Amazon Redshift suits companies already built on AWS, because it pulls data from Aurora, RDS and DynamoDB through zero-ETL integrations and its serverless option scales compute for you. Snowflake suits teams that want one managed data and AI platform working across clouds and regions, with read and write access to Iceberg tables and AI built in. If your data lives in AWS, start with Redshift; if you're multi-cloud or may move, start with Snowflake.
Both are cloud data warehouses, so on a feature list they look alike. The difference is where they're hosted and what they're built to connect to. I read AWS's Redshift and Redshift Serverless pages, Snowflake's platform page and its trial documentation.
Redshift and Snowflake at a glance
| Amazon Redshift | Snowflake | |
|---|---|---|
| Where it runs | Inside AWS | Across clouds and regions |
| Hands-off option | Redshift Serverless scales compute | Fully managed platform |
| Open table formats | Queries Apache Iceberg | Read and write for Iceberg tables |
| AI and analytics | SQL analytics in Amazon SageMaker | Cortex AI |
| Getting data in | Zero-ETL from Aurora, RDS, DynamoDB | Open ecosystem of third-party data |
| Application data | Not covered on the pages I read | Snowflake Postgres and Hybrid Tables |
| Trial | Sign up free through AWS | 30 days, no payment details |
The trial detail that catches Snowflake testers
Snowflake's trial is easy to start: you need only a valid email, with no payment information. Two details in its documentation matter. The cloud, region and edition you pick change how fast your free usage runs out, and the trial ends after 30 days or when that usage is spent, whichever comes first, at which point the account is suspended. AI features are also switched off on self-service trials until you add a credit card, so if you're testing Cortex AI, plan for that step. It's an easy thing to miss when you're only running a quick proof of concept.
Why AWS teams lean towards Redshift
The case for Redshift is integration. Zero-ETL integrations move transactional data from Amazon Aurora, RDS and DynamoDB into the warehouse without you building and running complex pipelines, and Redshift Serverless learns from your workloads and scales compute automatically, with no clusters to manage. AWS also lets you run SQL analytics across your lakehouse in Amazon SageMaker and query Apache Iceberg tables in your data lake.
Why multi-cloud teams lean towards Snowflake
The pitch here is a single, fully managed data and AI platform that works across clouds and regions, with business continuity and disaster recovery across them. It gives read and write access to Iceberg tables, adds Cortex AI and Horizon Catalog, and now runs Snowflake Postgres and Hybrid Tables for application data, so some teams can keep operational and analytical work on one platform. Its platform page also lists Notebooks and built-in governance, security and compliance controls, including business continuity and disaster recovery, all run for you. That suits a small data team that would rather not manage infrastructure at all.
Five questions for your data team
- Where does most of your source data live today?
- Do you need to run in more than one cloud?
- Will analysts share data with partners on other platforms?
- Do you want AI features inside the warehouse?
- Who will manage capacity and performance settings?
If you're earlier on, my roundup of data analytics tools covers the reporting layer, and this piece on business intelligence success explains what a warehouse should feed.
My advice on Redshift or Snowflake
If your applications already run on AWS databases, Redshift's the shorter path, because the zero-ETL integrations and serverless scaling remove a lot of setup. If you're multi-cloud, expect to change cloud providers, or want AI and application data next to your warehouse, Snowflake is the more flexible choice. Run the same three real queries on both trials before you decide, and time them. That'll tell you more than any feature list, and it won't take long.
Do you make data or analytics software? Tool makers can ask to be considered for a featured place in comparisons like this one.
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Frequently asked questions
Is Snowflake only on AWS?
No. Snowflake says its platform works across clouds and regions, and you choose the cloud platform and region when you start a trial.
Does Redshift have a serverless option?
Yes. Redshift Serverless learns from your workloads and scales compute automatically, with no infrastructure to manage.
Can both query Apache Iceberg?
Yes. AWS says Redshift can query open table formats like Apache Iceberg, and Snowflake gives read and write access to Iceberg tables.
How long is the Snowflake free trial?
Snowflake's documentation says the trial lasts 30 days or until your free usage balance is used up, whichever comes first.
What is zero-ETL in Redshift?
AWS uses the term for integrations that move transactional data from Amazon Aurora, RDS and DynamoDB into Redshift without you building and managing complex pipelines.
Can I test Snowflake AI features on a trial?
Only after adding a credit card. Snowflake disables AI features on self-service trials until a card is on the account.
