Amazon Redshift and Snowflake both solve large-scale SQL analytics, but they start from different assumptions about where your data already lives. Redshift is…
| Amazon Redshift | Snowflake | |
|---|---|---|
| Primary category | Databases | Databases |
| Rating | Not documented | Not documented |
| Pricing model | Usage-based | usage-based / consumption (credit-based) |
| Starting price | $0.375 per RPU-hour (Serverless) | Pay-as-you-go, approximately $2 per credit on Standard Edition (billed per second), plus separate storage costs; 30-day free trial with $400 in usage credits |
| Free plan | Not documented | Not documented |
| Free trial | Yes | Yes |
| Platforms | Web | Web |
| Team collaboration | Not documented | Not documented |
| AI features | Yes | Yes |
| Public API | Yes | Yes |
Cloud platform support
Amazon Redshift: Amazon Redshift runs exclusively within AWS.
Snowflake: Snowflake runs natively on AWS, Microsoft Azure, and Google Cloud.
Multi-cloud or cloud-diversifying organizations cannot use Redshift outside AWS, while Snowflake lets them pick a provider and region per account.
Compute and storage architecture
Amazon Redshift: Redshift Serverless scales compute automatically per Redshift Processing Unit-hour, while provisioned clusters require choosing and managing node types and cluster size.
Snowflake: Snowflake separates storage and compute entirely, letting multi-cluster virtual warehouses scale up, down, or out independently of stored data.
Independent scaling of storage and compute in Snowflake can simplify capacity planning compared to Redshift provisioned clusters, which still require manual tuning of distribution keys and sort keys.
Pricing model and free tier
Amazon Redshift: Redshift bills per node-hour for provisioned clusters or per RPU-hour for Serverless, plus storage, with a free trial available for new AWS customers.
Snowflake: Snowflake bills approximately 2 dollars per credit on Standard Edition, billed per second, plus separate storage costs, with a 30-day free trial including 400 dollars in usage credits.
Both are usage-based rather than flat-fee, but Snowflake's trial is time- and credit-limited while Redshift's free trial is tied to ongoing AWS Free Tier eligibility.
Data lake querying
Amazon Redshift: Redshift Spectrum lets SQL queries run directly against data stored in Amazon S3 without loading it into the warehouse first.
Snowflake: Snowflake's provided facts do not document an equivalent named data lake query feature.
Redshift Spectrum is a documented, named capability for querying S3 data in place, which is a common data lake pattern for AWS-based teams.
Native data sharing and marketplace
Amazon Redshift: Redshift's documented facts do not include a named data sharing marketplace feature.
Snowflake: Snowflake offers Secure Data Sharing for live governed datasets and a Snowflake Marketplace for discovering third-party data and applications.
Organizations that regularly exchange data with partners, subsidiaries, or customers, or want to consume third-party datasets, get this as a built-in Snowflake capability.
Built-in AI and machine learning
Amazon Redshift: Redshift ML lets analysts create, train, and run machine learning models using SQL statements, powered by Amazon SageMaker under the hood.
Snowflake: Snowflake Cortex provides built-in large language model and AI functions for summarization, translation, sentiment analysis, and forecasting, and Snowpark lets developers write pipelines in Python, Java, or Scala inside Snowflake.
Both platforms embed AI and ML capability directly in SQL workflows, but Snowflake's documented set spans both LLM-style functions and a general programmatic runtime, while Redshift's is SQL-driven ML backed by SageMaker.
Operational data integration
Amazon Redshift: Redshift offers Zero-ETL integrations for near real-time data from Amazon Aurora and RDS, plus federated query against live PostgreSQL and MySQL databases.
Snowflake: Snowflake's provided facts do not document equivalent named zero-ETL or federated query features.
Redshift's documented integrations reduce custom pipeline work specifically for AWS-native operational databases.
Time travel and instant cloning
Amazon Redshift: Redshift's documented facts do not include a named time travel or zero-copy cloning feature.
Snowflake: Snowflake's Time Travel and zero-copy cloning let teams query or restore historical data and clone databases, schemas, or tables instantly without duplicating storage.
This documented Snowflake capability supports fast recovery from mistakes and low-cost creation of test or development copies of production data.
