This post is maintained by Bytebase, an open-source database governance platform that can manage both Snowflake and Redshift. We update the post every year.
Why Comparing Snowflake and Amazon Redshift
Snowflake represents a cloud-agnostic approach with its unique separation of storage and compute resources, while Amazon Redshift is deeply integrated with the AWS ecosystem, offering tight connections to other AWS services. Since 2025 the two have been converging: both now read and write Apache Iceberg tables, and both pitch AI agents on top of the warehouse.
This comparison reflects the current state of both systems as of 2026, including the latest features and capabilities:
Feature Comparison
Core Database Features
| Feature | Snowflake | Amazon Redshift |
|---|---|---|
| Data Types | Comprehensive set including structured, semi-structured (JSON, XML, Parquet, Avro) | Standard SQL data types, plus SUPER type for semi-structured data |
| Indexing | Automatic clustering, no manual index management required | Automatic table sort and distribution keys, zone maps |
| Transactions | ACID-compliant with automatic concurrency control | ACID-compliant with serializable isolation |
| Stored Procedures | JavaScript, SQL, Java, Python, Scala | SQL, Python, stored procedures with transaction support |
| Views | Regular, Materialized, Secure | Regular, Late Binding, Materialized |
| Triggers | Limited support through tasks and streams | Limited support, primarily through Lambda integration |
| Partitioning | Automatic micro-partitioning, clustering keys | Distribution keys, sort keys |
| Constraints | Primary key, Foreign key, Unique, Not Null (not enforced) | Primary key, Foreign key, Unique (enforced) |
Advanced Features
| Feature | Snowflake | Amazon Redshift |
|---|---|---|
| High Availability | Built-in redundancy, automatic failover, cross-region replication | Multi-AZ deployments, automatic backups, cross-region snapshots |
| Scalability | Independent scaling of compute and storage, instant scaling | Elastic resize, concurrency scaling, RA3 instances with managed storage |
| Security | Role-based access control, column-level security, row-level security, encryption | IAM integration, VPC, encryption, column-level access control, dynamic data masking |
| Cloud Integration | Multi-cloud (AWS, Azure, GCP), cloud-agnostic | Deep AWS ecosystem integration |
| Open Table Formats | Iceberg tables, Apache Polaris catalog (open sourced by Snowflake) | Iceberg read/write, SageMaker Lakehouse, S3 Tables |
| AI/ML Capabilities | Cortex AI (Analyst, Search), CoWork agent, semantic views, Snowpark ML, vector search | Redshift ML, Amazon Q generative SQL, SageMaker Data Agent, vector search |
| Extensibility | External functions, UDFs, stored procedures, Snowpark | UDFs, stored procedures, Lambda integration, Apache Spark integration |
Snowflake-Specific Features
- Multi-cloud support (AWS, Azure, GCP)
- Zero-copy cloning for instant data duplication
- Time Travel to access historical data
- Secure data sharing without data movement
- Snowpark for multi-language data processing
- Fully automated optimization (no vacuuming or tuning); Gen2 warehouses and Adaptive Compute (2025)
- Unlimited concurrency with isolated warehouses
- Openflow for native, multi-modal data ingestion (2025)
- Cortex AI and CoWork: text-to-SQL over governed semantic views, plus a conversational analytics agent
- dbt Projects, Workspaces, and DCM Projects for in-platform development and declarative change management
Amazon Redshift-Specific Features
- Tight AWS integration (S3, Glue, EMR, SageMaker)
- Spectrum for querying S3 data without loading it
- Zero-ETL ingestion from Aurora, RDS, and DynamoDB
- Amazon Q generative SQL in Query Editor v2
- Iceberg read/write and SageMaker Lakehouse integration (2025)
- Multidimensional Data Layouts for large sorted workloads (2025)
- Auto table optimization and maintenance
- Federated queries across diverse sources
- Serverless option with AI-driven scaling
- Multi-AZ deployments for high availability
Technical Specifications
Architecture
Snowflake (cloud-native, three layers)
- Storage: columnar micro-partitions on S3, Azure Blob, or GCS; automatically organized, compressed, and encrypted by default
- Compute: independent virtual warehouses that resize on demand, auto-scale, cache results, and isolate workloads from one another
- Cloud Services: authentication, metadata, and query optimization
- The practical consequence: storage and compute scale independently, and there is almost no manual tuning surface
Best for: teams who want hands-off, auto-optimized operation.
