevOps vs. DevSecOps: What's the Difference

MySQL 9.7 LTS vs SQL Server 2025: real license math (per-server vs per-core), concurrency model differences, edition gating, and an honest decision framework.

MySQL vs SQL Server: Key Differences and Which to Choose (2026)

An extensive comparison between PostgreSQL 18 and SQL Server 2025 — features, architecture, query optimization, development workflow, pricing, and a decision framework for picking the right one in 2026.

Postgres vs. SQL Server: a Complete Comparison in 2026

An extensive comparison between pgAdmin and DBeaver on features, user experience, database support, extensibility, performance, and more.

pgAdmin vs. DBeaver: a Complete Comparison in 2026

An extensive comparison between TiDB and CockroachDB on license, architecture, compatibility, scalability

TiDB vs. CockroachDB: the Distributed Clash between MySQL and PostgreSQL

Bytebase vs StrongDM in 2026: a side-by-side comparison for database just-in-time (JIT) access control, covering permission models, SQL review, data masking, approval flow, audit log, and pricing.

Bytebase vs. StrongDM: a side-by-side comparison for database access control and JIT

An extensive comparison between GitHub and GitLab on history, deployment model, tech stack, repository management, code review, code search, ci/cd, security, project management, ai, pricing

GitHub vs. GitLab: a Complete Comparison in 2025

MySQL vs. MariaDB in 2026: licensing, features, vector search, cloud support, and where each shines. Updated for MySQL 9.7 LTS and MariaDB 11.8 LTS.

MySQL vs. MariaDB: a Complete Comparison in 2026

Neon and Supabase are the 2 new generation Postgres service providers. This is an extensive comparison between Neon and Supabase on architecture, compatibility, agentic workload, developer workflow, scalability, integration, compliance, pricing and more.

Neon vs. Supabase: Which One Should I Choose

Flyway vs Liquibase: features, pricing, databases, rollback, drift detection, and a when-to-choose guide. Updated for Flyway 12.9.0 and Liquibase 5.0.3.

Flyway vs. Liquibase: The Definitive Comparison in 2026

Bytebase vs. CloudBeaver compared across product scope, supported databases, change review, access control, data masking, and pricing. Updated for Bytebase 3.16 and CloudBeaver 25.

Bytebase vs. CloudBeaver: a side-by-side comparison for web-based database management

Bytebase vs Metabase in 2026: a side-by-side comparison of database tooling for developer teams and data teams, covering pricing, features, SQL Review, change workflow, and BI.

Bytebase vs. Metabase: a side-by-side comparison for developer and data teams

Bytebase vs. DBeaver compared across product scope, supported databases, change workflow, access control, data masking, and pricing. Updated for Bytebase 3.16 and DBeaver 25.

Bytebase vs. DBeaver: a side-by-side comparison for database management

An engineering perspective to evaluate Amazon Aurora and RDS, and help you to choose the right AWS database for 2026

Aurora vs. RDS: engineering guide to choose the right AWS database for 2026

An extensive comparison between Postgres and MongoDB on performance, reliability, scalability, usability, operability, ecosystem and more.

Postgres vs. MongoDB: a Complete Comparison in 2025

PostgreSQL vs MySQL: in-depth comparison of performance, features, security, extensibility, JSON support, replication, and ecosystem. Updated for Postgres 18 and MySQL 9.x with real-world scaling references.

PostgreSQL vs MySQL: Which Database Should You Choose in 2026?

An extensive comparison between PlanetScale and Neon on architecture, compatibility, developer workflow, scalability, operability, integration, compliance, pricing and more.

PlanetScale vs. Neon: the Continued Saga between MySQL and PostgreSQL

Bytebase vs. Flyway compared across migration workflow, SQL review, access control, pricing, and CI/CD. Updated for Flyway Desktop and Bytebase 3.16.

Bytebase vs. Flyway: a side-by-side comparison for database schema migration

Bytebase vs. Liquibase compared across migration workflow, SQL review, access control, pricing, and CI/CD. Updated for Liquibase 5.0 Secure and Bytebase 3.16.

Bytebase vs. Liquibase: a side-by-side comparison for database schema migration

When evaluating database management tools, Navicat and Bytebase represent two different philosophies: individual desktop productivity versus team collaboration and governance.

Bytebase vs. Navicat: a side-by-side comparison for database management


<HintBlock type="info">

This post is maintained by Bytebase, an open-source database governance platform that can manage both Neon and Supabase (it's PG after all). We update the post every year.

</HintBlock>

**Neon is a serverless Postgres database; Supabase is a backend-as-a-service platform built on Postgres.** Choose Neon if you want a standalone, scale-to-zero Postgres with instant branching. Choose Supabase if you want a full-stack platform with auth, storage, realtime, and edge functions included alongside your database.

Besides the typical Postgres service providers like AWS RDS, Google Cloud SQL, DigitalOcean Managed Databases, [Neon](https://neon.com/) and [Supabase](https://supabase.com/) are the two modern Postgres service providers.

<HintBlock type="info">

Databricks [completed its acquisition](https://www.databricks.com/blog/databricks-neon) of Neon in 2025 for ~$1 billion, making Neon the Postgres foundation for Databricks' agentic AI platform. Neon continues to operate as an independent product with its own pricing and brand (now at neon.com).

</HintBlock>

On the surface, Neon and Supabase are different products. Neon is a database service. Supabase is a BaaS (Backend-as-a-Service) and Postgres is one of its included services.

They are comparable because they both offer a **developer-friendly**, **scalable** Postgres service. In the agentic era, each aims to become the de-facto database for agentic workloads.

![reddit](/content/blog/neon-vs-supabase/reddit.webp)

And their websites certainly don't help the choice easier.

![neon-website](/content/blog/neon-vs-supabase/neon-site.webp)

![supabase-website](/content/blog/neon-vs-supabase/supabase-site.webp)

At Bytebase, we are Postgres fans. Our founders build [Google Cloud SQL for PostgreSQL](https://cloud.google.com/sql) and Bytebase also chooses Postgres to store its own metadata. Below we compare Neon and Supabase from the following dimensions:

- [Architecture](#architecture)
- [Compatibility](#compatibility)
- [Branching](#branching)
- [Agentic Workload](#agentic-workload)
- [Integration](#integration)
- [Compliance](#compliance)
- [Open Source](#open-source)
- [Pricing](#pricing)
- [FAQ](#faq)

## Architecture

Neon is a shared-storage architecture. It separates the compute and storage. The compute part is just normal Postgres server, the storage part is a custom-built multi-tenant storage system shared by all Postgres compute nodes.

![neon-arch](/content/blog/neon-vs-supabase/neon-arch.webp)

Supabase is a battery-included Postgres platform. It uses vanilla Postgres as the core and augments the database with various middlewares.

![supabase-arch](/content/blog/neon-vs-supabase/supabase-arch.webp)

## Compatibility

Neon is mostly compatible with vanilla Postgres whereas Supabase is a dedicated vanilla Postgres instance. They both bear the limitations of a hosted database service (e.g. no superuser).

- [Neon compatibility](https://neon.com/docs/reference/compatibility)
- [Supabase compatibility](https://supabase.com/docs/guides/database/postgres/roles-superuser)

## Branching

Both Neon and Supabase are targeting developers, and they both offer a branching feature.

Neon purpose-built paging layer has copy-on-write (CoW), which enables database cloning instantaneous and cost-effective.

![neon-branch](/content/blog/neon-vs-supabase/neon-branch.webp)

Supabase branching integrates with Git repository. It provisions a new empty database, runs the migration script and initializes the seed data.

Overall, Neon's instant branch cloning is closer to the Git semantics.

## Agentic Workload

Both Neon and Supabase highlight AI agents as a primary use case.

![neon-agent](/content/blog/neon-vs-supabase/neon-ai-agent.webp) ![supabase-agent](/content/blog/neon-vs-supabase/supabase-ai-agent.webp)

The Databricks acquisition confirmed that Neon’s architecture — instant provisioning, scale-to-zero, and per-agent database branching — is purpose-built for agentic workloads.

![neon-4x-agent-db](/content/blog/neon-vs-supabase/neon-4x-agent-db.webp)

Neon now offers a unified `neon init` command that configures both its MCP Server and VS Code Extension in one step, giving AI coding assistants like Cursor direct database access. Neon Auth also branches automatically alongside database branches, so preview environments get isolated auth state.

Supabase is responding with its own agentic play. BKND joined Supabase to build a Lite offering specifically for agentic workloads. AI app builders such as [Lovable](https://lovable.dev/) and [bolt](https://bolt.new/) continue to adopt Supabase as their default backend.

Overall, Neon’s scale-to-zero and instant branching make it better suited for agents that spin up many short-lived databases. Supabase’s full-stack platform is a better fit for AI app builders who need auth, storage, and realtime alongside the database.

## Integration

Neon integrates with application platforms such as Vercel to provide a high-fidelity preview environment. The Vercel integration now automatically provisions Neon Auth on preview branches, so authentication works out of the box in preview deployments. Neon is also now part of the Databricks ecosystem, opening up integrations with Databricks' data and AI tools.

Supabase is an application platform by itself and boasts a wide variety of integrations.

![supabase-integration](/content/blog/neon-vs-supabase/supabase-integration.webp)

Recent additions include a one-click Stripe Sync Engine integration (query customers, subscriptions, and invoices with SQL) and the Hydra-powered Supabase Warehouse for analytics workloads. There are also quite a few SaaS boilerplates built on Supabase.

## Compliance

Both Neon and Supabase achieve SOC 2 Type 2. Both now offer HIPAA compliance — Neon added HIPAA eligibility on its Scale plan after the Databricks acquisition.

- [Neon security](https://neon.com/docs/security/security-overview)
- [Supabase security](https://supabase.com/security)

## Open Source

Neon [open sources](https://github.com/neondatabase/neon) its entire database codebase under Apache-2.0 license.

Supabase also [open sources](https://github.com/supabase/supabase) its entire platform under Apache-2.0 license.

Supabase is one of the most popular repositories on GitHub, while Neon's star growth is also impressive.

[![neon-vs-supabase](/content/blog/neon-vs-supabase/supabase-vs-neon.webp)](https://star-history.com/#supabase/supabase&neondatabase/neon&Date)

## Pricing

Both Neon and Supabase offer a free tier and usage-based pricing, but their models differ significantly.

### Neon — Usage-Based (Pay for What You Use)

Post-Databricks acquisition, Neon slashed prices: storage dropped from $1.75 to **$0.35 per GB-month** and compute costs fell ~25%.

| Plan     | Monthly Cost                | Included Compute     | Compute Rate   | Storage        | Key Limits                       |
| -------- | --------------------------- | -------------------- | -------------- | -------------- | -------------------------------- |
| Free     | $0                          | 100 CU-hours/project | N/A            | 0.5 GB/project | Up to 2 CU, scale-to-zero        |
| Launch   | Usage-based (~$15 typical)  | -                    | $0.106/CU-hour | $0.35/GB-month | Up to 16 CU, 100 projects        |
| Scale    | Usage-based (~$701 typical) | -                    | $0.222/CU-hour | $0.35/GB-month | Up to 56 CU, SLA, SOC 2, HIPAA   |
| Business | Custom                      | Custom               | Custom         | Custom         | Dedicated infra, premium support |

Neon's scale-to-zero means you pay nothing when the database is idle — a significant advantage for dev/test environments and agentic workloads that spin up many short-lived databases.

### Supabase — Platform Fee + Usage

Supabase charges a platform subscription fee plus usage-based overages.

| Plan       | Monthly Cost | Database      | Auth MAU | Storage | Key Features                         |
| ---------- | ------------ | ------------- | -------- | ------- | ------------------------------------ |
| Free       | $0           | 500 MB        | 50K      | 1 GB    | 2 projects, paused after 7 days idle |
| Pro        | $25 + usage  | 8 GB included | 100K     | 100 GB  | $10 compute credit, spend cap        |
| Team       | $599 + usage | 8 GB included | 100K     | 100 GB  | SSO, SOC 2, 28-day logs              |
| Enterprise | Custom       | Custom        | Custom   | Custom  | SLA, 24/7 support, BYO cloud         |

Supabase's pricing includes auth, storage, edge functions, and realtime — all bundled into the platform fee. If you need these services, the value proposition is strong compared to assembling them separately.

## Neon or Supabase

If you want a **standalone Postgres database** with serverless scaling, instant branching, and scale-to-zero, Neon is the stronger choice. It is especially well-suited for agentic workloads and development workflows that benefit from lightweight, ephemeral databases. The Databricks acquisition gives it additional backing for long-term investment.

If you are building a **full-stack application** and want auth, file storage, realtime subscriptions, and edge functions included alongside your database, Supabase provides a more complete platform. Its dashboard, growing integration ecosystem, and active community make it a productive choice for shipping quickly.

## FAQ

### Is Neon still independent after the Databricks acquisition?

Yes. Neon continues to operate as an independent product with its own pricing, brand, and roadmap. It is now available at [neon.com](https://neon.com/) (previously neon.tech).

### Can I self-host Neon or Supabase?

Both are open source under Apache 2.0. [Neon](https://github.com/neondatabase/neon) and [Supabase](https://github.com/supabase/supabase) can be self-hosted, though the managed services include features (HA, backups, monitoring) that require additional setup when self-hosting.

### Which is cheaper for a small project?

Both offer generous free tiers. Neon's free plan includes 100 CU-hours and 0.5 GB storage per project. Supabase's free plan includes 500 MB database, 50K MAU, and 1 GB file storage. For hobby projects, both are effectively free. As you scale, Neon's usage-based model can be cheaper if your workload is bursty (scale-to-zero saves cost during idle periods), while Supabase's bundled platform can be cheaper if you would otherwise pay separately for auth, storage, and realtime.

### Which has better performance?

Both run vanilla Postgres, so raw query performance is comparable. Neon's shared-storage architecture adds a small latency overhead on cold starts but enables instant branching and scale-to-zero. Supabase runs dedicated Postgres instances with no cold-start penalty. For latency-sensitive production workloads, Supabase's always-on compute may have a slight edge; for workloads with variable traffic, Neon's autoscaling can be more efficient.

## Other Comparisons

- [PlanetScale vs. Neon](/blog/planetscale-vs-neon/)
- [Postgres vs. MySQL](/blog/postgres-vs-mysql/)
- [Postgres vs. MongoDB](/blog/postgres-vs-mongodb/)
- [MySQL vs. MariaDB](/blog/mysql-vs-mariadb/)



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database governance platform that is a Liquibase/Flyway alternative. We update the post every year.

</HintBlock>

When looking for a database CI/CD and schema migration tool, Flyway and Liquibase are the two names that come up first. Both have a long open-source history, both are Java-based, and both solve the same core problem: getting schema changes into production in a repeatable, versioned way. The differences are in how they get there.

This guide breaks down every aspect — interface, database coverage, orchestration, rollback, pricing, and the recent 2026 releases — so you can decide which database migration tool fits your team. If you're in a hurry, start with the decision guide below.

## Flyway or Liquibase: which should you choose?

Most comparisons bury the recommendation at the bottom. Here it is up front. Read the table, then dig into the sections that matter for your case.

| Choose **Flyway** when… | Choose **Liquibase** when… |
| --- | --- |
| You want the simplest possible model: numbered SQL files, `flyway migrate`, done. | You need database-agnostic changelogs and want to target 50+ databases from one definition. |
| Your team is comfortable with SQL and doesn't want a changelog abstraction. | You need fine-grained control over ordering with preconditions, contexts, labels, and flows. |
| You're a single team or small set of apps and can live with CLI-first workflows. | You're standardizing migrations across many heterogeneous databases and teams. |

For most teams the choice comes down to SQL-first simplicity (Flyway) versus changelog flexibility and broader database coverage (Liquibase). They're also popular alternatives to each other, so the rest of this guide is a fair, feature-by-feature breakdown.

## What Flyway and Liquibase have in common

- Java-based, providing Java SDK and CLI.
- Provide open-source community version and closed-source commercial version.
- Native SQL supported, however Liquibase requires XML changelog to define the changes.
- Database CI/CD with version control system #GitOps (see [Database GitOps configuration](#database-gitops-configuration)).
- Auto SQL check (see [Auto SQL check](#sql-auto-check)).
- Schema Synchronization (see [Sync schema](#sync-schema)).
- Tiered pricing, offering both free and paid plan.
- Open source, both has long history. ![star-history](/content/blog/flyway-vs-liquibase/star-history-flyway-vs-liquibase.webp)

## What are the differences between Flyway and Liquibase?

While both Flyway and Liquibase are tools for database CI/CD, there are some key differences between the two. The following table summarizes the differences between Flyway and Liquibase.

|                                                        | Flyway                          | Liquibase                              |
| ------------------------------------------------------ | ------------------------------- | -------------------------------------- |
| [Product position](#product-position)                  | Schema Change & Version Control | Schema Change & Version Control        |
| [Developer interface](#developer-interface)            | CLI + Flyway Desktop            | CLI                                    |
| [Supported databases](#supported-databases)            | 20+ SQL + Native Connectors     | 50+ SQL & NoSQL DB                     |
| [Programming language and installation](#installation) | Java + JVM                      | Java + JVM                             |
| [Change execution](#change-execution)                  | SQL script + CLI                | Changelog (SQL, XML, JSON, YAML) + CLI |
| [Change orchestration](#change-orchestration)          | Numbering of SQL files          | Changelog + Flow                       |
| [Database GitOps](#database-gitops-configuration)      | ✅                              | ✅                                     |
| [SQL auto check](#sql-auto-check)                      | ✅                              | ✅                                     |
| [Change history](#change-history)                      | ✅                              | ✅                                     |
| [Sync schema](#sync-schema)                            | ✅                              | ✅                                     |
| [Rollback](#rollback)                                  | ✅                              | ✅                                     |
| [Schema drift detection](#schema-drift-detection)      | ✅                              | ✅                                     |

### Product position

- **Flyway**: A database schema change and version control tool.

- **Liquibase**: A database schema change and version control tool. It helps you track, manage, and automate changes to your database. ![liquibase-position](/content/blog/flyway-vs-liquibase/liquibase-position.webp)

### Developer interface

- **Flyway**: A command-line tool with Java API, Maven plugin, and Gradle plugin. Flyway Desktop has been enhanced with full Schema Model view and Git history tracking, available for SQL Server, PostgreSQL, MySQL, and other supported databases.

- **Liquibase**: Primarily a command-line tool. The Liquibase Hub GUI was sunset in May 2023, with focus shifting to CLI and integration capabilities.

### Supported databases

- **Flyway**: 20+ SQL databases, plus a growing set of Native Connectors for non-JDBC engines:
  - MongoDB (Native Connectors mode only; JDBC support removed in 2025)
  - Cassandra (Native Connectors mode as of Flyway 12.5.0; the JDBC approach is deprecated)
  - SinglestoreDB
  - Google Cloud Spanner
  - EnterpriseDB
  - TimescaleDB
  - Postgres 18, SQL Server 2025, Oracle 26ai (added 2025)
  - Full compatibility for all databases in Community version

- **Liquibase**: 50+ SQL and NoSQL databases with enhanced support for:
  - Databricks (added 2025)
  - Google BigQuery (including DATABASECHANGELOGHISTORY table support)
  - Continued MongoDB support
  - Snowflake with OAuth/PKI authentication

### Installation

- **Flyway**: Java-based tool requiring JVM installation. In 2025, configuration has moved to unified flyway.toml format, deprecating the older JSON format.