Concurrency handling under bursty load
Amazon Redshift: Redshift's concurrency scaling automatically adds temporary cluster capacity during spikes in concurrent queries.
Snowflake: Snowflake's multi-cluster virtual warehouses scale automatically to handle concurrent queries and variable workload demand.
Both platforms document automatic scaling for concurrency spikes, though Redshift's own documented cons note that Serverless workloads can experience scaling latency under sudden bursty patterns.
SQL dialect and learning curve
Amazon Redshift: Redshift originated as a fork of PostgreSQL, giving it a familiar PostgreSQL-based SQL dialect that lowers the learning curve for existing SQL users.
Snowflake: Snowflake's advanced features such as Snowpark and Cortex are documented as requiring additional learning for teams new to the platform.
Teams with existing PostgreSQL SQL skills may ramp up faster on Redshift's core dialect, while unlocking Snowflake's full advanced feature set takes more onboarding investment.
| Feature | Amazon Redshift | Snowflake |
|---|---|---|
| Columnar storage | Available | Not documented |
| Massively parallel processing | Available | Not documented |
| Separation of storage and compute | Not documented | Available |
| Serverless or auto-provisioned compute | Available | Available |
| Feature | Amazon Redshift | Snowflake |
|---|---|---|
| Runs on AWS | Available | Available |
| Runs on Microsoft Azure | Unavailable | Available |
| Runs on Google Cloud | Unavailable | Available |
| Feature | Amazon Redshift | Snowflake |
|---|---|---|
| Query S3 data lake directly | Available | Not documented |
| Federated query into operational databases | Available | Not documented |
| Zero-ETL integration from operational databases | Available | Not documented |
| Feature | Amazon Redshift | Snowflake |
|---|---|---|
| Native secure data sharing | Not documented | Available |
| Data or app marketplace | Not documented | Available |
| Feature | Amazon Redshift | Snowflake |
|---|---|---|
| SQL-driven machine learning | Available | Not documented |
| Built-in LLM or generative AI functions | Not documented | Available |
| In-platform programmatic pipeline development | Not documented | Available |
| Feature | Amazon Redshift | Snowflake |
|---|---|---|
| Materialized views | Available | Not documented |
| Concurrency scaling for query spikes | Available | Available |
| Instant cloning or historical data recovery | Not documented | Available |
| Feature | Amazon Redshift | Snowflake |
|---|---|---|
| Encryption at rest and in transit | Available | Not documented |
| Role-based access control | Not documented | Available |
| Dedicated private deployment option | Not documented | Available |
| Feature | Amazon Redshift | Snowflake |
|---|---|---|
| Usage-based billing | Available | Available |
| Free trial | Available | Available |
| Reserved or discounted long-term pricing | Available | Not documented |
Starting price reflects the lowest paid tier, not the full cost for every team size or usage level.
Pros
Cons
Pros
Cons
Neither is a flat cheaper option since both use usage-based pricing, but Redshift bills per node-hour or per RPU-hour plus storage, while Snowflake bills roughly 2 dollars per credit on its Standard Edition billed per second plus separate storage costs, so the actual cheaper choice depends on workload shape, and both require monitoring to control cost.
No, Amazon Redshift is an AWS-native service and runs exclusively within AWS, whereas Snowflake runs natively on AWS, Microsoft Azure, and Google Cloud.
The provided Snowflake facts do not document an equivalent named feature for querying S3 data lake content directly the way Redshift Spectrum does, so this should be verified against current Snowflake documentation if S3-native querying is a requirement.
Redshift may have a shorter initial learning curve for teams already comfortable with SQL because it originated as a PostgreSQL fork, while Snowflake's core querying is also SQL-based but its advanced features like Snowpark and Cortex are documented as requiring additional learning for teams new to the platform.
Yes, Redshift offers a free trial for new AWS customers under AWS Free Tier terms, and Snowflake offers a 30-day free trial that includes 400 dollars in usage credits.
Both document built-in AI capability but of different kinds: Redshift ML lets analysts build machine learning models using SQL statements powered by Amazon SageMaker, while Snowflake Cortex provides built-in large language model and AI functions such as summarization, translation, sentiment analysis, and forecasting, plus Snowpark for programmatic pipeline development.
Read the full Amazon Redshift review · Read the full Snowflake review