Snowflake and Amazon Redshift are both enterprise-grade cloud data warehouses, but they serve different strategic needs. Snowflake excels in multi-cloud flexibility, cross-organization data sharing, and consumption-based pricing transparency. Amazon Redshift delivers superior value for AWS-committed organizations through zero-ETL integrations, deep SageMaker and S3 connectivity, and competitive on-demand pricing. The right choice depends on your cloud strategy, existing ecosystem investments, and workload patterns.
| Feature | Snowflake | Amazon Redshift |
|---|---|---|
| Best For | Multi-cloud analytics teams needing independent compute and storage scaling across AWS, Azure, and GCP with consumption-based billing starting at ~$2/credit | AWS-native organizations needing deep ecosystem integration with Aurora, S3, SageMaker, and Glue plus zero-ETL capabilities for near real-time analytics |
| Architecture | Multi-cluster shared data architecture with full separation of compute, storage, and cloud services layers; runs natively on AWS, Azure, and GCP | Columnar MPP architecture with leader and compute nodes; RA3 instances separate compute from managed storage; integrates with S3 data lake via Spectrum |
| Pricing Model | Standard (1-10 users): $89/mo; Enterprise: custom | Free tier (3 nodes, 2 TB storage), Pro $299/mo (10 nodes, 30 TB storage) |
| Ease of Use | Rated 8.7/10 across 455 reviews; users praise standard ANSI SQL support, minimal infrastructure tuning, and a fully managed platform experience | Rated 8.9/10 across 218 reviews; PostgreSQL-compatible SQL interface; Redshift Serverless removes cluster management; Amazon Q provides natural language SQL authoring |
| Scalability | Instant elastic scaling with multi-cluster virtual warehouses; auto-suspend and auto-resume eliminate idle costs; independent storage and compute scaling | Concurrency scaling handles virtually unlimited concurrent users; RA3 nodes scale compute independently from managed storage; Multi-AZ deployment delivers 99.99% SLA |
| Community/Support | 455 user reviews, strong partner ecosystem, developer community with open-source integrations, and enterprise support tiers across all editions | 22,913 companies use Redshift; Gartner ranked AWS 1st in Event Analytics, 2nd in Enterprise Data Warehouse; 551 Gartner ratings at 4.4/5 stars |
| Metric | Snowflake | Amazon Redshift |
|---|---|---|
| TrustRadius rating | 8.7/10 (455 reviews) | 8.9/10 (218 reviews) |
| PyPI weekly downloads | 39.0M | 11.2M |
| Search interest | 0 | 3 |
| Product Hunt votes | 88 | 83 |
As of 2026-05-04 — updated weekly.
| Feature | Snowflake | Amazon Redshift |
|---|---|---|
| Data Storage & Processing | ||
| Storage Architecture | Micro-partitioned columnar storage with automatic clustering and compression across all three major clouds | Columnar storage with zone maps, LZO/Zstandard/AZ64 compression encodings, and distribution styles for query optimization |
| Compute Model | Virtual warehouses with T-shirt sizing from X-Small to 6X-Large consuming 1-512 credits per hour | MPP compute nodes (dc2, ra3 types) with leader node coordination and per-node slice parallelism |
| Data Lake Integration | Interoperability with Apache Iceberg, Parquet, and other open table formats through external tables | Redshift Spectrum queries S3 data in Iceberg, Hudi, Delta Lake, Parquet, ORC, Avro, JSON, and CSV formats |
| Performance & Scalability | ||
| Concurrency Handling | Multi-cluster warehouses spin up additional clusters automatically to handle concurrent query spikes | Concurrency scaling adds transient capacity in seconds; free credits cover 97% of customers' concurrency needs |
| Query Optimization | Automatic query optimization with adaptive micro-partition pruning and result caching across warehouses | Machine learning-driven optimization with materialized views, result caching, and multidimensional data layouts (MDDL) |
| Auto-Scaling | Auto-suspend and auto-resume warehouses with per-second billing; scales to zero when idle | Serverless auto-scales compute based on workload; provisioned clusters use RA3 nodes with managed storage scaling |
| Security & Governance | ||
| Encryption | Automatic encryption of all data at rest and in transit; Tri-Secret Secure on Business Critical edition | TLS for data in transit and hardware-accelerated AES-256 for data at rest with AWS KMS key management |
| Access Control | Role-based access control with granular governance and privacy controls on Enterprise edition and above | Row-level and column-level security with dynamic data masking; integrated with AWS IAM Identity Center and Lake Formation |
| Compliance & Availability | Business Critical edition provides failover/failback disaster recovery; VPS edition for government and defense isolation | Multi-AZ deployment delivers 99.99% SLA; VPC network isolation with firewall rules and IPsec VPN support |
| Integration & Ecosystem | ||
| Cloud Provider Support | Runs natively on AWS, Azure, and Google Cloud with cross-cloud data sharing between accounts | AWS-exclusive with deep integration into S3, Glue, SageMaker, QuickSight, Kinesis, and DynamoDB |
| ETL & Data Ingestion | Snowpipe for continuous data loading; supports Python, Java, and SQL-based data pipelines via Snowpark | Zero-ETL integrations with Aurora, RDS, and DynamoDB; S3 auto-copy for automated file ingestion; Kinesis and MSK streaming |
| AI & Machine Learning | Snowpark ML for building and deploying LLMs and ML models; Snowflake Intelligence for natural language enterprise queries | Redshift ML trains and deploys models using SQL via SageMaker integration; Amazon Q provides generative SQL authoring |