- **Liquibase**: Java-based tool requiring JVM installation. **Liquibase 5.0+ requires Java 17 minimum** (Java 8/11 deprecated). MacOS .dmg installer was deprecated in favor of Homebrew installation.

### Change execution

- **Flyway**: CLI or GitOps. Users write SQL files and then run command `flyway migrate`. In 2025, enhanced capabilities for script migrations and callbacks have been added to the Community version.

- **Liquibase**: CLI or GitOps. Users specify the changes by defining a `changelog` and then run a command. In 2025, flow enhancements provide more sophisticated orchestration capabilities.

![liquibase-changelog](/content/blog/flyway-vs-liquibase/liquibase-changelog.webp) ![liquibase-update](/content/blog/flyway-vs-liquibase/liquibase-update.webp)

### Change orchestration

- **Flyway**: Number the SQL files in the order you want them to be executed.

  ![liquibase-change-order](/content/blog/flyway-vs-liquibase/flyway-change-order.webp)

- **Liquibase**: Specify the order of changes in the changelog file. In 2025, Flow capabilities have been enhanced with conditionals and more advanced orchestration features.

  ![liquibase-change-order](/content/blog/flyway-vs-liquibase/liquibase-change-order.webp)

Liquibase continues to be more flexible as it can specify arbitrary orders, and the enhanced [flow file](https://docs.liquibase.com/liquibase-pro/flow/home.html) capabilities in 2025 make orchestrating complex steps even more powerful.

### Database GitOps configuration

- **Flyway**: Configure with VCS CI/CD workflow manually. In 2025, integration with Test Data Manager and Docker for dedicated development databases improves the GitOps workflow.

- **Liquibase**: Configure with VCS CI/CD workflow manually. In 2025, enhanced validation and error messaging improve the GitOps experience.

  ![liquibase-gitlab-gitops](/content/blog/flyway-vs-liquibase/liquibase-gitlab-gitops.webp)

### SQL auto check

SQL auto check helps developers write less buggy SQL and save DBAs manual review efforts.

- **Flyway**: Code Analysis with enhanced drift detection in 2025
- **Liquibase**: SQL Quality check with improved Policy Checks in 2025

#### Supported plan

- **Flyway**: Enterprise only (Teams discontinued in 2025)
- **Liquibase**: Paid tiers only (Starter and above)

#### Number of rules

- **Flyway**: 10+ general rules or integrate SQLFluff for more rules
- **Liquibase**: Enhanced policy checks with more comprehensive validation in 2025

#### How to configure

- **Flyway**: Predefined, users may set them active or not. In 2025, improved configuration through unified flyway.toml.

- **Liquibase**: Predefined, users may set levels while calling. Python-based Custom Policy Checks available in paid tiers.

  ![liquibase-quality-check](/content/blog/flyway-vs-liquibase/liquibase-quality-check.webp)

#### How to trigger

- **Flyway**: Run `flyway check -code ....` command to produce a report.

- **Liquibase**: Run `check` command or manually integrate this command in automation. Users may go to admin console to view logs. ![liquibase-validate-result](/content/blog/flyway-vs-liquibase/liquibase-validate-result.webp)

### Change history

- **Flyway**: Run `flyway info` to show `flyway_schema_history` table. In 2025, enhanced info filters provide more granular control.

- **Liquibase**: Simple Database Change Logs. In 2025, new parameters for the `history` command and improved Operation Reports.

  ![liquibase-logs](/content/blog/flyway-vs-liquibase/liquibase-logs.webp)

### Sync schema

- **Flyway**: Via Flyway Desktop, generate a migration script to bring the target database schema in sync. In 2025, the new backup-based baseline approach allows restoring a backup file instead of creating a database from scratch in the baseline script.

- **Liquibase**: Support `diff-changelog` to compare databases and create a deployable changelog to sync. In 2025, enhanced Drift Report capabilities improve schema synchronization.

### Rollback

- **Flyway**: Write rollback scripts manually. Enterprise only (Community does not support rollback).

- **Liquibase**: Support `rollback-one-changeset` or `rollback`. Enhanced Rollback Report in paid tiers.

### Schema drift detection

- **Flyway**: Run `flyway check -drift ...` to produce a report indicating difference between target database and the one created by the migrations applied by Flyway. Enhanced in 2025 with more comprehensive code analysis.

- **Liquibase**: Run `liquibase diff --format=json` to produce a report indicating difference between target database and source database. In 2025, improved Drift Report capabilities with better visualization.

## Flyway enhancements in 2025-2026

Key features released in [2025](https://www.red-gate.com/blog/redgate-flyway-2025-year-in-review):

- **State-based deployments** - Work the way you want with declarative schema definitions
- **Backups as baseline** - Use database backups instead of lengthy baseline scripts, solving issues with invalid objects or external server references
- **Improved drift detection** - Snapshots and drift resolution scripts to identify and resolve schema drift
- **Database support** - Added support for Postgres 18, SQL Server 2025, Oracle 26ai

[January 2026 updates](https://www.red-gate.com/blog/redgate-flyway-product-updates-january-2026):

- **AI-powered migration descriptions** - Flyway Desktop 8 generates descriptions based on migration script contents
- **New shadow database dialog** - Option to let Flyway create shadow databases automatically
- **Schema model view** - View all object definitions in Flyway Desktop
- **SQLFluff 4.0.0** - Upgraded from 3.5.0

The spring 2026 [release train](https://documentation.red-gate.com/fd/release-notes-for-flyway-engine-179732572.html) (Flyway 12.5.0 through 12.10.0, with 12.9.0 landing June 18 and 12.10.0 on June 30, 2026) added a run of engine-level changes worth calling out:

- **Cassandra Native Connectors** (12.5.0) - Cassandra joins MongoDB as a non-JDBC Native Connectors target; the older JDBC approach is deprecated
- **Azure Key Vault secrets resolver** (12.5.0) - Pull credentials directly from Azure Key Vault
- **Native Connectors for the Deploy command** (12.6.0) - Deploy now works against Native Connectors engines, not just Migrate
- **Custom SQLFluff rules for Check** (12.7.0) - Load your own SQLFluff rules into the Check command
- **Distinct CLI exit codes** (12.9.0) - The CLI now returns specific exit codes derived from Flyway's error codes, so CI pipelines can branch on the exact failure
- **`check.driftResolutionFolder`** (12.9.0) - Configure where drift-resolution scripts are written, plus phase-level progress reporting for check, drift, changes, and dryrun
- **`docker-compose` provisioner** (12.10.0) - The preview `docker` environment provisioner was renamed to `docker-compose`, spinning up throwaway databases from a Docker Compose file for shadow/build steps (existing `docker` configs that use a Compose file keep working)
- **Smart docker provisioner** (12.10.0) - A `keepAlive` option reuses the build-database container across Flyway commands instead of recreating it each run

That 12.9.0 drift-resolution and exit-code work is squarely aimed at teams wiring Flyway into CI/CD pipelines — the same audience deciding between Flyway, Liquibase, and a governance layer.

**Important licensing change**: Since May 2025, Flyway only offers Community (free) and Enterprise (paid). The Teams tier was discontinued for new customers, which moved rollback, code analysis, and drift detection behind the Enterprise license.

## Liquibase enhancements in 2025-2026

[Liquibase 5.0](https://docs.liquibase.com/community/release-notes/5-0) was released in September 2025 as a major release:

- **Java 17 minimum requirement** - Java 8 and 11 deprecated for improved performance and security
- **Liquibase Secure 5.0** - Certified enterprise distribution with integrated extensions, drivers, and policies
- **Liquibase Secure Developer** - New VS Code extension for enterprise users
- **AI Changelog Generator** - MCP Server powered tool to convert natural language to production-ready changelogs (Private Preview)
- **Databricks support** - Added database connection support

Flow and automation enhancements:

- **Conditional Flow File logic** - Based on action exit codes with new `continueOnError` property
- **Snowflake OAuth/PKI authentication** - Simplified authentication for Snowflake's token-based requirements
- **New policy check** - `FormattedSqlHeaderRequired` ensures proper SQL file formatting
- **Python checks enhancements** - New `get_config_value()` helper function

Liquibase Community follows a quarterly cadence (February, May, August, November), and the 5.0 line has picked up two maintenance releases since:

- **[Liquibase 5.0.2](https://docs.liquibase.com/community/release-notes/liquibase-community-5-0-2-release-notes)** (March 2026) - Cross-database bug fixes and refreshed JDBC driver versions
- **[Liquibase 5.0.3](https://docs.liquibase.com/community/release-notes/liquibase-community-5-0-3-release-notes)** (May 2026) - Input sanitization for `generate-changelog` that closes two reported vulnerabilities, plus more accurate PostgreSQL sequence discovery

**Important licensing change**: Liquibase Community has adopted the [Functional Source License (FSL)](https://docs.liquibase.com/community/release-notes/5-0) to ensure long-term sustainability while keeping the software free for developers.

## Pricing

Flyway uses a per-user licensing model, while Liquibase uses a per-application/database-type licensing model.

| Aspect              | Flyway                         | Liquibase                                        |
| ------------------- | ------------------------------ | ------------------------------------------------ |
| **Free Tier**       | Community                      | Community (FSL license)                          |
| **Paid Tiers**      | Enterprise only                | 4 tiers: Starter, Growth, Business, Enterprise   |
| **Licensing Model** | Per-user                       | Per-application + database types                 |
| **Entry-Level**     | Enterprise (contact for quote) | Starter: up to 5 apps, 1 DB type                 |
| **Mid-Tier**        | —                              | Growth (10 apps, 3 DB types), Business (25 apps) |
| **Enterprise**      | Unlimited (custom pricing)     | Unlimited apps & DB types, 24/7 support          |
| **Pricing**         | Contact sales                  | Contact sales (all tiers)                        |

## Summary

Both Flyway and Liquibase are Java-based, ship SDKs and a CLI, and follow a [migration-based](/blog/database-version-control-state-based-vs-migration-based/#migration-based-version-control-imperative) approach to schema changes. They also lean on the same open-source monetization strategy. Flyway is generally considered more developer-friendly; Liquibase offers a broader set of advanced features and 50+ databases.

The key difference is that Liquibase introduces the Changelog and Flow concepts, enabling explicit migration ordering, preconditions, labels, and contexts. Flyway determines migration order from file naming conventions alone. Both saw meaningful 2026 releases — Flyway 12.9.0 sharpened CI/CD ergonomics with distinct exit codes and drift-resolution controls; Liquibase 5.0 reset its baseline with a Java 17 requirement, the FSL license, and an AI changelog generator.

Pick Flyway for simplicity or Liquibase for flexibility. Both give you a Git-like experience for schema changes; if you also want a web GUI and team collaboration on top of either engine, [Bytebase](/) is our own take — see the side-by-side comparisons:

- [Bytebase vs. Liquibase](/blog/bytebase-vs-liquibase/)
- [Bytebase vs. Flyway](/blog/bytebase-vs-flyway/)

For a wider field, see our roundup of [open-source Postgres migration tools](/blog/top-open-source-postgres-migration-tools/), which covers Atlas, pgroll, Reshape, and others alongside Flyway and Liquibase.

## FAQ

### Which is better, Flyway or Liquibase?

It depends on your team. Flyway is simpler and SQL-first, so it fits teams that want plain versioned SQL scripts. Liquibase is more flexible thanks to its changelog and flow concepts, supports 50+ databases versus Flyway's 20+, and gives you finer control over migration ordering. Teams that need a GUI, approval workflows, and audit logging on top of either engine tend to reach for a governance layer like [Bytebase](/).

### Is Flyway free?

Flyway Community Edition is free and open source. Since May 2025 the only paid option is Flyway Enterprise; the Teams tier was discontinued for new customers, so features like rollback and code analysis now sit behind Enterprise.

### Is Liquibase free?

Liquibase Community Edition is free under the Functional Source License (FSL) adopted in Liquibase 5.0. Paid tiers (Starter, Growth, Business, Enterprise) unlock policy checks, advanced orchestration, and support.

### Does Flyway support rollback?

Flyway supports rollback only in the Enterprise edition, and you write the undo scripts manually. Liquibase supports rollback in its paid tiers, including automatic rollback for changes it can reverse.

### What are the alternatives to Liquibase and Flyway?

Common alternatives include [Bytebase](/), Atlas, SchemaHero, and Skeema. Bytebase adds a web GUI, built-in SQL review, approval workflows, GitOps, and audit logging that neither Flyway nor Liquibase provide out of the box, while Atlas and SchemaHero lean toward declarative, Kubernetes-native schema management.



> CloudBeaver is a web-based SQL client. Bytebase is a database governance platform.

CloudBeaver and Bytebase both run server-side and render in a browser, which is where the similarity ends. CloudBeaver is the web-native descendant of DBeaver — a lightweight SQL client that a team can share through a browser instead of installing desktop software. Bytebase is a database governance platform built around change review, access control, data masking, and audit logging. If you want a shared web GUI for everyday querying and editing, CloudBeaver fits. If you need review workflows, granular permissions, masking, and audit trails, that's Bytebase.

## What Bytebase and CloudBeaver have in common

- Server-side, web-based GUI — query, browse, export, edit.
- Tiered pricing with free and paid plans.
- AWS Marketplace listings for easy procurement.
- Support for a range of SQL and NoSQL databases.
- Open source — CloudBeaver started slightly earlier; Bytebase's star growth has outpaced it since 2023.
  ![star-history](/content/blog/bytebase-vs-cloudbeaver/star-history.webp)

## Key Differences Between Bytebase and CloudBeaver

Bytebase is built around team workflow — every database change flows through review, approval, and audited rollout. CloudBeaver is built around shared data access — one browser-based client that a team can log into, with lightweight team permissions and a query history log.

|                                             | CloudBeaver                                                  | Bytebase                                              |
| ------------------------------------------- | ------------------------------------------------------------ | ----------------------------------------------------- |
| [Product position](#product-position)       | Web-based SQL client (DBeaver's cloud sibling)               | Database governance platform                           |
| [Developer interface](#developer-interface) | Server-side, web-based                                       | Server-side, web-based                                |
| [Installation](#installation)               | Java-based, requires JVM (or Docker)                         | Go binary, no external dependency                     |
| [Supported databases](#supported-databases) | CE: ~20; Enterprise: 80+                                     | 23 with deep integration                              |
| [Query](#query)                             | ✅                                                           | ✅                                                    |
| [Change](#change)                           | Direct                                                       | Review workflow + configurable direct / Admin mode    |
| [SQL review rules](#change)                 | ❌                                                           | ✅ 200+ rules (free)                                  |
| [Access control](#access-control)           | Basic team/user groups + per-connection restrictions          | Workspace/project roles (free); database/table-level (Enterprise) |
| [Data masking](#data-masking)               | ❌                                                           | ✅ Enterprise (dynamic, column-level)                 |
| [Compare schema](#compare-schema)           | ❌                                                           | ✅ 1:N (free)                                         |
| [Audit log](#audit-log)                     | ✅ Query Manager (paid)                                      | ✅ Pro                                                |
| [API](#api)                                 | GraphQL                                                       | gRPC + HTTP/REST + Terraform provider                |
| [GitOps](#gitops)                           | Not built in                                                  | ✅ GitHub, GitLab, Bitbucket, Azure DevOps           |

### Product position

- **CloudBeaver**: A lightweight web SQL client — DBeaver's browser-based cousin. It ships a shared web GUI for querying, editing, and browsing databases. Collaborative features like shared connections and team permissions have grown over time, but the product's center of gravity is still "one SQL client that a team can share in a browser."
  ![cb-position](/content/blog/bytebase-vs-cloudbeaver/cb-position.webp)

- **Bytebase**: A database governance platform built around team workflow — every change flows through review, approval, and audited rollout. SQL Editor is one surface; the rest is governance (access control, data masking, audit log, approval flows, GitOps).
  ![bb-3-op](/content/blog/bytebase-vs-cloudbeaver/bb-3-op.webp)

### Developer interface

- **CloudBeaver**: Web GUI focused on query, edit, and schema browsing.
  ![cb-gui](/content/blog/bytebase-vs-cloudbeaver/cb-ui.webp)

- **Bytebase**: Web GUI with an issue-based change workflow plus a SQL Editor for querying. Also exposes an [API](https://api.bytebase.com/), a [Terraform provider](https://docs.bytebase.com/integrations/terraform/overview), and [GitOps](https://docs.bytebase.com/gitops/overview) for teams that prefer automation.
  ![bb-issue](/content/blog/bytebase-vs-cloudbeaver/bb-issue-waiting.webp)
  ![bb-gui](/content/blog/bytebase-vs-cloudbeaver/bb-gui.webp)

### Installation

- **CloudBeaver**: Java-based, runs on a JVM or via Docker.

- **Bytebase**: Single Go binary, no external dependencies. Also ships as a [Docker image](https://docs.bytebase.com/get-started/deploy-with-docker) and [Helm chart for Kubernetes](https://docs.bytebase.com/get-started/deploy-with-kubernetes).

### Supported databases

- **CloudBeaver**: The **Community Edition** covers ~20 common databases (MySQL, PostgreSQL, Oracle, SQLite, SQL Server, MariaDB, and similar). **CloudBeaver Enterprise** adds 80+ engines including NoSQL (MongoDB, Cassandra, Redis) and cloud warehouses (Redshift, BigQuery, Snowflake).

- **Bytebase**: 25 engines with deep integration — 9 RDBMS (MySQL, PostgreSQL, Oracle, SQL Server, MariaDB, TiDB, OceanBase, CockroachDB, Spanner), 6 NoSQL (MongoDB, Redis, Cassandra, DocumentDB, DynamoDB, Cosmos DB), 9 data warehouses (Snowflake, BigQuery, Redshift, Hive, ClickHouse, Databricks, StarRocks, Doris, Trino), and Elasticsearch. Each engine gets engine-specific SQL review rules, schema sync, and masking support.

### Query

- **CloudBeaver**: Query by double-clicking a table or writing SQL with templates and autocomplete. **Enterprise** adds a visual query builder for point-and-click query construction.
  ![cb-query-sql](/content/blog/bytebase-vs-cloudbeaver/cb-query-sql.webp)

  Saved scripts can be shared alongside the connection they belong to.
  ![cb-script](/content/blog/bytebase-vs-cloudbeaver/cb-script.webp)

- **Bytebase**: The **SQL Editor** runs queries through the platform — double-click a table or write SQL with autocomplete.
  ![bb-data-query](/content/blog/bytebase-vs-cloudbeaver/bb-data-query.webp)

  Save or star scripts for quick access. On **Pro** and above, share scripts with your team or project.
  ![bb-sheets](/content/blog/bytebase-vs-cloudbeaver/bb-sheets.webp)

### Change

- **CloudBeaver**: Edit data directly in the result grid or run SQL manually. Changes hit the database immediately — no review, no approval flow. **Query Manager** logs executed statements for later inspection (paid tier).
  ![cb-change](/content/blog/bytebase-vs-cloudbeaver/cb-change.webp)

  ![cb-change-query-history](/content/blog/bytebase-vs-cloudbeaver/cb-change-query-history.webp)

- **Bytebase**: Changes flow through an **issue** — like a pull request for the database. Each issue runs automated [SQL review](https://docs.bytebase.com/sql-review/review-rules) (200+ rules), routes through a [custom approval flow](https://docs.bytebase.com/change-database/approval) if configured, and deploys with full history tracking. Admins can choose to allow direct change per environment or enforce review platform-wide.
  ![bb-issue](/content/blog/bytebase-vs-cloudbeaver/bb-issue-waiting.webp)

Every change is recorded in **Change History** with diff view and rollback scripts.
![bb-history](/content/blog/bytebase-vs-cloudbeaver/bb-history.webp)

Ad-hoc DDL (e.g. `ALTER TABLE` typed into SQL Editor) either creates a new issue or requires **Admin mode** — a deliberate escape hatch for break-glass scenarios, similar to SSH on a server.
![bb-force-issue](/content/blog/bytebase-vs-cloudbeaver/bb-force-issue.webp)
![bb-force-issue-preview](/content/blog/bytebase-vs-cloudbeaver/bb-force-issue-preview.webp)

For schema changes, most teams use the visual **Schema Editor** — edit columns, constraints, and indexes in the GUI and Bytebase generates the DDL automatically.

