Updated October 25, 2026
· ~2 min read
The warehouse decision used to lock in your data. It locks in much less now. Redshift, Snowflake and Databricks can all read and write Apache Iceberg tables in S3, so the storage layer can be shared and the compute engine chosen per workload.
What still differs is how each one bills, how much of the platform you get beyond SQL, and how naturally it fits the rest of an AWS account.
Question 1 of 3
Who writes most of the queries?
**Redshift:** your data and pipelines are on AWS, you want zero-ETL from Aurora, RDS and DynamoDB, and you prefer one bill and one IAM model. Serverless removes most of the cluster management. **Snowflake:** analysts and SQL are the center of the work, you want the least administration and strong data sharing between companies. You pay a premium for that simplicity. **Databricks:** engineering-heavy teams doing data engineering, ML and SQL on the same platform, comfortable with notebooks and Spark. The broadest platform and the most to configure. At list prices, similar workloads land in similar ranges. Concurrency, idle time and how well the team tunes each platform move the bill more than the rate card.
AWS Data Lakehouse Platform
A production data lakehouse on AWS (S3-backed data lake with Glue, Iceberg, Athena, and Redshift) structured for analytics, ML feature stores, and governed data sharing.
AWS Cost Optimization & FinOps Sprint
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As of October 25, 2026. Provided as is, for general information, and accurate to the best of our knowledge on the date shown. Not legal, compliance, tax, financial or other professional advice. Prices, limits and vendor services change; check current vendor documentation before relying on anything here. Terms of use.