| Data Sharing & Collaboration | ||
| Data Sharing | Live data sharing across clouds and organizations without copying data; provider pays storage, consumers pay compute | Multi-data warehouse writes through data sharing; cross-warehouse read and write access with independent billing |
| Marketplace & Third-Party Data | Snowflake Marketplace provides access to live, ready-to-query third-party datasets from partner organizations | AWS Data Exchange enables subscribing to and combining third-party datasets directly within Redshift queries |
| Developer Tools | Snowsight web interface, Snowpark developer framework supporting Python/Java/Scala, and open-source connector ecosystem | Redshift Query Editor web workbench, SageMaker Unified Studio SQL editor, and PostgreSQL-compatible driver ecosystem |
Storage Architecture
Compute Model
Data Lake Integration
Concurrency Handling
Query Optimization
Auto-Scaling
Encryption
Access Control
Compliance & Availability
Cloud Provider Support
ETL & Data Ingestion
AI & Machine Learning
Data Sharing
Marketplace & Third-Party Data
Developer Tools
Snowflake and Amazon Redshift are both enterprise-grade cloud data warehouses, but they serve different strategic needs. Snowflake excels in multi-cloud flexibility, cross-organization data sharing, and consumption-based pricing transparency. Amazon Redshift delivers superior value for AWS-committed organizations through zero-ETL integrations, deep SageMaker and S3 connectivity, and competitive on-demand pricing. The right choice depends on your cloud strategy, existing ecosystem investments, and workload patterns.
Choose Snowflake if:
Choose Snowflake when your organization operates across multiple cloud providers or plans to avoid lock-in to a single cloud ecosystem. Snowflake is the stronger option for teams that need to share live data across organizational boundaries, require near-zero infrastructure management, and want predictable consumption-based billing. Its multi-cluster warehouse architecture handles variable concurrency patterns well, and the Snowflake Marketplace provides direct access to third-party datasets. The trade-off is higher per-credit costs compared to Redshift Reserved Instances, and you lose the deep native integrations that come with being inside the AWS ecosystem.
Choose Amazon Redshift if:
Choose Amazon Redshift when your organization is heavily invested in AWS and needs tight integration with services like Aurora, S3, SageMaker, Glue, and DynamoDB. Redshift's zero-ETL integrations eliminate complex pipeline engineering, and the Serverless option removes cluster management overhead. With on-demand pricing starting at $0.54/hour and Reserved Instance discounts for steady workloads, Redshift delivers strong price-performance for predictable analytics. The trade-off is AWS lock-in, and teams running multi-cloud or needing cross-organization data sharing will find Snowflake's approach more flexible.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Direct cost comparison depends heavily on workload patterns. Snowflake charges $2-4 per credit depending on edition, with the median annual contract at $96,594 across 622 verified purchases. Redshift on-demand pricing starts at $0.54/hour per node, with Reserved Instances offering significant discounts for steady workloads. For variable, spiky workloads Snowflake's auto-suspend can reduce waste, while Redshift Reserved Instances deliver better value for predictable, always-on analytics. Both platforms bill storage separately, with Snowflake at $23-40/TB/month and Redshift managed storage at $0.02/GB/month.
Yes, some organizations run both platforms for different use cases. A common pattern is using Redshift for AWS-native operational analytics with zero-ETL from Aurora and DynamoDB, while using Snowflake for cross-cloud data sharing and multi-cloud analytics. Both platforms support open table formats like Apache Iceberg, making it possible to query the same S3 data from either warehouse. ETL tools like dbt, Fivetran, and Airbyte support both platforms, so teams can maintain pipelines to each warehouse without duplicating source integrations.
Both platforms have strong concurrency solutions but use different approaches. Snowflake's multi-cluster warehouses automatically spin up additional compute clusters during demand spikes, with each cluster independently scaling. Amazon Redshift uses concurrency scaling that adds transient capacity in seconds, with free credits covering the concurrency needs of 97% of customers. For organizations with hundreds of concurrent dashboard users, Snowflake's approach provides more predictable isolation between workloads, while Redshift's concurrency scaling is more cost-effective for teams that stay within the free credit allocation.
Both platforms support querying data in open formats stored on Amazon S3 or other object storage. Redshift Spectrum queries Apache Iceberg, Hudi, Delta Lake, Parquet, ORC, Avro, JSON, and CSV files directly in S3, and integrates with the Amazon SageMaker lakehouse for unified data access. Snowflake supports Apache Iceberg and other open table formats through external tables and has built cross-cloud data sharing into its architecture. Redshift has the advantage for AWS-native lakehouse patterns through its SageMaker integration, while Snowflake provides a more cloud-agnostic lakehouse approach that works across AWS, Azure, and GCP.