![bb-select-db](/content/blog/bytebase-vs-cloudbeaver/bb-select-db.webp)
![bb-schema-editor](/content/blog/bytebase-vs-cloudbeaver/bb-schema-editor.webp)

### Access Control

- **CloudBeaver**: Default teams are `admin` (admin panel access) and `user` (no admin access). You can create additional teams and restrict connection access per team or per user.
  ![cb-teams](/content/blog/bytebase-vs-cloudbeaver/cb-teams.webp)

  ![cb-db-access](/content/blog/bytebase-vs-cloudbeaver/cb-db-access.webp)

  The model is connection-scoped — if you can see a connection, you can see everything the connecting user can. No concept of permissions below the database level.

- **Bytebase**: Roles are scoped to **Workspace** and **Project** levels. `Workspace Admin` / `DBA` provision instances and manage members; `Project Owner` manages databases, issues, and roles within their project. Individuals can hold multiple project-level roles like `Project Developer` (change database) or `SQL Editor User` (query only).
  ![bb-access-control](/content/blog/bytebase-vs-cloudbeaver/bb-access-control.webp)

  On **Community**, users only see projects they're assigned to. On **Enterprise**, `Project Owner` can grant access down to specific databases or tables, and developers can request [Just-In-Time access](https://docs.bytebase.com/security/database-permission/overview) via a request issue that expires automatically.
  ![bb-access-control-adv](/content/blog/bytebase-vs-cloudbeaver/bb-access-control-adv.webp)

  Different environments can enforce different policies — e.g., read-only access to production, full access to development.
  ![bb-access-environment](/content/blog/bytebase-vs-cloudbeaver/bb-access-control-env.webp)

### Data Masking

- **CloudBeaver**: Not available. Query results return raw data.

- **Bytebase**: [Dynamic data masking](https://docs.bytebase.com/security/data-masking/overview) at the column level (Enterprise). `Admin` / `DBA` / `Project Owner` configure masking per column and authorize specific users to see the original data. Everyone else sees masked values in query results.

  Semantic types (email, phone, credit card, SSN) let you define reusable masking algorithms applied consistently across the schema.
  ![bb-masking-graph](/content/blog/bytebase-vs-cloudbeaver/bb-masking-graph.webp)

### Compare Schema

- **CloudBeaver**: Not available.

- **Bytebase**: **Schema Sync** is **1:N** (free) — pick one source database and compare against several targets at once. Results become a batch change issue that routes through SQL review, approval, and rollout.
  ![bb-schema-compare-several](/content/blog/bytebase-vs-cloudbeaver/bb-schema-compare-several.webp)

  ![bb-batch-issue](/content/blog/bytebase-vs-cloudbeaver/bb-batch-issue.webp)

### Audit log

- **CloudBeaver**: **Query Manager** (paid) logs executed SQL statements per connection.
  ![cb-audit-log](/content/blog/bytebase-vs-cloudbeaver/cb-audit-log.webp)

- **Bytebase**: [Audit Log](https://docs.bytebase.com/security/audit-log) (Pro and above) records every action in the platform — queries, DDL, approvals, policy changes. Filter by user, action type, or time range; export for SIEM ingestion.
  ![bb-audit-log](/content/blog/bytebase-vs-cloudbeaver/bb-audit-log-dropdown.webp)

### API

- **CloudBeaver**: [GraphQL API](https://github.com/dbeaver/cloudbeaver/wiki/Server-API-explorer). Documentation is sparse — mostly auto-generated schema reference.

- **Bytebase**: gRPC and HTTP/REST APIs with full [docs](https://docs.bytebase.com/api/overview/), [code samples](https://github.com/bytebase/example-api), and a [Terraform provider](https://docs.bytebase.com/integrations/terraform/overview).

### GitOps

- **CloudBeaver**: No built-in GitOps. You could roll your own via the GraphQL API, but there's no out-of-the-box workflow.

- **Bytebase**: [GitOps](https://docs.bytebase.com/gitops/overview) with GitHub, GitLab, Bitbucket, and Azure DevOps. SQL files committed to a repo create change issues in Bytebase automatically, with SQL review running as a merge check. See the [database-security example](https://github.com/bytebase/example-database-security) for a full GitOps setup.

### Pricing

- **CloudBeaver**: **Community Edition** is free and open source (AGPLv2). **CloudBeaver Team** and **Enterprise** are subscription-based with custom pricing — add NoSQL drivers, cloud warehouse support, Query Manager, SSO, and team management.

- **Bytebase**: [Community](https://www.bytebase.com/pricing/) is free, self-hosted, up to 20 users and 10 instances — includes SQL review, GitOps, multi-env rollouts. Pro is $20/user/month (cloud, up to 10 instances) and adds SSO, audit log, user groups. Enterprise is custom pricing, self-hosted or cloud, and unlocks dynamic data masking, custom approval flows, custom roles, SCIM, LDAP/OIDC SSO, and 2FA.

## When to Choose CloudBeaver

- You want a browser-based SQL client your team can share without installing desktop software.
- Your workflow is primarily query and edit — review, approval, and compliance aren't priorities.
- You need JDBC breadth for obscure or niche databases (Enterprise tier).
- You're already invested in the DBeaver ecosystem and want the web equivalent.

## When to Choose Bytebase

- You want a platform that covers change review, access control, data masking, and audit logging — not just a web SQL client.
- Developers write changes and DBAs (or platform engineers) review and deploy them.
- You need compliance artifacts — approval trails, audit logs, masking — for SOC 2, GDPR, or internal policy.
- You want database changes treated like code changes: reviewed, approved, traceable, and rolled out across environments.

## FAQ

### Can I use CloudBeaver and Bytebase together?

Yes. Teams sometimes use CloudBeaver as a general-purpose web SQL client for developers who need ad-hoc querying across many databases, and use Bytebase as the governance layer — any change that ships to production routes through Bytebase's review and approval pipeline.

### Isn't CloudBeaver just "DBeaver in a browser"?

Pretty much. CloudBeaver shares DBeaver's JDBC driver foundation and much of its UI language. The value over DBeaver is that it runs on a shared server and everyone logs in through a browser instead of installing a desktop client. The tradeoff is that some advanced desktop features (ER diagrams, data transfer wizards, complex query builders) are lighter or missing in CloudBeaver.

### Does Bytebase have everything CloudBeaver has?

For everyday team querying, yes — SQL Editor, schema browser, export, sharing. What Bytebase doesn't prioritize is JDBC breadth (25 engines vs CloudBeaver Enterprise's 80+). Bytebase goes deep on its supported engines with engine-specific review rules, schema sync, and masking support; CloudBeaver goes wide across more drivers.

### Is Bytebase Community actually usable for production?

Yes. Community includes the full GUI, 200+ SQL review rules, GitOps integration, multi-environment rollouts, and batch changes — same core workflow as the paid tiers. Limits: 20 users and 10 instances. You upgrade when you need SSO, audit log (Pro), or compliance features like data masking and custom approval flows (Enterprise).

---

Related comparisons:

- [Bytebase vs. DBeaver](/blog/bytebase-vs-dbeaver/)
- [Bytebase vs. DataGrip](/blog/bytebase-vs-datagrip/)
- [Top Open Source SQL Clients](/blog/top-open-source-sql-clients/)
- [Top DBeaver Alternatives](/blog/top-dbeaver-alternative/)



"Bytebase vs Metabase" is a common search, but the two tools sit in different product categories and solve different problems. Bytebase is a database governance platform for developers, DBAs, and platform teams. It handles schema changes, SQL review, access control, and data masking. Metabase is a business intelligence (BI) tool for data teams and business users. It turns SQL queries into dashboards and charts. This 2026 comparison covers where they actually overlap (web SQL editor, team permissions, self-hosting), where they differ (change workflow, BI visualization), and which one fits which role.

## Bytebase vs Metabase at a glance

|  | Metabase | Bytebase |
| --- | --- | --- |
| [Primary purpose](#primary-purpose) | Business intelligence (charts, dashboards) | Database change management + access control |
| [Primary users](#primary-purpose) | Data analysts, business users | Developers, DBAs, platform engineers |
| [Supported databases](#supported-databases) | 20+ (OLAP-leaning) | 20+ (OLTP-leaning, plus NoSQL) |
| [SQL editor](#query-experience) | ✅ (with visualization) | ✅ (SQL client feel, with schema browser) |
| [Dashboards / BI](#visualization-vs-schema-diagram) | ✅ Core product | ❌ Out of scope |
| [Schema change workflow](#change-workflow) | ❌ | ✅ Issue-based with approvals |
| [SQL Review (static + policy)](#sql-review) | ❌ | ✅ 200+ rules |
| [Data masking](#data-masking) | ❌ (row/column permissions only, Pro) | ✅ Dynamic masking with semantic types |
| [Audit log](#audit-log) | ✅ Pro/Enterprise | ✅ Pro/Enterprise |
| [Just-in-time access requests](#access-control) | ❌ | ✅ Enterprise |
| [Starting paid price](#pricing) | $100/mo flat (5 users) | $20/user/mo (Pro, cloud) |
| Open source | ✅ AGPL (fastest-growing BI) | ✅ MIT + Enterprise License |
| Self-host | ✅ | ✅ |

## Primary purpose

**Metabase** is a BI tool. Its core loop is: connect a database, ask a question (visual query builder or SQL), turn the result into a chart, pin the chart to a dashboard, share it with the team. Metabase 57 (Nov 2025) added Remote Sync for Git-tracked dashboard versioning, and Metabase 59 and 60 (Mar–Apr 2026) added AI SQL generation, Data Studio, and the Metabot AI assistant. Its audience is data analysts and business users who need to read the database, not change it.

**Bytebase** is a database governance platform. Its core loop is: register a database, developers submit change issues, SQL Review and custom approval gates run automatically, approved changes are applied and logged, and access to production data is gated by policy, masked, or granted just-in-time. Its audience is developers, DBAs, and platform teams who own the database lifecycle. For background on this category, see [what is database change management](/blog/what-is-database-change-management/).

![mb-position](/content/blog/bytebase-vs-metabase/mb-position.webp)

![bb-3-op](/content/blog/bytebase-vs-metabase/bb-3-op.webp)

In one line: Metabase reads and visualizes data; Bytebase governs and changes the database itself.

## Pricing

Pricing is one of the biggest practical differences. Metabase's paid tiers have a higher floor but bundle BI hosting. Bytebase's Pro tier is per-seat with a low entry point.

| Plan | Metabase | Bytebase |
| --- | --- | --- |
| Free / self-host Community | Open Source (self-host, unlimited users) | Free (self-host or cloud, up to 20 users, 10 instances) |
| Starter / entry paid | **Starter: $100/mo flat for 5 users**, then ~$6/user/mo monthly or $65/user/yr annual (cloud) | **Pro: $20/user/mo** (cloud only, unlimited users, SSO) |
| Mid tier | **Pro: $575/mo base**, then ~$12/user/mo monthly or $130/user/yr annual (cloud or self-host) | n/a |
| Enterprise | **Custom** (median ~$39k/yr, entry ~$20k/yr per Vendr data) | **Custom** (self-host or cloud, dynamic masking, custom approval, audit log, SCIM, SSO) |

Numbers above are from Metabase's public pricing page and Vendr's 2026 buyer data. Two things to watch:

- Metabase's $100/mo Starter cap of 5 users makes the per-user math misleading at small scale. A team of 10 on Starter is effectively $16/user/mo; on Pro, it's $57.50/user/mo before annual discounts.
- Bytebase Pro is cloud-only. Self-hosting drops you into Community (free but capped at 20 users / 10 instances) or Enterprise (custom). Most teams that self-host move straight to Enterprise.

## Supported databases

- **Metabase**: 20+ databases with a BI lean (BigQuery, Snowflake, Redshift, Athena, Druid, Presto, plus the usual OLTP engines: PostgreSQL, MySQL, SQL Server).
- **Bytebase**: 20+ databases with an OLTP lean (PostgreSQL, MySQL, Oracle, SQL Server, TiDB, Spanner, OceanBase), plus NoSQL (MongoDB, Redis) and analytics engines (ClickHouse, Snowflake, Redshift, BigQuery).

Both cover the common set. The tail is different because the products serve different workloads.

## Query experience

Both ship a web SQL editor, but the editor is doing different work in each product.

**Metabase** offers two entry points:

1. **Question builder**: a no-code interactive query for non-technical users. Drops to SQL when needed.
   ![mb-question](/content/blog/bytebase-vs-metabase/mb-question.webp)
2. **SQL query**: a raw SQL editor with schema browser. Results become charts and feed dashboards.
   ![mb-sql-query](/content/blog/bytebase-vs-metabase/mb-sql-query.webp)

**Bytebase SQL Editor** is closer to a traditional SQL client (think DBeaver or pgAdmin in the browser) with auto-completion, a schema browser, saved sheets, and a history pane. For a direct comparison against dedicated SQL clients, see [Bytebase vs DBeaver](/blog/bytebase-vs-dbeaver/) and [Bytebase vs CloudBeaver](/blog/bytebase-vs-cloudbeaver/), or the roundup at [top open source SQL clients](/blog/top-open-source-sql-clients/).

![bb-data-query](/content/blog/bytebase-vs-metabase/bb-data-query.webp)

The key difference: a Metabase query is meant to end in a chart. A Bytebase query is meant to end in verified data for a developer or an ad-hoc fix, and the editor enforces read-only or masking policies when the user opens it.

## Visualization vs schema diagram

- **Metabase**: visualization is the product. Line, bar, pie, map, pivot, funnel, and custom visualizations with dashboard embedding and AI-assisted chart suggestions in Metabase 60.
- **Bytebase**: no chart builder. The only visualization is the **Schema Diagram** for the database structure.
  ![bb-schema-diagram](/content/blog/bytebase-vs-metabase/bb-schema-diagram.webp)

If your team needs dashboards, Bytebase is not the tool. Pair it with Metabase.

## Access control

- **Metabase** organizes content into Collections with three permission levels (`Curate`, `View`, `No Access`) assignable to user groups. The Pro plan adds row- and column-level data permissions.
  ![mb-access-control](/content/blog/bytebase-vs-metabase/mb-access-control.webp)

- **Bytebase** has a two-level model: Workspace (Admin, DBA) manages database instances and global members; Project (Owner, Developer, SQL Editor User) manages day-to-day database work. Enterprise adds fine-grained controls: per-database or per-table access, per-environment policies (open in dev, restricted in prod), and request-based access where a developer files an issue to query or export a specific dataset.
  ![bb-access-control-adv](/content/blog/bytebase-vs-metabase/bb-access-control-adv.webp)

This second model matches [database access control best practices](/blog/database-access-control-best-practices/) for production: least privilege by default, access granted through approvals, every grant tied to an audit trail.

## Data masking

- **Metabase**: no dynamic masking. Row- and column-level permissions on Pro can hide fields from groups, but there's no transformation (e.g., showing `****-1234` instead of a full card number).
- **Bytebase**: dynamic data masking with semantic types. Admins tag a column (email, phone, credit card), Bytebase applies the appropriate masking algorithm, and authorized users can request unmasked access through an approval flow.
  ![bb-masking-graph](/content/blog/bytebase-vs-metabase/bb-masking-graph.webp)

Masking is an Enterprise feature. If you handle regulated data (PCI, HIPAA, GDPR), this is usually the deciding factor.

## Audit log

- **Metabase**: audit log is Pro+. Shows dashboard views, question runs, user sessions. Useful for BI usage analytics.
- **Bytebase**: audit log is Pro and Enterprise. Records every SQL Editor query, every schema change, every access grant, every approval decision. Built for compliance (SOC 2, ISO 27001) rather than product analytics. See [audit log for SOC 2 compliance](/blog/soc2-audit-logging/) for the mapping.

Both products are auditing different things, which follows from their primary purpose.

## SQL Review

**Only Bytebase has this.** SQL Review is a static-analysis layer with 200+ built-in rules plus custom rules (naming conventions, missing primary keys, dangerous statements, performance anti-patterns) that run automatically when a developer submits a change issue. It pairs with custom approval flows so that policy violations can block or gate a merge. Metabase has no equivalent because it does not change the database.

## Change workflow

- **Metabase**: not available. Metabase is a read-path tool; schema changes happen outside it.

- **Bytebase**: the change workflow is the product. A change goes through an issue that includes SQL Review, custom approval, scheduled rollout, rollback, and full history. If a developer types `ALTER TABLE` in SQL Editor, Bytebase prompts them to open an issue (or switch to Admin mode if they have the role).
  ![bb-issue](/content/blog/bytebase-vs-metabase/bb-issue-waiting.webp)

  Every change is recorded in the **Change History**. Bytebase also detects out-of-band changes and marks them as **Schema Drift**.
  ![bb-history](/content/blog/bytebase-vs-metabase/bb-history.webp)

## Open source and community

Both are open source, and they often appear together in database tooling stacks. Metabase has the longer history (launched 2015) and a larger BI user base. Bytebase (launched 2022) has grown faster on a per-month basis and sits in a different category.

![star-history](/content/blog/bytebase-vs-metabase/star-history.webp)

## Which should you choose?

This is rarely an either-or decision. A typical data stack uses both:

- **Pick Metabase** if your goal is dashboards, self-serve analytics, or embedded reporting. It is one of the cheapest ways to give business users a view into the database.
- **Pick Bytebase** if your goal is to govern database changes and production access: schema migrations with review and approval, masked queries for developers, audit trails for compliance.
- **Run both** if you have developers changing the schema and analysts reading from it. Bytebase protects the write path; Metabase powers the read path. They don't overlap.

## FAQ

**Is Bytebase a replacement for Metabase?**
No. Bytebase does not build dashboards or charts. If you need BI, you still need Metabase (or Superset, Looker, etc.). Bytebase replaces schema-change spreadsheets, ad-hoc DBA queues, and per-database access scripts: problems Metabase doesn't touch.

**Can Bytebase and Metabase run on the same database?**
Yes, and many teams do. Bytebase connects with an admin-level account to manage changes and access; Metabase connects with a read-only account for analytics. They don't conflict.

**Does Bytebase do data visualization?**
Only a Schema Diagram for database structure. There is no chart builder, no dashboard, no pivot. For data visualization, pair Bytebase with a BI tool.

**Does Metabase have schema change management or SQL review?**
No. Metabase reads the database; it does not alter it. Schema changes, SQL review, and approval workflows require a database change management tool like Bytebase, Liquibase, or Flyway.