Amazon Redshift (classic MPP, AWS-integrated)
- Leader node plans and coordinates queries; compute nodes execute them in parallel across slices
- Redshift Managed Storage: S3-backed columnar storage with zone maps, compression, and automatic backups
- Concurrency Scaling adds temporary capacity; Serverless sizes compute to the workload automatically
- The practical consequence: excellent price-performance when you invest in distribution keys and sort keys; provisioned clusters reward hands-on tuning
Best for: AWS-centric teams who want control over data layout.
Development Workflow
| Feature | Snowflake (Flexible, Cloud-Agnostic) | Amazon Redshift (AWS-Native, Integrated) |
|---|---|---|
| Web Interface | Snowsight with Workspaces (file explorer, Git, dbt Projects) | Query Editor v2 with Amazon Q generative SQL |
| Dev Tools Support | VS Code, SnowSQL CLI, Snowpark (SQL, Python, Java), CoCo AI agent | Optimized for AWS tools (Glue, SageMaker Unified Studio) |
| Schema Management | SQL, Snowpark, or declarative DCM Projects (plan-then-deploy) | SQL and AWS Glue for catalog integration |
| Version Control | Native Git integration in Workspaces | Any Git tool (CodeCommit closed to new customers in 2024) |
| Deployments | DCM Projects, Tasks, external CI/CD (e.g., GitHub Actions) | Fully automatable with CloudFormation + CodePipeline |
| Testing | Custom or third-party frameworks | AWS-native DevOps tools or external testing integrations |
| CI/CD | Tool-agnostic; integrates with various CI/CD platforms | Deep integration with AWS CI/CD tools like CodeBuild, CodePipeline |
Pricing
| Snowflake (Pay-as-you-go, Flexible but Complex) | Amazon Redshift (AWS-Integrated, Discount Options) | |
|---|---|---|
| Pricing | 4 tiers: Standard → Enterprise → Business Critical → VPS | On-demand, Reserved Instances (1–3 years), or Serverless |
| Compute Pricing | Per-second billing for virtual warehouses (scales up/down) | Hourly (node-based) or Serverless (pay per query) |
| Storage Pricing | Monthly per TB (compressed) | Redshift Managed Storage (RMS) per GB |
| Cloud Services | Included in compute costs | Spectrum (query S3), Concurrency Scaling (beyond free tier incurs cost) |
| Best For | Bursty workloads, multi-cloud setups, flexible scaling | Steady AWS-native workloads, long-term cost optimization |
Conclusion
The classic guidance still holds: Snowflake excels for multi-cloud flexibility, hands-off management, and data sharing, while Redshift is ideal for AWS-centric environments with cost-efficient steady workloads and deep AWS integrations.
What changed since 2025 is that the lock-in argument weakened on both sides. Both warehouses now read and write Apache Iceberg tables (Snowflake with the Apache Polaris catalog it open sourced, Redshift through SageMaker Lakehouse and S3 Tables), so your data can outlive your warehouse choice. And both are racing up the same AI stack: Snowflake with Cortex Analyst and the CoWork agent over governed semantic views, AWS with Amazon Q generative SQL and the SageMaker Data Agent. Pick by where your workloads and team already live; the storage format no longer has to decide for you.
References
- Snowflake Official Documentation
- Amazon Redshift Documentation
- Snowflake Editions and Pricing
- Amazon Redshift Pricing
- Snowflake Architecture Overview
- Amazon Redshift Architecture