Independent comparison for technology buyers. Updated May 2026.
Quick verdict: Choose Databricks when ML/AI workloads share the platform with ETL and BI and when an open Delta lakehouse strategy is preferred. Choose Amazon Redshift when SQL-first analytics on AWS with tight S3, Glue, and SageMaker integration is the primary need. The differentiator is multi-cloud lakehouse with rich ML tooling versus AWS-native MPP and serverless warehouse.
| Criteria | Databricks | Amazon Redshift |
|---|---|---|
| Rating | 4.6 / 5.0 (3,200 reviews) | 4.2 / 5.0 (2,100 reviews) |
| Architecture | Lakehouse on Delta / Spark + Photon | RA3 nodes with managed storage, Serverless |
| Cloud Deployment | AWS, Azure, GCP | AWS only |
| Pricing Model | DBUs + cloud VM/storage | Node-hour, RPU-hour, storage |
| Open Format | Delta Lake, Iceberg via UniForm | Native columnar, S3 Iceberg via Spectrum |
| ML / AI | MLflow, Mosaic AI, AutoML, serving | Redshift ML via SageMaker |
| BI Integration | Databricks SQL, Power BI, Tableau | Power BI, Tableau, QuickSight |
| Best For | ETL, ML/AI, lakehouse workloads | AWS-native BI/SQL, lakehouse on S3 |
| Concurrency | SQL warehouses (serverless), Photon | Concurrency scaling clusters / RPUs |
Databricks runs across AWS, Azure, and GCP as a unified lakehouse for ETL, BI on Delta, ML training and serving, and increasingly generative AI workflows via Mosaic AI. Photon accelerates SQL on Delta to warehouse-class performance while keeping data in open format. Unity Catalog governs data, ML features, and models centrally.
Amazon Redshift is AWS-native. RA3 instances decouple storage and compute via Redshift Managed Storage on S3. Redshift Serverless removes node management for spiky workloads. Redshift Spectrum queries S3 (Parquet, Iceberg) directly, and Redshift ML wraps SageMaker for in-database training and inference. Tight integration with Lake Formation, Glue, Kinesis, and SageMaker simplifies AWS-centric architectures.
For AWS-only data estates with predominantly SQL workloads, Redshift is hard to displace on integration economics. For workloads where machine learning, streaming, and ETL must share governance and engine with BI, Databricks tends to fit better. Compare against Snowflake vs Databricks and Snowflake vs Redshift; further options in data analytics.
Databricks pricing combines DBU rates (around $0.07-$0.95/DBU by workload and tier) with the underlying cloud VM and storage. Photon clusters and All-Purpose compute cost more than Jobs compute. Enterprise spend commonly lands $300,000-$10M ARR including AWS infrastructure.
Redshift RA3 dc2 instances start around $0.85/hour on demand, with 1-year and 3-year Reserved Instance discounts of 30-60%. Redshift Serverless is around $0.36/RPU-hour with an 8-RPU minimum. Storage on RMS runs around $0.024/GB/month. Enterprise Redshift spend typically lands $200,000-$8M ARR. AWS Enterprise Discount Program can lower effective cost significantly.
Choose Databricks when ML/AI, data engineering, and BI share infrastructure, when open Delta or Iceberg is a strategic direction, when multi-cloud portability matters, or when streaming via Structured Streaming and Delta Live Tables is in scope.
Choose Amazon Redshift when the estate is AWS-native, when reserved instance economics drive procurement, when SQL is the predominant interface, or when SageMaker integration via Redshift ML is sufficient for the ML roadmap.
This Databricks vs. Redshift comparison summarises the practical differences between the two options for enterprise buyers. The analysis covers pricing models, target customer size, deployment options, integration coverage, and customer-reported strengths. Use the related comparisons below to evaluate either product against other alternatives.