**Which is cheaper for a 20-person team?**
Rough math with public pricing: Metabase Starter is $100/mo flat for 5 users + $65/user/yr for the other 15 on annual = ~$181/mo. Bytebase Pro is $20/user/mo × 20 = $400/mo. Bytebase Community (self-hosted) is free for up to 20 users. The answer depends on whether you need BI, self-hosting, and which features you actually use.

**Are both open source?**
Yes. Metabase is AGPL-licensed; Bytebase uses a mix of licenses with a commercial Enterprise edition. Self-hosting is supported by both.



> DBeaver is a desktop SQL client. Bytebase is a database governance platform.

DBeaver and Bytebase solve different problems. DBeaver is a universal SQL client — you install it locally, connect to a database, and query or edit data through a rich GUI. Bytebase is a server-side platform — you deploy it once, and teams use it for change review, access control, data masking, and audit logging across every database. If your team needs a powerful client to explore and edit data, DBeaver is the obvious choice. If your team needs to control who changes production and keep an audit trail of every operation, that's what Bytebase is built for.

## What Bytebase and DBeaver have in common

- Database management through a GUI — querying, exporting, data visualization, schema browsing.
- Tiered pricing with both free and paid plans.
- Support for a wide range of SQL and NoSQL databases.
- Open source — DBeaver has a longer history; Bytebase's star growth has outpaced it since 2023.
  ![star-history](/content/blog/bytebase-vs-dbeaver/star-history.webp)

## Key Differences Between Bytebase and DBeaver

The most significant difference is that Bytebase is a server-side platform built for teams — DBAs centralize access control, data masking, and audit logging. DBeaver is a client-side tool built primarily for individual use, with a Team Edition that adds shared connections but still runs as a desktop app per user.

|                                             | DBeaver                    | Bytebase                                              |
| ------------------------------------------- | -------------------------- | ----------------------------------------------------- |
| [Product position](#product-position)       | Powerful desktop SQL client | Database governance platform                          |
| [Developer interface](#developer-interface) | Client-side, desktop-based | Server-side, web-based                                |
| [Installation](#installation)               | Java-based, requires JVM   | Go binary, no external dependency                     |
| [Supported databases](#supported-databases) | 200+                       | 23                                                    |
| [Query](#query)                             | ✅                         | ✅                                                    |
| [Change](#change)                           | Direct                     | Review workflow (Git-style issue) + Admin mode        |
| [SQL review rules](#change)                 | ❌                         | ✅ 200+ rules (free)                                  |
| [Access control](#access-control)           | ❌ (Team Edition: shared conns only) | ✅ Free: workspace/project roles; Enterprise: database/table-level |
| [Data masking](#data-masking)               | ❌                         | ✅ Enterprise (dynamic, column-level)                 |
| [Compare schema](#compare)                  | 1:1 (Pro)                  | 1:N (free)                                            |
| [Audit log](#audit-log)                     | ❌                         | ✅ Pro                                                |

### Product position

- **DBeaver**: A universal SQL client. It started as an individual desktop tool and expanded to a Team Edition and a web-based CloudBeaver. Its center of gravity is still **individual database management** — rich query editor, data viewer, schema browser, ER diagrams.
  ![dbv-position](/content/blog/bytebase-vs-dbeaver/dbv-position.webp)

- **Bytebase**: A database governance platform for developers, DBAs, and platform teams. Built around **team workflows** — change review, custom approval flows, access control, data masking, audit logging — with a SQL Editor layered on top.
  ![bb-3-op](/content/blog/bytebase-vs-dbeaver/bb-3-op.webp)

### Developer interface

- **DBeaver**: Desktop GUI on Windows / macOS / Linux. Every user installs it locally and connects directly to databases. DBeaver Team Edition adds a shared connection store, but the client is still a per-user desktop app.
  ![dbv-gui](/content/blog/bytebase-vs-dbeaver/dbv-gui.webp)

- **Bytebase**: Web GUI with an issue-based change workflow plus a SQL Editor for querying. Also exposes an [API](https://api.bytebase.com/), a [Terraform provider](https://docs.bytebase.com/integrations/terraform/overview), and [GitOps](https://docs.bytebase.com/gitops/overview) for teams that prefer automation.
  ![bb-issue](/content/blog/bytebase-vs-dbeaver/bb-issue-waiting.webp)
  ![bb-gui](/content/blog/bytebase-vs-dbeaver/bb-gui.webp)

### Installation

- **DBeaver**: Download from the official website — bundled with Eclipse and a JVM. DBeaver Team Edition requires a server on Linux or macOS.

- **Bytebase**: Single Go binary, no external dependencies. Also ships as a [Docker image](https://docs.bytebase.com/get-started/deploy-with-docker) and [Helm chart for Kubernetes](https://docs.bytebase.com/get-started/deploy-with-kubernetes).

### Supported databases

- **DBeaver**: 200+ databases in the **Community** edition via JDBC, covering MySQL, PostgreSQL, Oracle, SQL Server, SQLite, and many more. The **Pro** editions (Ultimate, Team) add NoSQL drivers (MongoDB, Cassandra, Redis, DynamoDB) and cloud warehouses (Redshift, BigQuery, Snowflake, Databricks).

- **Bytebase**: 25 engines — 9 RDBMS (MySQL, PostgreSQL, Oracle, SQL Server, MariaDB, TiDB, OceanBase, CockroachDB, Spanner), 6 NoSQL (MongoDB, Redis, Cassandra, DocumentDB, DynamoDB, Cosmos DB), 9 data warehouses (Snowflake, BigQuery, Redshift, Hive, ClickHouse, Databricks, StarRocks, Doris, Trino), and Elasticsearch. Depth varies by engine: mainstream ones like MySQL and PostgreSQL get the deepest support (SQL review rules, schema sync, data masking), while the rest cover querying and change deployment.

### Query

- **DBeaver**: Query by double-clicking a table or by writing SQL in the editor with templates, syntax highlighting, and autocomplete. The GUI is dense and configurable — power users can tune toolbars, keybindings, and result grid behavior in detail.
  ![dbv-data-query](/content/blog/bytebase-vs-dbeaver/dbv-data-query.webp)

  Saved scripts stay local in Community; **Team Edition** shares scripts and connections across the team.
  ![dbv-sheets](/content/blog/bytebase-vs-dbeaver/dbv-sheets.webp)

- **Bytebase**: The **SQL Editor** runs queries through the platform — double-click a table or write SQL with autocomplete. The GUI is lighter than DBeaver but covers everyday querying, exporting, and chart-based visualization.
  ![bb-data-query](/content/blog/bytebase-vs-dbeaver/bb-data-query.webp)

  Save or star scripts for quick access. On **Pro** and above, share scripts with your team or project.
  ![bb-sheets](/content/blog/bytebase-vs-dbeaver/bb-sheets.webp)

### Change

- **DBeaver**: Edit data directly in the result grid or run SQL manually. Edits stay local to the user's session — no review, no approval, no centralized audit. A per-user transaction log records what you ran.
  ![dbv-edit-cell-dbclick](/content/blog/bytebase-vs-dbeaver/dbv-edit-cell-dbclick.webp)

  ![dbv-change-history](/content/blog/bytebase-vs-dbeaver/dbv-change-history.webp)

- **Bytebase**: Changes flow through an **issue** — like a pull request for the database. Each issue runs automated [SQL review](https://docs.bytebase.com/sql-review/review-rules) (200+ rules), routes through a [custom approval flow](https://docs.bytebase.com/change-database/approval) if configured, and deploys with full history tracking.
  ![bb-issue](/content/blog/bytebase-vs-dbeaver/bb-issue-waiting.webp)

  Every change is recorded in **Change History** with diff view and rollback scripts.
  ![bb-history](/content/blog/bytebase-vs-dbeaver/bb-history.webp)

  Ad-hoc DDL (e.g. `ALTER TABLE` typed into SQL Editor) either creates a new issue or requires **Admin mode** — a deliberate escape hatch for break-glass scenarios, similar to SSH on a server.
  ![bb-force-issue](/content/blog/bytebase-vs-dbeaver/bb-force-issue.webp)
  ![bb-force-issue-preview](/content/blog/bytebase-vs-dbeaver/bb-force-issue-preview.webp)

For schema changes, most teams use the visual **Schema Editor** — edit columns, constraints, and indexes in the GUI and Bytebase generates the DDL automatically.

![bb-select-db](/content/blog/bytebase-vs-dbeaver/bb-select-db.webp)
![bb-schema-editor](/content/blog/bytebase-vs-dbeaver/bb-schema-editor.webp)

### Access Control

- **DBeaver**: Desktop-based — each user's credentials live in their local client. DBeaver Team Edition centralizes connection storage and adds shared credential management, but actual database permissions are still whatever the DBMS grants each connecting user. No concept of granular permission control inside DBeaver itself.

- **Bytebase**: Roles are scoped to **Workspace** and **Project** levels. `Workspace Admin` / `DBA` provision instances and manage members; `Project Owner` manages databases, issues, and roles within their project. Individuals can hold multiple project-level roles like `Project Developer` (change database) or `SQL Editor User` (query only).
  ![bb-access-control](/content/blog/bytebase-vs-dbeaver/bb-access-control.webp)

  On **Community**, users only see projects they're assigned to. On **Enterprise**, `Project Owner` can grant access down to specific databases or tables, and developers can request [Just-In-Time access](https://docs.bytebase.com/security/database-permission/overview) via a request issue that expires automatically.
  ![bb-access-control-adv](/content/blog/bytebase-vs-dbeaver/bb-access-control-adv.webp)

  Different environments can enforce different policies — e.g., read-only access to production, full access to development.
  ![bb-access-environment](/content/blog/bytebase-vs-dbeaver/bb-access-control-env.webp)

### Data Masking

- **DBeaver**: No data masking. Because DBeaver is client-side, the raw data is already on the user's machine by the time it's rendered.

- **Bytebase**: [Dynamic data masking](https://docs.bytebase.com/security/data-masking/overview) at the column level (Enterprise). `Admin` / `DBA` / `Project Owner` configure masking per column and authorize specific users to see the original data. Everyone else sees masked values in query results.

  Semantic types (email, phone, credit card, SSN) let you define reusable masking algorithms applied consistently across the schema.
  ![bb-masking-graph](/content/blog/bytebase-vs-dbeaver/bb-masking-graph.webp)

### Compare

- **DBeaver**: Schema compare is **1:1** — compare two databases at a time. Results export as DDL scripts or Liquibase changelogs. Available in DBeaver Pro.
  ![dbv-schema-compare](/content/blog/bytebase-vs-dbeaver/dbv-schema-compare.webp)

- **Bytebase**: **Schema Sync** is **1:N** (free) — pick one source database and compare against several targets at once. Results become a batch change issue that routes through SQL review, approval, and rollout.
  ![bb-schema-compare-several](/content/blog/bytebase-vs-dbeaver/bb-schema-compare-several.webp)

  ![bb-batch-issue](/content/blog/bytebase-vs-dbeaver/bb-batch-issue.webp)

### Audit log

- **DBeaver**: No audit log — client-side by design, so there's no central event stream to capture.

- **Bytebase**: [Audit Log](https://docs.bytebase.com/security/audit-log) (Pro and above) records every action in the platform — queries, DDL, approvals, policy changes. Filter by user, action type, or time range; export for SIEM ingestion.
  ![bb-audit-log](/content/blog/bytebase-vs-dbeaver/bb-audit-log-dropdown.webp)

### Pricing

- **DBeaver**: Community is free and open source. DBeaver Pro is $10/month for Ultimate (personal) or $15/user/month for Team Edition (collaborative, server-based). Enterprise is custom pricing.

- **Bytebase**: [Community](https://www.bytebase.com/pricing/) is free, self-hosted, up to 20 users and 10 instances — includes SQL review, GitOps, multi-env rollouts. Pro is $20/user/month (cloud, up to 10 instances) and adds SSO, audit log, user groups. Enterprise is custom pricing, self-hosted or cloud, and unlocks dynamic data masking, custom approval flows, custom roles, SCIM, LDAP/OIDC SSO, and 2FA.

## When to Choose DBeaver

- You need a powerful desktop SQL client to explore and edit data across many database types.
- Your team works individually — each developer manages their own connections and runs their own queries.
- You rely on JDBC breadth (200+ engines) and don't need server-side governance.
- Query convenience and rich UI features (ER diagrams, data transfer, visual query builder) matter more than review workflows.

## When to Choose Bytebase

- You want a single platform that covers change review, access control, data masking, and audit logging — not just a SQL client.
- Developers write changes and DBAs (or platform engineers) review and deploy them.
- You need compliance artifacts — approval trails, audit logs, masking — for SOC 2, GDPR, or internal policy.
- You want database changes treated like code changes: reviewed, approved, traceable, and rolled out across environments.

## FAQ

### Can I use DBeaver and Bytebase together?

Yes — many teams do. Developers use DBeaver locally for rich data exploration and ad-hoc querying, and submit schema or production data changes through Bytebase for review and controlled rollout. DBeaver handles "I need to explore"; Bytebase handles "this change is going to production."

### Does Bytebase replace my local SQL client?

For everyday querying across reviewed databases, yes — the SQL Editor works well. For deep data exploration, complex result-grid operations, or connecting to obscure JDBC databases, a desktop client like DBeaver still has an edge. Bytebase's SQL Editor is built around team workflow, not raw client capability.

### Is DBeaver Team Edition comparable to Bytebase?

Partially. Team Edition shares connections and scripts across a team and adds a server-based backend — but it doesn't add review workflows, approval flows, data masking, or audit logging. The core mental model is still "desktop clients connecting to a shared connection store."

### Is Bytebase Community actually usable for production?

Yes. Community includes the full GUI, 200+ SQL review rules, GitOps integration, multi-environment rollouts, and batch changes — same core workflow as the paid tiers. Limits: 20 users and 10 instances. You upgrade when you need SSO, audit log (Pro), or compliance features like data masking and custom approval flows (Enterprise).

---

Related comparisons:

- [Bytebase vs. CloudBeaver](/blog/bytebase-vs-cloudbeaver/)
- [Bytebase vs. DataGrip](/blog/bytebase-vs-datagrip/)
- [Top DBeaver Alternatives](/blog/top-dbeaver-alternative/)
- [pgAdmin vs. DBeaver](/blog/pgadmin-vs-dbeaver/)
- [Top Open Source SQL Clients](/blog/top-open-source-sql-clients/)



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database governance platform that can manage both Aurora and RDS. We update the post every year.

</HintBlock>

![gartner](/content/blog/aurora-vs-rds/gartner-2024.webp)

Year after year, AWS ranks among the top in Gartner's Magic Quadrant for Cloud Database Management Systems — in the latest report it was named a Leader for the 11th consecutive year and positioned highest for Ability to Execute among the 20 vendors evaluated. The AWS OLTP relational database portfolio consists of 3 products:

- `Amazon RDS` (Relational Database Service) was announced by AWS on October 22, 2009 and probably is the largest database fleet in the world.

- `Amazon Aurora` was announced on November 12, 2014, positioned as RDS's premium offering, providing unparalleled high performance and availability at global scale with full MySQL and PostgreSQL compatibility. Aurora passed its 10-year milestone in 2024.

- `Amazon Aurora DSQL` was announced in December 2024 at AWS re:Invent and reached general availability in May 2025. It's a serverless, distributed SQL database with 99.999% multi-Region availability, strong consistency, PostgreSQL compatibility, and claims of 4x faster reads and writes compared to other distributed SQL databases.

When an engineering team onboards AWS, one of the very first tasks is to choose the right database solution for their needs (unless they decide to go NoSQL, then they need to pick between MongoDB, DynamoDB, and other options). Below we provide our engineering perspective to compare the following dimensions:

- [Supported Databases](#supported-databases)
- [Architecture](#architecture)
- [Compatibility](#compatibility)
- [Performance](#performance)
- [Elasticity](#elasticity)
- [High Availability](#high-availability)
- [Pricing](#pricing)
- [Recommended Setup](#recommended-setup)
- [Further Readings](#further-readings)

## Supported Databases

![databases](/content/blog/aurora-vs-rds/databases.webp)

RDS supports all mainstream relational databases including MySQL, PostgreSQL, MariaDB, SQL Server, Oracle, and Db2, with Extended Support for older versions. As of 2026, RDS maintains major versions of these engines.

Aurora supports MySQL and PostgreSQL. The service has maintained its focus on these two popular open-source database engines, optimizing their performance in the cloud environment.

![databases](/content/blog/aurora-vs-rds/upstream-version.webp)

Aurora DSQL, the newest addition to AWS's database portfolio, is PostgreSQL-compatible only — there is no MySQL-compatible flavor.

RDS offers all upstream versions, but some are reaching end of standard support, such as PostgreSQL 13 (end date: Feb 28, 2026) and MySQL 8.0 (end date: Jul 31, 2026). Extended Support is available for up to 3 years past that date at additional cost, after which RDS auto-upgrades the instance.

Aurora tracks recent upstream releases (Aurora PostgreSQL 16.x/17.x, with PostgreSQL 18.1 already in the RDS preview environment; Aurora MySQL compatible with MySQL 8.0/8.4). Aurora also doubled maximum storage to 256 TiB with no upfront provisioning. Aurora DSQL focuses on PostgreSQL compatibility and distributed workloads with strong consistency across regions.

## Architecture

RDS mostly runs the vanilla database engines on the cloud. While Aurora is a **cloud-native** database service. Aurora is cloud-native because it leverages the cloud environment to separate compute and storage, uses [Amazon S3](https://aws.amazon.com/s3/) to persist data. This novel approach increase performance, high availability, and scalability.

![aurora-arch](/content/blog/aurora-vs-rds/aurora-arch.webp)

Aurora DSQL builds on this with a distributed, multi-region design, enabling active-active architecture for global operations. It ensures consistency through **decoupled transaction processing** and **microsecond-accurate time synchronization**, ideal for globally distributed, highly available applications.

![aurora-dsql-arch](/content/blog/aurora-vs-rds/aurora-dsql-arch.webp)

## Compatibility

Both Aurora and RDS bear the same limitation as a Cloud database:

- You can only have [semi-super user](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/UsingWithRDS.MasterAccounts.html).
- You are not allowed to access the database server file systems. For MySQL, this means you can only use `LOAD DATA LOCAL` to import data from your local file system, while you can't use `LOAD DATA` to import from the server file system.

Though Aurora is a proprietary technology, it mostly differs from the vanilla MySQL / PostgreSQL on the storage engine layer. On the other hand, the server layer, which determines most user-facing behaviors is almost identical. Thus the main compatibility gaps for Aurora is storage engine related. e.g. Aurora for MySQL only supports InnoDB, while RDS supports old engines such as MyISAM (however, you won't use MyISAM engine anyway if you start a new project).

Saying that, Aurora codebase diverges more from the vanilla MySQL/Postgres than RDS, thus you should expect more lags for the AWS team to bring in the latest upstream update. e.g. Aurora PostgreSQL added the popular pgvector extension 2 months later than RDS.

![pgvector-rds](/content/blog/aurora-vs-rds/pgvector-rds.webp) ![pgvector-aurora](/content/blog/aurora-vs-rds/pgvector-aurora.webp)

For Aurora DSQL, even after GA the extension ecosystem is still developing. It maintains core PostgreSQL compatibility and has since added PostgreSQL views along with AWS Backup, PrivateLink, CloudFormation, CloudTrail, and KMS customer-managed keys, but not all extensions available in standard PostgreSQL or Aurora PostgreSQL are supported. AWS continues to expand compatibility based on customer demand, so check the [PostgreSQL compatibility reference](https://docs.aws.amazon.com/aurora-dsql/latest/userguide/working-with-postgresql-compatibility.html) before assuming a feature is available.

## Performance

According to [the official website](https://aws.amazon.com/rds/aurora/features/), Aurora offers up to 5x the throughput of MySQL and 3x the throughput of PostgreSQL.

This [benchmark](https://dev.to/aws-heroes/amazon-aurora-is-now-60-times-faster-than-rds-for-mysql-really-3c0e) suggesting Aurora can be 60 times faster than RDS.

Aurora provides better write performance because it reduces the write amplification by only sending the redo log to the remote storage service, which eliminates other writes during transaction commit path such as the infamous double-write buffer.

Aurora provides better read scalability because of the log-based architecture, it can support up to 15 read-replicas. RDS can only support 5, RDS doesn't support more because the classic streaming replication carries more performance penalty on the primary. Aurora also incurs much lower replication lags, especially under write-heavy load.

Aurora DSQL takes performance to a new level for distributed workloads. According to AWS, Aurora DSQL delivers 4x faster reads and writes compared to other popular distributed SQL databases. This is achieved through its innovative approach to transaction processing that parallelizes writes across regions and its use of the Amazon Time Sync Service for precise operation ordering.

RDS uses EBS, the disk performance differs based on the [storage types](https://docs.aws.amazon.com//AmazonRDS/latest/UserGuide/CHAP_Storage.html). Since 2023, AWS has continued to improve the underlying EBS performance, but the architectural differences still give Aurora and Aurora DSQL significant advantages for most workload types.

In general, Aurora outperforms RDS considerably, and Aurora DSQL offers even greater performance for distributed workloads. But you still need to benchmark for your own specific use cases to determine the best fit.

## Elasticity

![gp2-burst](/content/blog/aurora-vs-rds/gp2-burst.webp)

RDS doesn't have much elasticity, if you choose the gp2 SSD storage, it can be [bursted](https://docs.aws.amazon.com//AmazonRDS/latest/UserGuide/CHAP_Storage.html) and that's pretty much it.

Standard Aurora can provision read-replicas quickly, other than that, it doesn't provide elasticity out of the box. However, Aurora's architecture does allow for faster storage scaling compared to RDS.

Aurora Serverless and especially its [Serverless v2](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/aurora-serverless-v2.html) offers great elasticity to scale up/down computing resources. Great for workloads such as e-commerce. Serverless v2 keeps improving: the 2026 [platform version 4](https://aws.amazon.com/blogs/database/aurora-serverless-faster-performance-enhanced-scaling-and-still-scales-down-to-zero/) delivers roughly 27–34% higher throughput than platform version 3 and a faster ramp-up, and a new [Express configuration](https://aws.amazon.com/blogs/aws/announcing-amazon-aurora-postgresql-serverless-database-creation-in-seconds/) provisions an Aurora PostgreSQL serverless database in seconds — now also available in the AWS Free Tier.

Aurora DSQL takes elasticity to a new level with its distributed architecture. It automatically scales to meet any workload demand without database sharding or instance upgrades, and can scale reads and writes independently, eliminating scaling bottlenecks while maintaining performance. This makes Aurora DSQL particularly well-suited for applications with unpredictable global traffic patterns or those requiring rapid scaling across regions.

## High Availability

![ha](/content/blog/aurora-vs-rds/ha.webp)

RDS offers multi-AZ HA setup with a SLA of `up to 99.95%`, while Aurora HA offers `up to 99.99%`. Aurora can failover faster because of its log-based architecture. And for write-intensive load, RDS failover are more problematic because of the high replication lag.

Aurora also has [global databases](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/aurora-global-database.html) to enable low latency global reads and disaster recovery from outages across an AWS Region.

Aurora DSQL takes high availability to a new level with 99.99% single-Region and 99.999% multi-Region availability (five nines for multi-Region). Its active-active architecture and automated failure recovery ensure that applications can always read and write to any Aurora DSQL endpoint, even during regional outages. All transactions written in one Region are reflected in other Regions with strong consistency, eliminating the traditional trade-offs between availability and consistency.

In 2024, AWS also enhanced Amazon DynamoDB global tables with multi-Region strong consistency, using the same underlying technology leveraged by Aurora DSQL. This indicates AWS's strategic focus on providing strongly consistent, highly available database services across its portfolio.

## Pricing

![pricing-rds-aurora](/content/blog/aurora-vs-rds/pricing-rds-aurora.webp)

If you follow the AWS wizard to pick the production template (8c64g r6g.2xlarge + multi-AZ HA), you will find the price for Aurora is much cheaper than RDS. Too good to be true.

![pricing-aurora-notes](/content/blog/aurora-vs-rds/pricing-aurora-notes.webp)

The caveat is Aurora also charges on I/O that is not included in the pricing estimate. This surprised quite a few customers when they receive a huge AWS bill. This unpredictable cost was the biggest concern for teams adopting Aurora initially. In 2023, Aurora fixed this by introducing Aurora I/O-Optimized to include the I/O cost in the storage, and this pricing model has continued to be refined through 2025.

![pricing-aurora-io-optimized](/content/blog/aurora-vs-rds/pricing-aurora-io-optimized.webp)

At re:Invent 2025, AWS introduced [Database Savings Plans](https://aws.amazon.com/savingsplans/database-pricing/), a commitment-based discount that spans Aurora, RDS, and other database services — up to 35% off serverless (ACU) usage and up to 20% off provisioned Gen 7+ instances (db.r7/r8g, db.m7/m7g), with the flexibility to shift across instance types, Regions, and even engines while keeping the discount. Older instance families still rely on Reserved Instances.

For RDS, starting March 2024, Extended Support fees apply (per-vCPU-per-hour, rising over three years) for outdated database versions, making version planning critical — PostgreSQL 13 and MySQL 8.0 both reach end of standard support in 2026.

Aurora DSQL bills by [DPU](https://aws.amazon.com/rds/aurora/dsql/pricing/) (Distributed Processing Unit), a normalized unit covering query processing, reads, and writes — roughly $8 per million DPU plus $0.33 per GB-month of storage in US East as of July 2026, with a monthly free tier. It's more expensive per request but can be cost-effective for multi-region, strongly consistent applications compared to building custom solutions.

Besides, only RDS offers free-tier and low db instance tiers starting from 2c1g t3.micro. Standard Aurora starts from 2c16g r5.large and burstable classes start from 2c4g t3.medium. Aurora Serverless can specify 0.5 ACU as the minimum. 1 ACU provides 2 GiB of memory and corresponding compute and networking.

You should use [AWS Pricing Calculator](https://calculator.aws/) and it's also a good idea to monitor your database cost by using a specialized tool such as [Vantage](https://www.vantage.sh/).

## Recommended Setup

Capability-wise, AWS Aurora beats RDS in almost every aspect, and Aurora DSQL now extends these capabilities even further for distributed applications. AWS continues to invest more resources in Aurora and Aurora DSQL than in RDS since these services are key differentiators from other cloud vendors. Though Aurora is mostly MySQL / PostgreSQL compatible, it creates a different vendor lock-in by providing unmatched ROI. Aurora DSQL adds another layer of capability for applications requiring global distribution with strong consistency.

For bootstrapped or small business, Aurora is still pricy and RDS is a cost-effective solution:

| Startup       | Choice         |
| ------------- | -------------- |
| Test Instance | RDS without HA |
| Prod Instance | RDS with HA    |

For growth stage business, Aurora is more suitable, and Aurora Serverless offers the extra flexibility to optimize the cost.

| Growth                               | Choice                       |
| ------------------------------------ | ---------------------------- |
| Always-on Test Instance              | Standard Aurora without HA   |
| On-demand Test Instance              | Aurora Serverless without HA |
| Prod Instance                        | Standard Aurora with HA      |
| Prod Instance with fluctuate traffic | Aurora Serverless with HA    |

For enterprise businesses with global applications requiring the highest levels of availability and consistency, Aurora DSQL provides capabilities that were previously difficult to achieve:

| Enterprise                                           | Choice      |
| ---------------------------------------------------- | ----------- |
| Global applications                                  | Aurora DSQL |
| Multi-region applications with high write throughput | Aurora DSQL |

Normally, you should start with RDS and migrate to Aurora when the business takes off, and consider Aurora DSQL when your application requires global distribution with strong consistency. AWS provides [detailed guide](https://aws.amazon.com/getting-started/hands-on/migrate-rdsmysql-to-auroramysql/) on how to migrate from RDS to Aurora with near zero downtime, and similar migration paths are available from Aurora to Aurora DSQL.

---

After you decide the database product, there remains task to figure out the database development workflow:

- How developers propagate the change from dev to production?
- How to get the SQL change peer-reviewed by the DB person in an efficient manner? Or even better with automated SQL checks?
- How to enforce data access policy for PII compliance?

If you are thinking about these problems, please check out Bytebase. It's an [all-in-one tool](/blog/all-database-tools-bytebase-replaces/) to centralize all human-to-db operations for heterogeneous databases.

![change-query-secure-govern-database-all-in-one](/content/blog/_shared/db-scheme-lg.png)

## References

- [Amazon Aurora ascendant: How we designed a cloud-native relational database](https://www.allthingsdistributed.com/2019/03/amazon-aurora-design-cloud-native-relational-database.html)
- [Amazon database master user account privileges](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/UsingWithRDS.MasterAccounts.html)
- [Amazon Aurora features](https://aws.amazon.com/rds/aurora/features/)
- [Amazon Aurora pricing](https://aws.amazon.com/rds/aurora/pricing/)
- [Amazon Aurora DSQL: The fastest distributed SQL database](https://aws.amazon.com/rds/aurora/dsql/)
- [Using Aurora Serverless v2](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/aurora-serverless-v2.html)
- [High availability for Amazon Aurora](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Concepts.AuroraHighAvailability.html)
- [Amazon RDS features](https://aws.amazon.com/rds/features/)
- [Amazon RDS pricing](https://aws.amazon.com/rds/pricing/)
- [Amazon RDS Extended Support](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/extended-support.html)
- [Amazon RDS DB instance storage](https://docs.aws.amazon.com//AmazonRDS/latest/UserGuide/CHAP_Storage.html)
- [AWS Database Savings Plans](https://aws.amazon.com/savingsplans/database-pricing/)
- [Amazon RDS Extended Support engine version dates](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/extended-support-viewing-support-dates.html)



<HintBlock type="info">

This post is maintained by Bytebase, an open-source database governance platform that can manage both Postgres and MongoDB. We update the post every year.

</HintBlock>

> For the impatience, jump to the [last section](#postgres-or-mongodb) to see the comparison table.

## Why Comparing Postgres and MongoDB

Postgres is the most admired, desired database in the last 2 Stack Overflow surveys ([2024](https://survey.stackoverflow.co/2024/technology/#1-databases), [2023](https://survey.stackoverflow.co/2023/#most-popular-technologies-database).

![stackoverflow](/content/blog/postgres-vs-mongodb/stackoverflow.webp)

ICYDK, MongoDB used to hold that title for 4 consecutive years from 2017 to 2020. And according to DB-Engines, Postgres and MongoDB are among the top 5 databases. They are the two climbling the ladder and eating the shares of the big three, Oracle, MySQL, and Microsoft SQL Server.

![db-engines](/content/blog/postgres-vs-mongodb/db-engines.webp)

MongoDB is widely perceived as a NoSQL database which is opposite to the SQL database clan including Postgres. But in recent years, they are converging:

- MongoDB becomes more like a traditional RDBMS, adding multi-document ACID transaction, secondary-index, advanced query capabilities.
- Postgres keeps improving its JSON capabilities such as indexing, query optimization, and more operators, which makes people wonder whether MongoDB becomes obsolete.

![hn](/content/blog/postgres-vs-mongodb/hn.webp)

In 2018, folks at The Guardian wrote a [lengthy post](https://www.theguardian.com/info/2018/nov/30/bye-bye-mongo-hello-postgres) about migrating MongoDB to Postgres. A lot of things have changed since then, but one thing still holds true, it's always painful to migrate databases.

![hn](/content/blog/postgres-vs-mongodb/byebye-mongo.webp)

At Bytebase, we work with both databases extensively since the Bytebase product needs to integrate with both. Our founders also build [Google Cloud SQL](https://cloud.google.com/sql), one of the largest hosted database services.

Based on our operating experience, below we give an extensive comparison between Postgres and MongoDB from the following dimensions:

- [License](#license)
- [Data Model](#data-model)
- [JSON Support](#json-support)
- [Performance](#performance)
- [Reliability](#reliability)
- [Scalability](#scalability)
- [Usability](#usability)
- [Operability](#operability)
- [Ecosystem](#ecosystem)
- [AI](#ai)

_Unless otherwise specified, the comparison below is between the latest major release, Postgres 15 vs. MongoDB 6. We also use Postgres instead of PostgreSQL throughout the article, though we know the latter is the official name, which is considered as [the biggest mistake in Postgres History](https://www.craigkerstiens.com/2018/10/30/postgres-biggest-mistake/)_.

## License

- Postgres is released under the PostgreSQL license which is a liberal Open Source license similar to the BSD or MIT licenses.

- MongoDB community edition is released under [Server Side Public License (SSPL)](https://en.wikipedia.org/wiki/Server_Side_Public_License), a license created by MongoDB Inc. itself to prevent others from offering the competing MongoDB service. MongoDB Inc. also provides commercial licenses upon request.

Most companies use databases to support their internal infrastructure, both Postgres and MongoDB permit this usage.

## Data Model

Postgres is a relational database management system. It stores data in tables with predefined columns and data types. Relationships between tables are established using foreign keys.

MongoDB is a document-oriented database, which means that data is stored as documents in a collection. Each document is a JSON-like structure that can contain nested fields and arrays. MongoDB is designed to handle unstructured and semi-structured data.

| Postgres (Tabular Model) | MongoDB (Document Model) |
| ------------------------ | ------------------------ |
| Database                 | Database                 |
| Schema                   | N/A                      |
| Table                    | Collection               |
| Row                      | Document                 |
| Column                   | Field                    |

Postgres also supports JSON column, so one can also use Postgres in the MongoDB way, defining table as:

```sql
CREATE TABLE my_collection (
   id SERIAL PRIMARY KEY,
   data JSONB
);
```

## JSON Support

Both MongoDB and Postgres are very capable of handling JSON. MongoDB stores JSON using its own invented [BSON](https://www.mongodb.com/docs/manual/reference/bson-types/), while Postgres uses a different JSONB format. For those interested, there is a [lengthy discussion](https://www.postgresql.org/message-id/CA%2BTgmoagjFfJst%3D9kSu4rZatCE8SRuOQCH_h-_YW%3D4_c687GTA%40mail.gmail.com) around whether to choose BSON or JSONB in Postgres.

MongoDB has 2 advantages:

1. Built-in schema validator.
1. Its integration with the Node.js/frontend ecosystem. MongoDB is a favored choice among full-stack developers who commonly utilize Node.

## Performance

Performance is primarily determined by the access pattern. If an operation involves different entities, MongoDB is usually faster because data is de-normalized and doesn't require costly joins between tables. On the other hand, Postgres is more capable of handling complex queries thanks to SQL and its sophisticated query optimizer.

## Reliability

MongoDB was known to be less reliable because it didn't support ACID transaction semantics in the early days. This has changed since they acquired WiredTiger and use its WiredTiger storage engine. Today, from the transaction perspective, MongoDB is as solid as Postgres.

MongoDB provides built-in automatic failover via its replica set. Postgres needs 3rd party solutions like [pg_auto_failover](https://github.com/hapostgres/pg_auto_failover).

## Scalability

MongoDB scales out, while Postgres scales up.

MongoDB is a distributed database supporting automatic sharding. For Postgres, people usually scale up the single node postgres first and defer the sharding solution as late as possible. Of course, sharding Postgres is doable:

- [Sharding & IDs at Instagram](https://instagram-engineering.com/sharding-ids-at-instagram-1cf5a71e5a5c)
- [Herding elephants: Lessons learned from sharding Postgres at Notion](https://www.notion.so/blog/sharding-postgres-at-notion)

## Usability

MongoDB doesn't enforce schema upfront and has an easy learning curve. Back in 2010, 10gen (the original name for the company that developed MongoDB) released the first version of the official MongoDB Node.js driver, which allowed developers to easily interact with MongoDB databases from Node.js applications. The driver provided a simple and intuitive API that supported a wide range of MongoDB features, including querying, indexing, and aggregation. Today, MongoDB still is the de-facto choice for full-stack developers because of its ease of use.

Postgres, as a relational database, enforces schemas. And even among the relational database group, Postgres is more rigorous than other peers like MySQL.

![stackoverflow](/content/blog/postgres-vs-mongodb/stackoverflow-learning.webp)

As the Stack Overflow survey shows, though Postgres is the most popular database among all respondents, MongoDB is more welcomed by the new learners.

On the other hand, MQL (MongoDB Query Language) is less powerful than SQL in handling complex queries.

## Operability

Running a multi-node MongoDB is easier than running a multi-node Postgres, since sharding, failover are already handled by MongoDB itself. On the other hand, if you run both databases on a single node, then those distributed features in MongoDB become an overhead.

A variety of 3rd party vendors provide Postgres hosting service. MongoDB's license has prevented 3rd party from providing the MongoDB hosting service, and their own MongoDB Altas is more polished than the equivalent Postgres hosting options. After all, it's hard for 3rd party to beat 1st party cloud service.

## Ecosystem

Postgres has an extensible architecture and is still maintained by the community. The Postgres ecosystem is thriving in recent years, it has [a plethora of extensions](/blog/top-postgres-extension/) making it more capable of handling different workloads than other databases. And thanks to its liberal license and solid architecture, for every application platform offering a hosted database service, they all choose Postgres. From the [Heroku](https://www.heroku.com/) in the early days to the new [Supabase](https://supabase.com/), [render](https://render.com/), [Fly.io](https://fly.io/).

MongoDB, as the most successful commercial open source business by market cap, takes a more commercialized approach. Whenever there emerges a good solution in the ecosystem, MongoDB Inc. would consider acquiring it to make sure it becomes an integral part of the overall MongoDB product line. Notable acquisitions include:

- [WiredTiger](https://www.mongodb.com/press/wired-tige) for the storage engine.
- [mLab](https://www.mongodb.com/press/mongodb-strengthens-global-cloud-database-with-acquisition-of-mlab) for the hosting service.
- [Realm](https://www.mongodb.com/press/mongodb-strengthens-mobile-offerings-with-acquisition-of-realm) for the mobile offering.
- [Compass](https://www.mongodb.com/products/compass) for the MongoDB GUI client.

Though Postgres and MongoDB take polarized approach to cultivate the ecosystem, both are widely successful.

## AI

Postgres leverages [pgvector](https://github.com/pgvector/pgvector) extension for vector operations. MongoDB provide built-in [vector search](https://www.mongodb.com/products/platform/atlas-vector-search). MongoDB also [just acquired Voyage AI](https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai) to tackle the hallucination problem of LLM.

![mongodb-voyage](/content/blog/postgres-vs-mongodb/mongodb-voyage.webp)

## Postgres or MongoDB

|              | Postgres                                                        | MongoDB                                                 |
| ------------ | --------------------------------------------------------------- | ------------------------------------------------------- |
| License      | Postgres License (MIT alike)                                    | SSPL                                                    |
| Data Model   | Tabular, relational                                             | Document                                                |
| JSON Support | Capable and integrated with SQL                                 | Built-in schema validator                               |
| Performance  | Optimized for complex query                                     | Optimized for de-normalized data                        |
| Reliability  | Full ACID transaction support                                   | Transaction + built-in auto failover                    |
| Scalability  | Scale-up                                                        | Scale-out                                               |
| Usability    | Rigorous and powerful query capability                          | Relaxed enforcement and easy to start                   |
| Operability  | Optimized for single node and a wide range of hosting providers | Optimized for multi-node and a polished hosting service |
| Ecosystem    | Community driven, decentralized, vibrant                        | Business driven, centralized, integral                  |
| AI           | pgvector extension                                              | Built-in vector search                                  |

There are obvious domains where one database is more suitable than the other:

- Choose Postgres if your application has complex business logic and requires complex queries.
- Choose MongoDB if your application has a simple data model and handles web scale data.

Yet, the majority of application use cases fall between them and both databases are very capable:

- MongoDB as a document database, naturally provides 1st class JSON support. While Postgres also has the best JSON support among all open source relational databases.
- For full-stack developers, MongoDB used to be the go-to choice because of its excellent Node.js integration. While Postgres has been attracting them as every major 3rd party application platform offers managed Postgres service.
- For backend developers, MongoDB becomes appealing ever since it added transaction support. Who doesn't want to avoid [stressful schema change procedure](/blog/how-to-handle-database-schema-change/) and enjoy faster iteration cycle?
- MongoDB Atlas has built one of the most polished hosted database services. While many 3rd party vendors, from large to small, all provide great Postgres hosting solutions.

Overall, Postgres is a more versatile database. It adopts relational model, provides comprehensive SQL capability, carries an extensible architecture, and is driven by an enthusiastic community.

MongoDB is a holistic database solution. It popularizes document model, provides built-in scaling and high availability, offers an integral developer experience, and is driven by a sharp-minded for-profit business entity.

If choosing between [Postgres and MySQL](/blog/postgres-vs-mysql/) is hard, then choosing between Postgres and MongoDB is no easier. And as both databases are heading upward, the choice will only become harder 🤷‍♂️.

---

It's also common that Postgres and MongoDB co-exist inside an organization. And if you want to manage the database development lifecycle for both of them, please check out [Bytebase](/).

![change-query-secure-govern-database-all-in-one](/content/blog/_shared/db-scheme-lg.png)

## References

- [Bye bye Mongo, Hello Postgres](https://www.theguardian.com/info/2018/nov/30/bye-bye-mongo-hello-postgres)
- [Ask HN: Is MongoDB obsolote when Postgres and SQLite provide JSON types?](https://news.ycombinator.com/item?id=30464075)
- [Proposal: Add JSON support for Postgres](https://www.postgresql.org/message-id/e7e5fefd1003281348v6feb1730u7d43ccf011be6976%40mail.gmail.com)
- [Discussion for using BSON or JSONB for Postgres ](https://www.postgresql.org/message-id/CA%2BTgmoagjFfJst%3D9kSu4rZatCE8SRuOQCH_h-_YW%3D4_c687GTA%40mail.gmail.com)
- [Sharding & IDs at Instagram](https://instagram-engineering.com/sharding-ids-at-instagram-1cf5a71e5a5c)
- [Herding elephants: Lessons learned from sharding Postgres at Notion](https://www.notion.so/blog/sharding-postgres-at-notion)

## Other Comparisons

- [Postgres vs. MySQL](/blog/postgres-vs-mysql/)
- [MySQL vs. MariaDB](/blog/mysql-vs-mariadb/)
- [PlanetScale vs. Neon](/blog/planetscale-vs-neon/)
- [Neon vs. Supabase](/blog/neon-vs-supabase/)



This is a series of articles between MySQL and PostgreSQL:

- [MySQL vs. Postgres](/blog/postgres-vs-mysql/)
- PlanetScale vs. Neon (this one)
- [TiDB vs. CockroachDB](/blog/tidb-vs-cockroachdb/)

---

<HintBlock type="info">

On May 14, 2025, Databricks has [announced](https://www.databricks.com/blog/databricks-neon) to acquire Neon.

</HintBlock>

The last 2 years Stack Overflow survey ([2024](https://survey.stackoverflow.co/2024/technology/#1-databases), [2023](https://survey.stackoverflow.co/2023/#most-popular-technologies-database) shows that PostgreSQL has taken over the first place spot from MySQL and become the most admired, desired database.

![stackoverflow](/content/blog/planetscale-vs-neon/stackoverflow.webp)

It seems that the love-hate relationship between MySQL and PostgreSQL will never end. From vanilla MySQL vs. PostgreSQL, to distributed TiDB vs. CockroachDB, to cloud-native AWS Aurora vs. GCP AlloyDB, and now we are entering the next chapter, the serverless, developer-oriented PlanetScale vs. Neon.

![planetscale](/content/blog/planetscale-vs-neon/planetscale.webp)

![neon](/content/blog/planetscale-vs-neon/neon.webp)

At Bytebase, we work with the MySQL / PostgreSQL ecosystems extensively. Our founders also build [Google Cloud SQL](https://cloud.google.com/sql), one of the largest hosted MySQL & Postgres cloud services.

Below we give an extensive comparison between PlanetScale and Neon from the following dimensions:

- [Architecture](#architecture)
- [Compatibility](#compatibility)
- [Developer Workflow](#developer-workflow)
- [Reliability](#reliability)
- [Scalability](#scalability)
- [Operability](#operability)
- [Integration](#integration)
- [Compliance](#compliance)
- [Open Source](#open-source)
- [Pricing](#pricing)
- [Funding](#funding)

## Architecture

PlanetScale is a distributed database based on [Vitess](https://vitess.io/). Vitess is a shared-nothing architecture where each shard contains a MySQL primary node and set of replicas. VTGate proxy routes the application request to the respective shard.

![vitess-arch](/content/blog/planetscale-vs-neon/vitess-arch.webp)

Neon is a shared-storage architecture. It separates the compute and storage. The compute part is just normal PostgreSQL server, the storage part is a custom-built multi-tenant storage system shared by all Postgres compute nodes.

![neon-arch](/content/blog/planetscale-vs-neon/neon-arch.webp)

## Compatibility

**[PlanetScale's MySQL compatibility](https://planetscale.com/docs/reference/mysql-compatibility) is constrained.**

1. [Underlying Vitess limitations](https://vitess.io/docs/15.0/reference/compatibility/mysql-compatibility/). Vitess shared-nothing architecture carries inherent compatibility limitations. Features requiring session maintenance or cross-shard coordination are challenging to implement.

1. Product trade-off. e.g. To support online DDL, PlanetScale disallows foreign keys entirely, which is more strict than Vitess' FK limitation.

1. Cloud operating model. No super privilege or LOAD DATA INFILE to access the host file system.

**Neon is [mostly compatible](https://neon.tech/docs/reference/compatibility) with vanilla PostgreSQL.**

Neon rebuilds the paging layer and just lightly changes the Postgres codebase. Neon's compatibility is only constrained by its cloud operating model, which are similar to other hosted database services (no superuser or host file system access).

## Developer Workflow

Both PlanetScale and Neon are targetting developers, and they are telling the same developer workflow story in different ways.

PlanetScale's story emphasizes the end-to-end experience, from branching, to schema migration, monitoring and revert.

![planetscale-workflow](/content/blog/planetscale-vs-neon/planetscale-workflow.webp)

Neon's story highlights the single branching feature. Their purpose-built paging layer has copy-on-write (CoW), which makes branching instantaneous and cost-effective.

![neon-branch](/content/blog/planetscale-vs-neon/neon-branch.webp)

## Reliability

Shared-nothing architecture is inherently fault-tolerant as data is sharded and replicated. Vitess is a proven technology used by logos across the globe. PlanetScale has also proved its reliability for not having notable outages for years.

![vitess-user](/content/blog/planetscale-vs-neon/vitess-user.webp)

Shared-storage architecture requires more engineering effort to make the logical SPOF storage component fault tolerant. Neon details the work in [its architecture decisions](https://neon.tech/blog/architecture-decisions-in-neon).

As a database trailblazing new architecture, Neon's advantage is it sits on a solid foundation. PostgreSQL itself is rock solid and fully transactional. Neon's Pageserver approach also aligns perfectly with the WAL-based logging in PostgreSQL. Neon is also able to use Rust, a more suitable system language to implement its storage layer.

## Scalability

PlanetScale, as its name suggests, is planet-scale. The shared-nothing architecture grants near-linear scalability [up to 1 million QPS](https://planetscale.com/blog/one-million-queries-per-second-with-mysql). The underlying Vitess is originally developed inside YouTube to handle scalability challenges and it's been battle-tested in other large internet companies ([1](https://slack.engineering/scaling-datastores-at-slack-with-vitess/), [2](https://www.cncf.io/case-studies/jdcom-vitess/)).

Neon can not scale as much as PlanetScale. After all, it's a single-node PostgreSQL instance. But this single node can scale up quite well. Neon separates the storage and compute, thus each can scale individually. And in the cloud, storage is infinite, compute is abundant, only the network bandwidth is constrained. The separated architecture also infers elasticity, scale-to-zero is as easy as scale-up.

## Operability

**PlanetScale provides a complete managed database solution. It tries to take care of [every aspect of using the database](https://planetscale.com/blog/all-the-tech-planetscale-replaces).**

![planetscale-tech](/content/blog/planetscale-vs-neon/planetscale-tech.webp)

- **Deploy requests** and **Branches** for database change workflow.
- **Insights** for monitoring.
- **Boost** for speeding up query performance.
- **Revert** for reverting the change.
- **Console** for mysql CLI experience in the browser.
- **Backup** for disaster recovery.

It's quite a lot to cover and PlanetScale manages to craft every pixel. e.g. [Boost](https://planetscale.com/features/boost) shows PlanetScale's strength to transform an [academic paper](https://jon.thesquareplanet.com/papers/phd-thesis.pdf) into a polished product.

![planetscale-boost](/content/blog/planetscale-vs-neon/planetscale-boost.webp)

**Neon is relatively new and provides essentials.**

- Disaster recovery is provided by its branching feature.
- A visual SQL Editor to interact with the database.

![neon-sql-editor](/content/blog/planetscale-vs-neon/neon-sql-editor.webp)

The SQL Editor is handy. Here Neon embeds an external view from [explain.dalibo.com](https://explain.dalibo.com/) to display the query plan. It looks a bit strange visually, but serves the purpose well.

## Integration

PlanetScale itself is already a comprehensive database platform. Its [doc site](https://planetscale.com/docs) also lists a handful of standard database integrations:

- Application frameworks from major languages.

- Monitoring with Datadog.

- Data transfer via Airbyte, Stitch, Hightouch.

Neon hasn't accumulated many standard database integrations yet. But Neon's unique capabilities unlock new integration frontiers:

- [Snaplet](https://docs.snaplet.dev/tutorials/neon/) uses Neon to instantaneously prepare production database snapshots for the testing environment.

- [Replit](https://neon.tech/blog/neon-replit-integration) leverages Neon's scale-to-zero to offer its users a minimum-cost database service.

- [Vercel](https://neon.tech/blog/neon-vercel-integration) pairs with Neon's database branching to provide a realistic preview deployment in minutes instead of hours.

## Compliance

PlanetScale has [SOC2 Type 2 and HIPAA](https://planetscale.com/docs/concepts/security). Neon has completed [SOC2 Type 2](https://neon.tech/docs/security/security-overview).

## Open Source

PlanetScale and Neon choose an identical strategy, open soucing their database codebase under the same liberal license and only charging the cloud service.

[![Star History](/content/blog/planetscale-vs-neon/star-history.webp)](https://star-history.com/#planetscale/beam&vitessio/vitess&neondatabase/neon&Date)

PlanetScale is based on a [Vitess fork](https://github.com/planetscale/vitess) under Apache-2.0. The team once built [gh-ost](https://github.com/github/gh-ost), the best online schema migration tool for MySQL and probably among all open source RDBMS. They also open sourced [beam](https://github.com/planetscale/beam), a message board web app to showcase PlanetScale.

Neon also [open sources](https://github.com/neondatabase/neon) its entire database codebase under Apache-2.0 license.

## Pricing

PlanetScale employs a usage-based pricing on row reads/writes. This is atypical for a database service and raises [controversy](https://news.ycombinator.com/item?id=30457960). On one hand, it brings extra incentive for people to optimize queries, on the other hand, bad queries are inevitable and MySQL query optimizer is also [flawed](https://news.ycombinator.com/item?id=29455852). To offset this concern, PlanetScale recently introduced a new [Scaler Pro tier](https://planetscale.com/blog/announcing-scaler-pro) for unlimited row reads/writes.

![planetscale-pricing](/content/blog/planetscale-vs-neon/planetscale-pricing.webp)

Neon offers a usage-based pricing based on 4 metrics: **active compute time**, **data storage**, **data transfer out** and **data written**. This pricing model is more traditional and predictable. Neon also provides a pricing calculator to estimate the cost.

![neon-pricing](/content/blog/planetscale-vs-neon/neon-pricing.webp)

## Funding

Both are well-funded companies led by industry veterans. PlanetScale has raised $105M so far, while Neon nabs $103M (including a very recent [$46M series B](https://neon.tech/blog/series-b-funding)).

## PlanetScale or Neon

|  | PlanetScale | Neon |
| --- | --- | --- |
| Architecture | Shared-nothing, sharded compute and storage unit | Shared-storage, separated compute and storage |
| Compatibility | Limited by the underlying shared-nothing architecture | Near-compatible |
| Developer Workflow | End-to-end experience | Instantaneous and cost-effective branching |
| Reilability | Proven technology with track record | New architecture on a solid foundation |
| Scalability | Scale out to near infinite | Scale up to an extent, scale-to-zero elasticity |
| Operability | Comprehensive, polished tooling | Essential, pragmatic tooling |
| Integration | Typical database integrations | Novel integrations enabled by instant branching and elasticity |
| Compliance | SOC 2 Type 2, HIPAA | SOC2 Type 1 |
| Open Source | Close-sourced paid cloud service; Open-sourced database under Apache-2.0 | Close-sourced paid cloud service; Open-sourced database under Apache-2.0 |
| Pricing | Row reads/writes + storage + HA | Computation + storage + data transfer |
| Funding | $105M | $103M |

Overall, PlanetScale provides a cohesive experience for every database task. Originally, it was a hosted MySQL-compatible Vitess service only for hyperscalers. Since [pivoting to developers](https://planetscale.com/blog/announcing-planetscale-the-database-for-developers), PlanetScale has transformed into an advanced serverless database platform that happens to speak the MySQL dialect.

Neon debuted later, and has a very similar value proposition as the new PlanetScale. Unlike PlanetScale, Neon knows its targeting audiences from day 1, and purposefully builds the technology catering to them:

- Git-like branch management, in particular, the instant branching including both schema and data.
- Serverless database with autoscaling up and down.
- Embrace the rise of Postgres and maintain near-perfect Postgres compatibility.

## Beyond PlanetScale and Neon

Today, when choosing the database system for your next project, the very first thing to consider is no longer between [Postgres vs. MySQL](/blog/postgres-vs-mysql/). Instead, it's whether to choose RDBMS or other genres. RDBMS still dominates the database market by shares, however, ironically, this space doesn't advance as much as other database segments in the cloud era. Customers do have [a plethora of choices](/blog/postgres-hosting-options-pricing-comparison/), but none is coming close to MongoDB Altas for NoSQL or Snowflake for OLAP.

PlanetScale and Neon are similar to each other, they are also similar to MongoDB / Snowflake:

- Like MongoDB, PlanetScale uses shared-nothing architecture, provides a comprehensive database platform, is developer obsessed, and tells great product stories.

- Like Snowflake, Neon brings the novel shared-storage approach to the staggering OLTP architecture, uses Postgres dialect, and also values developer experience.

Both PlanetScale and Neon could become the next MongoDB / Snowflake for modern RDBMS database-as-a-service (DBaaS). We've all been waiting too long for this.

_A side note: PlanetScale has shifted its website art direction to a simplistic style, marking another drastic difference from Neon._

![website-planetscale](/content/blog/planetscale-vs-neon/website-planetscale.webp) ![website-neon](/content/blog/planetscale-vs-neon/website-neon.webp)

---

BTW, if you still stick with vanilla MySQL/Postgres and want PlanetScale's database change workflow or Neon's visual SQL Editor, please check out [Bytebase](/). Bytebase is a database tool for all mainstream databases, covering database change, query, security and governance all-in-one. It provides more customizable [change workflow](https://docs.bytebase.com/change-database/change-workflow/) and visual [SQL Editor](https://docs.bytebase.com/sql-editor/overview/) integrated with [access control](https://docs.bytebase.com/security/database-permission/overview/) and [data masking](https://docs.bytebase.com/security/data-masking/overview/).

![change-query-secure-govern-database-all-in-one](/content/blog/_shared/db-scheme-lg.png)

## References

- [Announcing PlanetScale: The database for developers](https://planetscale.com/blog/announcing-planetscale-the-database-for-developers)
- [All of the tech PlanetScale replaces](https://planetscale.com/blog/all-the-tech-planetscale-replaces)
- [One million queries per second with MySQL](https://planetscale.com/blog/one-million-queries-per-second-with-mysql)
- [Neon – Serverless Postgres](https://news.ycombinator.com/item?id=31536827)
- [Architecture decisions in Neon](https://neon.tech/blog/architecture-decisions-in-neon)

## Other Comparisons

- [Postgres vs. MySQL](/blog/postgres-vs-mysql/)
- [MySQL vs. MariaDB](/blog/mysql-vs-mariadb/)
- [Postgres vs. MongoDB](/blog/postgres-vs-mongodb/)
- [Neon vs. Supabase](/blog/neon-vs-supabase/)



> If Flyway is Git, then Bytebase is GitHub/GitLab.

Flyway and Bytebase both handle database schema migration, but they solve different problems. Flyway is a migration engine — you write SQL scripts, run `flyway migrate`, and it tracks what's been applied. Bytebase is an all-in-one database governance platform — migration is one part, alongside SQL review, approval workflows, access control, data masking, and audit logging. If your team needs a CLI to slot into existing CI/CD pipelines, Flyway does that well. If you need to control who can change what, enforce review before deployment, and keep an audit trail — that's the problem Bytebase solves.

## What They Have in Common

- Native SQL support for defining schema changes.
- GitOps integration — trigger migrations from pull requests.
- Schema synchronization and diff capabilities.
- Change history tracking.
- Tiered pricing with a free community edition.
- Both are open source — Flyway Community is Apache 2.0 (Redgate acquired Flyway in 2019). Bytebase is MIT licensed, with enterprise features under a commercial license.

![Star history of Bytebase and Flyway](/content/blog/bytebase-vs-flyway/star-history.webp)

## Key Differences Between Bytebase and Flyway

|                                | Flyway | Bytebase |
| ------------------------------ | ------ | -------- |
| [Developer interface](#developer-interface) | CLI + Flyway Desktop (GUI) | Web GUI + API + Terraform provider |
| [Supported databases](#supported-databases) | 30+ | 23 |
| [Installation](#installation) | Java 17 + JVM | Single Go binary, Docker, or K8s |
| [Change execution](#change-execution) | SQL scripts + CLI | Issue-based GUI or GitOps |
| [Schema sync](#schema-sync) | Flyway Desktop (1-to-1 comparison) | GUI-based batch sync (1-to-many) |
| [Batch change](#batch-change) | Manual scripting per target | Built-in multi-env / multi-tenant |
| [SQL review / code analysis](#sql-review-and-code-analysis) | Enterprise only: 74+ rules (SQLFluff + Redgate rules) | 200+ rules (all tiers) |
| [Approval flow](#approval-flow) | — | All tiers: manual rollout; Enterprise: custom approval |
| [Rollback](#rollback) | Enterprise only: manual + auto undo scripts | Auto-generated rollback + sync to any version |
| [CI/CD integration](#cicd-integration) | GitHub Actions, GitLab, Jenkins, Azure DevOps, Octopus Deploy, TeamCity | GitOps (GitHub, GitLab, Bitbucket, Azure DevOps) + API |
| [Change history](#change-history) | flyway_schema_history table | GUI with diff view + issue linkage |
| [Database access control & audit](#database-access-control-and-audit) | — | All tiers: workspace/project roles; Pro: SSO, audit log; Enterprise: + dynamic data masking, custom roles, just-in-time data access |
| License | Apache 2.0 | MIT + commercial (Enterprise features) |

### Pricing Comparison

|  | Free | Paid |
| --- | --- | --- |
| **Flyway** | Community (Apache 2.0, CLI + Desktop, 30+ DBs) | Enterprise: contact for pricing (AI features, code review policies, state-based deployment, undo scripts, change reports, drift detection) |
| **Bytebase** | Community (self-hosted, up to 20 users, 10 instances) | Pro: $20/user/mo (cloud-only, up to 10 instances); Enterprise: custom yearly (self-hosted or cloud) |

### Developer Interface

**Flyway** offers two interfaces. The CLI is the primary tool — write SQL migration scripts, run `flyway migrate`, and it handles versioning and execution. **Flyway Desktop** (added after the Redgate acquisition) is a GUI application with a schema model view, built-in Git client, and visual diff tools. Desktop supports SQL Server, PostgreSQL, and MySQL.

**Bytebase** provides a web-based GUI where developers submit changes, DBAs review them, and the platform handles rollout. It also exposes a full [API](https://api.bytebase.com/), a [Terraform provider](https://docs.bytebase.com/integrations/terraform/overview) for infrastructure-as-code workflows, and [GitOps workflow tutorials](https://docs.bytebase.com/tutorials/gitops-github-workflow) for Git-driven automation.

### Supported Databases

**Flyway** supports 30+ databases including PostgreSQL, MySQL, Oracle, SQL Server, MariaDB, Snowflake, BigQuery, Redshift, CockroachDB, ClickHouse, MongoDB, DuckDB, and many more. Database support is modular — some engines are community-contributed extensions.

**Bytebase** supports 25 database engines with deep integration: 9 RDBMS (MySQL, PostgreSQL, Oracle, SQL Server, MariaDB, TiDB, OceanBase, CockroachDB, Spanner), 6 NoSQL (MongoDB, Redis, Cassandra, DocumentDB, DynamoDB, Cosmos DB), 9 data warehouses (Snowflake, BigQuery, Redshift, Hive, ClickHouse, Databricks, StarRocks, Doris, Trino), and Elasticsearch.

Flyway covers more databases. Bytebase goes deeper on each one it supports — online schema change for MySQL, engine-specific SQL review rules for PostgreSQL, column-level data masking that understands each database's type system.

### Installation

**Flyway** requires Java 17+ (JVM). Install the JVM, then install Flyway. Flyway Desktop is a separate installer for the GUI.

**Bytebase** ships as a single Go binary with no external dependencies. Deploy via [Docker](https://docs.bytebase.com/get-started/deploy-with-docker) or [Kubernetes](https://docs.bytebase.com/get-started/deploy-with-kubernetes) in under 5 minutes.

### Change Execution

**Flyway** uses versioned SQL migration scripts. Name them `V1__create_users.sql`, `V2__add_email_column.sql`, and run `flyway migrate`. Flyway tracks which versions have been applied in a metadata table and runs only the new ones. You can also use repeatable migrations (prefix `R__`) for views and stored procedures that get reapplied when their checksum changes.

**Bytebase** uses an issue-based workflow. A developer creates an issue containing SQL statements, which goes through review and approval before execution. Issues can target a single database or batch across environments. Bytebase also supports online schema change for MySQL — large table migrations that would normally lock the table for hours complete in seconds with zero downtime.

![Bytebase issue-based change workflow](/content/blog/bytebase-vs-flyway/bytebase-issue.webp)

### Schema Sync

**Flyway Desktop** provides visual schema comparison between a development database and a schema model. It generates migration scripts from the diff. This is a 1-to-1 comparison — you compare one source against one target.

**Bytebase** has a built-in [schema sync](https://docs.bytebase.com/change-database/synchronize-schema) through the GUI. Pick a source database and one or more targets — Bytebase generates the DDL diff for each and applies them as a batch change issue, with SQL review, approval, and audit logging included. Useful for keeping staging in sync with prod, or syncing schema across dozens of tenant databases at once.

### Batch Change

**Flyway** doesn't have built-in multi-environment orchestration. You run `flyway migrate` against each target database separately in your CI/CD pipeline. You can script this, but it's you writing the orchestration.

**Bytebase** handles this natively. A single issue can roll out changes across [multiple environments](https://docs.bytebase.com/change-database/batch-change#change-databases-from-multiple-environments) (dev → staging → prod) or [multiple tenants](https://docs.bytebase.com/change-database/batch-change#change-databases-from-multiple-tenants).

### SQL Review and Code Analysis

**Flyway Community** has no built-in SQL quality checks. **Flyway Enterprise** includes code analysis with 74+ rules — 60+ SQLFluff rules (open-source linter) plus 14+ Redgate-specific rules for code quality and security. Enterprise also includes a policy library and custom policy integration.

**Bytebase** includes [SQL Review](https://docs.bytebase.com/sql-review/review-rules) with 200+ rules across MySQL, PostgreSQL, Oracle, SQL Server, and more — available in the free tier. Rules are database-engine-specific, and you can configure error levels per environment (warn in dev, block in prod).

SQL review triggers automatically in two places:

1. When a change issue is created.
2. In GitOps — when a new PR containing SQL is created.

### Approval Flow

**Flyway** has no built-in approval workflow. Approval happens outside the tool — in your CI/CD pipeline, Jira tickets, or Slack messages.

**Bytebase** Community and Pro include manual rollout — someone explicitly clicks "Deploy" to apply a change. **Bytebase Enterprise** adds [custom approval flows](https://docs.bytebase.com/change-database/approval). You define rules like "DDL on prod needs DBA approval" or "changes touching 3+ databases need manager sign-off," and the system routes each issue to the right reviewers.

### Rollback

**Flyway Community** does not support rollback. **Flyway Enterprise** adds undo migrations — you write `U1__undo_create_users.sql` or use auto-generated undo scripts for common operations.

**Bytebase** auto-generates rollback statements for DML changes and supports reverting to any previous schema version via [schema sync](https://docs.bytebase.com/change-database/rollback-data-changes). No manual rollback scripts needed.

### CI/CD Integration

**Flyway** fits into any CI/CD pipeline as a CLI step — GitHub Actions, GitLab CI, Jenkins, Azure DevOps, Octopus Deploy, Harness, TeamCity. It's a command-line tool, so it works anywhere you can run a shell command.

**Bytebase** offers [GitOps setup](https://docs.bytebase.com/gitops/overview) with GitHub, GitLab, Bitbucket, and Azure DevOps. SQL files committed to a repo automatically create change issues in Bytebase, with SQL review running as a merge check. For other platforms, Bytebase's [API](https://api.bytebase.com/) lets you wire up any CI/CD pipeline.

### Change History

**Flyway** tracks changes in the `flyway_schema_history` table — a record of which migrations were applied, when, and their checksums. You query it directly with SQL or run `flyway info` to see a summary.

**Bytebase** provides a visual change history with schema diffs and links back to the originating issue, reviewer, and approval chain.

### Database Access Control and Audit

**Flyway** doesn't cover this area. It's a migration tool — access control, data masking, and audit logging are outside its scope.

**Bytebase** layers access control across its tiers:

- **Community** — [workspace and project roles](https://docs.bytebase.com/administration/roles) (Owner, DBA, Developer) with built-in permissions, plus [SQL Editor](https://docs.bytebase.com/sql-editor/overview) for controlled query access.
- **Pro** — adds SSO, [audit log](https://docs.bytebase.com/security/audit-log), and user groups.
- **Enterprise** — [custom roles](https://docs.bytebase.com/administration/roles) with granular permissions, [dynamic data masking](https://docs.bytebase.com/security/data-masking/overview) at column level, enterprise SSO (OIDC, LDAP), 2FA, and just-in-time data access — developers request temporary query access, a reviewer approves it, and access expires automatically.

## When to Choose Flyway

- Your team is CLI-first and wants to embed migrations directly into existing CI/CD pipelines.
- You need to support a wide range of databases (30+), including niche or legacy systems.
- Your developers already work in Java/JVM ecosystems.
- You want Flyway Desktop's visual schema comparison for SQL Server development.
- You need a migration engine, not a governance platform — access control and approval happen in other tools.

## When to Choose Bytebase

- You want an all-in-one platform that covers migration, SQL review, access control, data masking, and audit logging.
- Your team includes DBAs or platform engineers who review database changes before they reach production.
- You need compliance — approval flows, audit trails, and data masking for SOC 2, GDPR, or internal security policies.
- You're running multi-environment or multi-tenant deployments where batch change orchestration matters.

## FAQ

### Is Flyway still open source?

Flyway Community remains Apache 2.0 licensed. Redgate acquired Flyway in 2019 and maintains both the open-source edition and commercial Flyway Enterprise with additional features (AI-powered summaries, auto undo scripts, policy library, state-based deployment).

### Can I use Flyway and Bytebase together?

They solve different layers. Flyway handles the migration file format and execution; Bytebase handles collaboration, review, and governance. If you already have a library of Flyway migration scripts, you don't have to throw them away — Bytebase can manage the review and deployment side while Flyway stays as the execution engine.

### What is Flyway Desktop?

Flyway Desktop is Redgate's GUI application for database development. It provides a visual schema model, built-in Git client, and schema comparison tools. It currently supports SQL Server, PostgreSQL, and MySQL. It's included in both Community and Enterprise editions.

### Which tool has better CI/CD integration?

Depends on what "CI/CD integration" means to you. If it means "I want a command I can add to my Jenkins/GitHub Actions pipeline," Flyway wins — it's a CLI call that works anywhere. If it means "I want SQL files in a repo to automatically become reviewed, approved change issues," Bytebase's GitOps integration does that out of the box.

### How does Flyway pricing compare to Bytebase?

Flyway Community is free. Flyway Enterprise requires contacting Redgate for a quote — no public pricing. Bytebase Community is free (up to 20 users), Pro is $20/user/month, and Enterprise is custom pricing. [Bytebase pricing details](https://www.bytebase.com/pricing/).

---

Related comparisons:

- [Bytebase vs. Atlas](/blog/bytebase-vs-atlas/)
- [Bytebase vs. Liquibase](/blog/bytebase-vs-liquibase/)
- [Flyway vs. Liquibase](/blog/flyway-vs-liquibase/)
- [Top Database Schema Change Tools](/blog/top-database-schema-change-tool-evolution/)



> If Liquibase is Git, then Bytebase is GitHub/GitLab.

Bytebase and Liquibase both handle database schema migration, but the right choice depends on how your team ships database changes — not just which databases you run. A solo developer writing changelogs in a terminal has different needs than a platform team where DBAs review every production migration.

## How They Approach Schema Migration

**Liquibase** (v5.0, September 2025) is a CLI-first migration engine. You define changes in XML, YAML, SQL, or JSON changelogs, then run `liquibase update` to apply them. It slots into any CI/CD pipeline as a command-line step. The commercial edition — rebranded from "Liquibase Pro" to **Liquibase Secure** in September 2025 — adds policy checks, flow files for workflow automation, and structured JSON logging.

**Bytebase** (v3.16, March 2026) is a web-based platform where developers submit changes, DBAs review and approve them, and the system rolls out across environments. Schema migration is one part of it — SQL review, access control, data masking, and audit logging are built in.

Put another way: **Liquibase is Git for databases; Bytebase is GitHub/GitLab for databases.**

## What They Have in Common

- Native SQL support for defining schema changes.
- GitOps integration — trigger migrations from pull requests.
- Schema synchronization and diff capabilities.
- Change history tracking.
- Tiered pricing with a free community edition.
- Both are open source — though Liquibase switched from Apache 2.0 to the Functional Source License (FSL) in September 2025 with v5.0. Bytebase is MIT licensed, with enterprise features under a commercial license.

![Star history of Bytebase and Liquibase](/content/blog/bytebase-vs-liquibase/star-history.webp)

## Key Differences Between Bytebase and Liquibase

|                                | Liquibase | Bytebase |
| ------------------------------ | --------- | -------- |
| [Developer interface](#developer-interface) | CLI; Secure adds VS Code extension | Web GUI + API + Terraform provider |
| [Supported databases](#supported-databases) | 60+ | 23 |
| [Installation](#installation) | Java 17 + JVM | Single Go binary, Docker, or K8s |
| [Change execution](#change-execution) | Changelog + CLI; Secure adds Flow files | Issue-based GUI or GitOps |
| [Schema sync](#schema-sync) | `liquibase diff` + `diff-changelog` (CLI, 1-to-1) | GUI-based batch sync (1-to-many) |
| [Batch change](#batch-change) | Manual scripting; Secure adds Flow files | Built-in multi-env / multi-tenant |
| [SQL review / policy checks](#sql-review-and-policy-checks) | Secure only: policy checks (custom rules) | 200+ rules (all tiers) |
| [Approval flow](#approval-flow) | — | All tiers: manual rollout; Enterprise: custom approval |
| [Rollback](#rollback) | Basic (manual scripts); Secure: granular + automated | Auto-generated rollback + sync to any version |
| [CI/CD integration](#cicd-integration) | Any CLI-based pipeline; Secure adds Flow files | GitOps (GitHub, GitLab, Bitbucket, Azure DevOps) + API |
| [Change history](#change-history) | DATABASECHANGELOG table; Secure adds structured JSON logging | GUI with diff view + issue linkage |
| [Database access control & audit](#database-access-control-and-audit) | — | All tiers: workspace/project roles; Pro: SSO, audit log; Enterprise: + dynamic data masking, custom roles, just-in-time data access |
| [Pricing](#pricing) | Community: free (FSL); Secure: quote-based (5 tiers) | Community: free (MIT); Pro: $20/user/mo; Enterprise: custom |

### Developer Interface

**Liquibase** is CLI-only for the Community edition. After Liquibase Hub was sunset in April 2023, there is no web dashboard. The Secure edition added a VS Code extension ("Liquibase Secure Developer") for IDE-based interaction, but the primary workflow remains the terminal.

**Bytebase** provides a web-based GUI where developers submit changes, DBAs review them, and the platform handles rollout. It also exposes a full [API](https://api.bytebase.com/), a [Terraform provider](https://docs.bytebase.com/integrations/terraform/overview) for infrastructure-as-code workflows, and [GitOps workflow tutorials](https://docs.bytebase.com/tutorials/gitops-github-workflow) for Git-driven automation.

### Supported Databases

**Liquibase** supports 60+ databases through a modular extension system. Core databases include PostgreSQL, MySQL, Oracle, SQL Server, MariaDB, Snowflake, MongoDB, and DB2. Many database extensions are community-contributed.

**Bytebase** supports 25 database engines with deep integration: 9 RDBMS (MySQL, PostgreSQL, Oracle, SQL Server, MariaDB, TiDB, OceanBase, CockroachDB, Spanner), 6 NoSQL (MongoDB, Redis, Cassandra, DocumentDB, DynamoDB, Cosmos DB), 9 data warehouses (Snowflake, BigQuery, Redshift, Hive, ClickHouse, Databricks, StarRocks, Doris, Trino), and Elasticsearch.

Liquibase covers more databases. Bytebase goes deeper on each one it supports — online schema change for MySQL, engine-specific SQL review rules for PostgreSQL, column-level data masking that understands each database's type system.

### Installation

**Liquibase** requires Java 17+ (as of v5.0). Install the JVM, then install Liquibase. The Secure edition includes a one-step certified installer with bundled database drivers.

**Bytebase** ships as a single Go binary with no external dependencies. Deploy via [Docker](https://docs.bytebase.com/get-started/deploy-with-docker) or [Kubernetes](https://docs.bytebase.com/get-started/deploy-with-kubernetes) in under 5 minutes.

### Change Execution

**Liquibase** uses changelogs — files in XML, YAML, SQL, or JSON that define database changes. You run `liquibase update` to apply them. Changelogs support preconditions (skip a changeset if the table already exists), contexts (apply only in certain environments), and labels (tag changesets for selective execution). The Secure edition adds Flow files: reusable YAML-based pipelines that chain multiple Liquibase commands with conditional logic — for example, run `validate` first, then `update`, and only run `rollback` if `update` fails.

![Liquibase changelog definition](/content/blog/bytebase-vs-liquibase/liquibase-changelog.webp)

**Bytebase** uses an issue-based workflow. A developer creates an issue containing SQL statements, which goes through review and approval before execution. Issues can target a single database or batch across environments. Bytebase also supports online schema change for MySQL — large table migrations that would normally lock the table for hours complete in seconds with zero downtime.

![Bytebase issue-based change workflow](/content/blog/bytebase-vs-liquibase/bytebase-issue.webp)

### Schema Sync

**Liquibase** offers `liquibase diff` to compare two databases and `liquibase diff-changelog` to generate a changelog from the differences. Both are CLI commands — you specify a reference database and a target, and Liquibase outputs the structural differences or the changesets needed to bring the target in line.

**Bytebase** has a built-in [schema sync](https://docs.bytebase.com/change-database/synchronize-schema) through the GUI. Pick a source database and one or more targets — Bytebase generates the DDL diff for each and applies them as a batch change issue, with SQL review, approval, and audit logging included. Useful for keeping staging in sync with prod, or syncing schema across dozens of tenant databases at once.

### Batch Change

**Liquibase** doesn't have built-in multi-environment orchestration. You run `liquibase update` against each target database separately in your CI/CD pipeline. The Secure edition's Flow files help — you can define a YAML pipeline that runs the same changelog against dev, staging, and prod in sequence, with gates between stages. But it's still you writing the orchestration, not the tool managing it.

**Bytebase** handles this natively. A single issue can roll out changes across [multiple environments](https://docs.bytebase.com/change-database/batch-change#change-databases-from-multiple-environments) (dev → staging → prod) or [multiple tenants](https://docs.bytebase.com/change-database/batch-change#change-databases-from-multiple-tenants).

### SQL Review and Policy Checks

**Liquibase Community** has no built-in SQL quality checks. **Liquibase Secure** offers Policy Checks (renamed from "Quality Checks" in September 2024) with custom rules and conditional logic chains (AND/OR/NOT). You can write checks like "block any changeset that drops a table in production" or "require a rollback section for every changeset." These operate on changelog structure and SQL patterns — useful for governance, though less granular than engine-specific SQL analysis.

![Liquibase policy check configuration](/content/blog/bytebase-vs-liquibase/liquibase-quality-check.webp)

**Bytebase** includes [SQL Review](https://docs.bytebase.com/sql-review/review-rules) with 200+ rules across MySQL, PostgreSQL, Oracle, SQL Server, and more — available in the free tier. Rules are database-engine-specific (not generic), and you can configure error levels per environment (warn in dev, block in prod).

![Bytebase SQL review rules configuration](/content/blog/bytebase-vs-liquibase/bytebase-sql-review-rules.webp)

SQL review triggers automatically in two places:

1. When a change issue is created.
2. In GitOps — when a new PR containing SQL is created.

### Approval Flow

**Liquibase** has no built-in approval workflow in either edition. Approval happens outside the tool — in your CI/CD pipeline, Jira tickets, or Slack messages.

**Bytebase** Community and Pro include manual rollout — someone explicitly clicks "Deploy" to apply a change. **Bytebase Enterprise** adds [custom approval flows](https://docs.bytebase.com/change-database/approval). You define rules like "DDL on prod needs DBA approval" or "changes touching 3+ databases need manager sign-off," and the system routes each issue to the right reviewers based on what it touches.

### Rollback

**Liquibase Community** supports basic rollback — you write rollback SQL manually in your changelog and run `liquibase rollback` to a specific tag or date. **Liquibase Secure** improves this with auto-generated rollback scripts for common operations, rollback reports (added in v4.27.0) that show what was rolled back, skipped, or failed, and custom rollback logic for complex cases.

**Bytebase** auto-generates rollback statements for DML changes and supports reverting to any previous schema version via [schema sync](https://docs.bytebase.com/change-database/rollback-data-changes). No manual rollback scripts needed.

### CI/CD Integration

**Liquibase** fits into any CI/CD pipeline as a CLI step — GitHub Actions, GitLab CI, Jenkins, CircleCI. The Secure edition's Flow files add reusable pipeline definitions with conditional branching based on exit codes.

**Bytebase** offers [GitOps setup](https://docs.bytebase.com/gitops/overview) with GitHub, GitLab, Bitbucket, and Azure DevOps. SQL files committed to a repo automatically create change issues in Bytebase, with SQL review running as a merge check. For other platforms, Bytebase's [API](https://api.bytebase.com/) lets you wire up any CI/CD pipeline.

![Bytebase GitOps SQL review in GitLab merge request](/content/blog/bytebase-vs-liquibase/bytebase-gitops-merge.webp)

### Change History

**Liquibase** tracks changes in the `DATABASECHANGELOG` table — a record of which changesets were applied, when, and by whom. You query it directly with SQL. The Secure edition adds structured JSON logging (replacing the old text-based logs) that you can pipe into Datadog, Splunk, or any log aggregator. This is how Liquibase replaced the Hub dashboard — instead of a built-in UI, you build observability from the log stream.

**Bytebase** provides a visual change history with schema diffs and links back to the originating issue, reviewer, and approval chain.

![Bytebase change history with diff view](/content/blog/bytebase-vs-liquibase/bytebase-change-diff.webp)

### Database Access Control and Audit

**Liquibase** doesn't cover this area. Neither edition has access control, data masking, or audit logging — the Secure edition's structured logs tell you what changed and when, but not who queried what data. If you need database access governance, you'd pair Liquibase with a separate tool.

**Bytebase** layers access control across its tiers:

- **Community** — [workspace and project roles](https://docs.bytebase.com/administration/roles) (Owner, DBA, Developer) with built-in permissions, plus [SQL Editor](https://docs.bytebase.com/sql-editor/overview) for controlled query access.
- **Pro** — adds SSO, [audit log](https://docs.bytebase.com/security/audit-log), and user groups.
- **Enterprise** — [custom roles](https://docs.bytebase.com/administration/roles) with granular permissions, [dynamic data masking](https://docs.bytebase.com/security/data-masking/overview) at column level, enterprise SSO (OIDC, LDAP), 2FA, and Just-In-Time data access — developers request temporary query access, a reviewer approves it, and access expires automatically.

![Bytebase SQL Editor with controlled data access](/content/blog/bytebase-vs-liquibase/bytebase-sql-editor.webp)

### Pricing

**Liquibase** restructured its pricing in September 2025 into five tiers: Community (free), Starter, Growth, Business, and Enterprise. All paid tiers are branded "Liquibase Secure." Paid plans are gated by number of applications, database types, and company revenue (Starter and Growth require under $1B revenue). No public pricing — quotes only.

**Bytebase** has three tiers: Community (free, self-hosted, up to 20 users, 10 instances), Pro ($20/user/month, cloud-only, up to 10 instances), and Enterprise (custom pricing yearly, self-hosted or cloud). [Full pricing details](https://www.bytebase.com/pricing/).

## When to Choose Liquibase

- Your team is CLI-first and wants to embed migrations directly into existing CI/CD pipelines.
- You need to support a wide range of databases (60+), including niche or legacy systems.
- Your developers already work in Java/JVM ecosystems.
- You need a migration engine, not a governance platform — access control and approval happen in other tools.

## When to Choose Bytebase

- You want an all-in-one platform that covers migration, SQL review, access control, data masking, and audit logging — not just a migration CLI.
- Your team includes DBAs or platform engineers who review database changes before they reach production.
- You need compliance — approval flows, audit trails, and data masking for SOC 2, GDPR, or internal security policies.
- You're running multi-environment or multi-tenant deployments where batch change orchestration matters.

## FAQ

### Can I use Liquibase and Bytebase together?

They solve different layers. Liquibase handles the migration file format and execution; Bytebase handles collaboration, review, and governance. Most teams pick one, but if you already have a library of Liquibase changelogs, you don't have to throw them away — Bytebase can manage the review and deployment side while Liquibase stays as the execution engine.

### Is Liquibase still open source?

Liquibase Community switched from Apache 2.0 to the Functional Source License (FSL) in September 2025. FSL is not an OSI-approved open source license — it restricts commercial use in competing products. Each version reverts to Apache 2.0 after two years. Bytebase is MIT licensed, with enterprise features under a commercial license.

### Which tool has better CI/CD integration?

Depends on what "CI/CD integration" means to you. If it means "I want a command I can add to my Jenkins/GitHub Actions pipeline," Liquibase wins — it's just a CLI call. If it means "I want SQL files in a repo to automatically become reviewed, approved change issues," Bytebase's GitOps integration does that out of the box.

### What happened to Liquibase Hub?

Liquibase Hub (the web dashboard) was sunset in April 2023 and never replaced. There's no Liquibase GUI anymore. The Secure edition compensates with structured JSON logs you can pipe into Datadog or Splunk, and a VS Code extension for IDE-based interaction — but if you want a web UI for database changes, Liquibase doesn't have one.

---

Related comparisons:

- [Bytebase vs. Atlas](/blog/bytebase-vs-atlas/)
- [Bytebase vs. Flyway](/blog/bytebase-vs-flyway/)
- [Flyway vs. Liquibase](/blog/flyway-vs-liquibase/)
- [Top Database Schema Change Tools](/blog/top-database-schema-change-tool-evolution/)



When evaluating database management tools, Navicat and Bytebase represent two different philosophies: individual desktop productivity versus team collaboration and governance. Both tools have earned distinct positions in the database ecosystem, each optimized for a different mode of work.

Navicat, launched in 2001, has spent two decades refining a **comprehensive desktop GUI for operating databases as a single user**. Starting with MySQL and later adding MariaDB, MongoDB, Oracle, SQLite, PostgreSQL, and SQL Server, it remains one of the most fully-featured single-user clients on the market.

![navicat](/content/blog/bytebase-vs-navicat/navicat.webp)

Bytebase, on the other hand, emerged from the recognition that modern development teams need **collaborative database management tools that integrate with DevOps workflows and enforce centralized control**.

![bytebase-sql-editor](/content/blog/bytebase-vs-navicat/bytebase-sql-editor.webp)

## Feature Comparison

| Feature | Navicat | Bytebase |
|---------|---------|----------|
| **Product Position** | Single-user database GUI | Collaborative database development platform |
| **Interface** | Desktop application | Web-based platform |
| **Installation** | Desktop install on each machine | Single binary deployment |
| **Database Support** | 7 engines (MySQL, MariaDB, MongoDB, Oracle, SQLite, PostgreSQL, SQL Server) | 25 enterprise-focused engines |
| **User Experience** | ⭐⭐⭐⭐ Mature desktop UI | ⭐⭐⭐⭐ Modern web UI |
| **Query Development** | ⭐⭐⭐⭐ Solid SQL editor | ⭐⭐⭐⭐ Solid SQL editor |
| **Team Collaboration** | ⭐ Minimal | ⭐⭐⭐⭐⭐ Excellent |
| **Credential Management** | ❌ Distributed per user | ✅ Centralized |
| **Change Management** | ❌ Direct changes only | ✅ Review workflow |
| **Access Control** | ❌ Tied to DB credentials | ✅ Fine-grained RBAC |
| **Data Masking** | ❌ Not available | ✅ Dynamic masking (Paid) |
| **Audit Log** | ❌ Not available | ✅ Comprehensive (Paid) |
| **SQL Review Policy** | ❌ Not available | ✅ Built-in lint rules |
| **GitOps Integration** | ❌ Manual processes | ✅ Native VCS integration |
| **Governance & Compliance** | ⭐ Limited | ⭐⭐⭐⭐⭐ Enterprise-grade |
| **DevOps Integration** | ⭐ Minimal | ⭐⭐⭐⭐⭐ Native |
| **Licensing Model** | Per-seat perpetual or annual | Free + paid tiers, open source core |
| **Open Source** | ❌ Proprietary | ✅ [Open source](https://github.com/bytebase/bytebase) |

## Database Engine Support Comparison

### Navicat: Classic Relational + Selected NoSQL (7 Engines)

Navicat covers the long-established commercial and open-source databases used by most teams:

- **Core Relational**: MySQL, MariaDB, PostgreSQL, Oracle, SQL Server, SQLite
- **NoSQL**: MongoDB

Each engine ships as a separate SKU (Navicat for MySQL, Navicat for PostgreSQL, etc.), and the unified **Navicat Premium** bundles them at a higher price point.

### Bytebase: Enterprise-Focused Support (25 Engines)

Bytebase supports **25 carefully selected database engines** focusing on enterprise environments:

- **Core Relational**: MySQL, PostgreSQL, Oracle, SQL Server, MariaDB, SQLite
- **Cloud Warehouses**: Snowflake, Redshift, BigQuery, Databricks
- **Modern Analytics**: ClickHouse, StarRocks, RisingWave
- **NoSQL & Emerging**: MongoDB, Redis, TiDB, OceanBase

**Key Difference**: Navicat targets the traditional OLTP stack a single DBA or developer touches day-to-day, while Bytebase covers the broader analytics and modern-data-stack engines that show up in team environments.

## Target Roles and Responsibilities

### Navicat: Individual Developers and Solo DBAs

**Best suited for:**
- **Database Developers**: Schema design, query writing, ad-hoc data exploration on a personal machine
- **Solo DBAs**: Running maintenance, backups, and data transfers on databases they own end-to-end
- **Freelancers and Consultants**: Quickly connecting to a client database without infrastructure setup
- **Students and Hobbyists**: Learning SQL or working on personal projects

### Bytebase: Team-Oriented Roles and Coordinators

**Best suited for:**
- **Team-Based Developers**: Collaborative environments, shared queries, web-based access
- **Database Administrators**: Production governance, team coordination, organizational policies
- **DevOps/Platform Engineers**: CI/CD integration, automated deployments, standardized operations
- **Engineering Managers & Security Engineers**: Change coordination, governance, access controls, audit trails

### Key Difference

- **Navicat**: Individual productivity on a workstation, single set of credentials per user
- **Bytebase**: Team collaboration, governance, structured change management, centralized credentials

## Goals and Objectives Each Tool Achieves

The choice between Navicat and Bytebase usually comes down to whether the work is owned by one person or by a team.

### Navicat: Maximizing Solo Productivity

**Primary Goals:**
- **Operate a Database End-to-End**: Connect, query, edit, back up, and transfer data from a single client
- **Cover Multiple Engines Locally**: Switch between MySQL, PostgreSQL, Oracle, and MongoDB without leaving the app
- **Provide a Mature Desktop UX**: Two decades of refinement on visual schema design, ER diagrams, and import/export wizards
- **Work Offline**: No server-side dependencies — the application is the system

### Bytebase: Enabling Team Collaboration and Governance

**Primary Goals:**
- **Implement Database DevOps**: Integrate database changes into modern CI/CD pipelines with GitOps workflows
- **Ensure Change Control**: Prevent unauthorized or risky database modifications through review and approval processes
- **Maintain Compliance**: Provide comprehensive audit trails and access controls for regulatory requirements
- **Standardize Processes**: Establish consistent database management practices across teams and environments
- **Reduce Security Risks**: Centralize credential management and implement fine-grained access controls

**Secondary Goals:**
- **Improve Team Coordination**: Enable collaboration on database changes and knowledge sharing
- **Automate Governance**: Implement SQL review policies and automated quality checks
- **Scale Operations**: Support multi-environment deployments and tenant management

## Real-World Use Case Studies

### Use Case 1: Distributing Database Access to a Growing Team

**Scenario**: A 20-person engineering team needs read access to production PostgreSQL and MySQL for debugging.

**Navicat**: The DBA exports connection strings and shares credentials over chat or a password manager. Each developer installs Navicat locally and stores credentials on their machine. Revoking access means rotating the shared password and re-distributing it.
*Result: Fast initial setup, but credentials sprawl across laptops and there is no record of who ran what.*

**Bytebase**: The DBA configures the instances once in Bytebase, grants per-developer roles, and developers log in via SSO. Queries run through Bytebase's SQL Editor — no database credentials leave the server. Revocation is a single click.
*Result: Centralized access with full audit trail; onboarding and offboarding are operations on Bytebase, not on the database.*

![bytebase-access-control](/content/blog/bytebase-vs-navicat/bytebase-access-control.webp)

### Use Case 2: Reviewing a Production Schema Change

**Scenario**: A developer needs to add a non-null column and backfill data on a 50M-row table in production.

**Navicat**: The developer writes the `ALTER TABLE` and `UPDATE` in Navicat's SQL editor and executes against production directly. Coordination happens out of band — a Slack message, a Jira ticket, or a verbal review. A wrong `WHERE` clause is one keystroke away from disaster.
*Result: Maximum speed, minimum safety. Mistakes are immediate and unrecoverable.*

**Bytebase**: The developer submits the change as an issue. Bytebase's SQL review policy flags the missing `WHERE` automatically; a DBA approves the migration; Bytebase runs it against staging, then production, recording every step.
*Result: Reviewable, replayable, auditable — the change passes through the same kind of pipeline code does.*

![bytebase-change-process](/content/blog/bytebase-vs-navicat/bytebase-change-process.webp)

### Use Case 3: Letting Support Engineers Query Customer Data

**Scenario**: A support team needs to look up customer records in production, but PII must not be exposed.

**Navicat**: Support engineers get read-only database credentials. They see every column in full, including emails, phone numbers, and payment metadata. The only options are "give them access" or "don't."
*Result: Either over-exposure or a ticket queue routed through the DBA.*

**Bytebase**: The DBA configures column-level masking policies. Support engineers query through Bytebase's SQL Editor; PII columns return masked values automatically. Every query is logged with the engineer's identity.
*Result: Self-service access with PII protected at the query layer, not at the application layer.*

![bytebase-data-masking](/content/blog/bytebase-vs-navicat/bytebase-data-masking.webp)

## What Navicat and Bytebase Have in Common

Despite their different approaches, both platforms share several core capabilities:

- **Multi-database support**: Both connect to the major relational engines and MongoDB
- **SQL editor with results grid**: Syntax highlighting, query execution, result browsing
- **Schema browsing and editing**: Table, view, index, and constraint management
- **Data import/export**: CSV, SQL, and other common formats
- **Visual query results**: Sortable, filterable grids over query output

## Navicat's Strengths: Where It Truly Excels

- **Mature Desktop UX**: Two decades of refinement — ER diagrams, visual query builder, data modeler, schema sync, all polished and stable
- **Offline-First**: The application is self-contained; no server, no network dependency beyond the database connection itself
- **Breadth of Local Tooling**: Backup scheduling, data transfer between databases, import/export wizards, and report builders all ship in a single binary
- **Single-User Speed**: For one person operating a database they own, there is no review layer, no approval queue — just direct access
- **Low Operational Overhead**: Install the app, enter credentials, work — no infrastructure to maintain

## Bytebase's Strengths: Team Collaboration and Governance

- **Centralized Credential Management**: Database credentials never leave the Bytebase server. Developers authenticate to Bytebase via SSO; Bytebase authenticates to the database. Eliminates the credential sprawl that comes with desktop clients.

![bytebase-admin-mode](/content/blog/bytebase-vs-navicat/bytebase-admin-mode.webp)

- **Fine-Grained Access Control**: Grant access to specific databases, schemas, or tables within an instance — not just "the whole server or nothing"
- **Data Masking**: Column-level masking policies protect PII at the query layer, with no application changes required
- **Change Review Workflow**: Every schema and data change passes through review, lint, and approval before reaching production
- **SQL Review Policy**: Automated checks for missing `WHERE` clauses, missing indexes, unsafe DDL, and dozens of other anti-patterns

![bytebase-sql-review](/content/blog/bytebase-vs-navicat/bytebase-sql-review.webp)

- **Comprehensive Audit Log**: Every query, every change, every approval recorded with user identity — the answer to "who did what" is always one filter away

![bytebase-audit-log](/content/blog/bytebase-vs-navicat/bytebase-audit-log.webp)

- **GitOps Integration**: Schema migrations live in your repository; merging a PR triggers the deployment pipeline through Bytebase

![bytebase-gitops](/content/blog/bytebase-vs-navicat/bytebase-gitops.webp)

- **Shared Query Library**: Save and share SQL across the team — the team's collective knowledge instead of scripts scattered across laptops

![bytebase-share-scripts](/content/blog/bytebase-vs-navicat/bytebase-share-scripts.webp)

## Making the Right Choice

**Core Trade-off**: Navicat prioritizes individual productivity on a desktop with direct database access. Bytebase prioritizes team collaboration and governance with centralized access and review workflows.

### Choose Navicat When:

- You are operating a database alone or in a very small team
- Direct, unmediated database access is acceptable
- There are no compliance or audit requirements
- Offline desktop work matters more than collaboration

### Choose Bytebase When:

- Multiple people touch the same databases
- Database credentials should never be distributed to individuals
- Schema and data changes need review, approval, and audit
- DevOps integration and automated workflows are priorities
- Compliance (SOC 2, ISO 27001, HIPAA, PCI-DSS) is in scope

## Conclusion

Navicat excels as a sophisticated single-user desktop database client with mature tooling and excellent individual productivity. Bytebase focuses on team collaboration, database DevOps, and governance. The choice depends on whether the work is owned by one person — where Navicat's direct, unmediated access is an advantage — or by a team, where centralized control, review workflows, and audit trails are required.

In addition, Bytebase is [open source](https://github.com/bytebase/bytebase) and adopts a usage-based pricing model.


Bytebase ブログ

データベースガバナンスのスタンダードを体験する

ソリューションとプラットフォーム

リソース

変更管理

アクセスとセキュリティ

AI とエージェント

比較

会社

法的事項