Comparison
SageMaker vs Databricks: which platform for data and machine learning on AWS?
Amazon SageMaker and Databricks compared for machine learning and data on AWS: SageMaker AI versus the next-generation SageMaker with Unified Studio, the lakehouse and table formats, MLOps, governance, multi-cloud, cost and when each fits.
Hemant Walchale
· 3 min read
“SageMaker vs Databricks” means two different questions, because the SageMaker name changed. SageMaker AI is AWS’s managed machine learning service. The next-generation SageMaker, announced at the end of 2024, is a wider data and AI platform; its SageMaker Unified Studio became generally available in March 2025. Decide which comparison you are making first.
The short answer
- Choose Databricks if you want one consolidated platform for data engineering, SQL analytics and machine learning, with Unity Catalog governance, Spark and Delta at its core, and the option to run on AWS, Azure and Google Cloud.
- Choose SageMaker if you are committed to AWS and want to assemble the platform from AWS services (Glue, EMR, Athena, Redshift, Bedrock, SageMaker AI) under one studio, with AWS identity and billing throughout.
- Use both when Databricks is your data platform but a team already serves models on SageMaker AI, or wants specific AWS ML features.
How they compare
| Databricks | Amazon SageMaker | |
|---|---|---|
| What it is | One lakehouse platform for data, analytics and AI | A family of AWS services under one studio |
| Clouds | AWS, Azure, Google Cloud | AWS |
| Data engineering | Spark, Lakeflow pipelines and jobs | AWS Glue, Amazon EMR |
| SQL analytics | Databricks SQL warehouses | Amazon Athena, Amazon Redshift |
| Table format | Delta Lake, with Iceberg interoperability | Apache Iceberg in SageMaker Lakehouse |
| Governance | Unity Catalog | SageMaker Catalog, AWS Lake Formation, IAM |
| Model training | Notebooks, Spark, GPU clusters, serverless GPU | SageMaker AI training jobs, HyperPod |
| Experiment tracking | Managed MLflow | Managed MLflow in SageMaker AI, SageMaker Experiments |
| Model serving | Model Serving endpoints | SageMaker AI endpoints |
| Generative AI | Foundation model APIs, Agent Bricks, vector search | Amazon Bedrock |
| Pricing model | DBUs plus your cloud compute | Per service: instance hours, queries, storage |
What usually decides it
- Single cloud or several. Databricks runs the same way on all three major clouds. SageMaker is AWS only.
- One platform or best-of-breed services. Databricks is more consolidated; SageMaker gives more service-level choice and deeper AWS integration, with more pieces to configure.
- Where your data engineering lives. If heavy ETL and Spark already run on Databricks, keeping ML next to the data avoids copies. If data lives in Redshift and Glue, SageMaker keeps you inside that estate.
- Governance. Unity Catalog covers tables, files, models and features in one place. On AWS, governance spans SageMaker Catalog, Lake Formation and IAM.
- Skills. Teams fluent in AWS services move quickly in SageMaker; Spark and SQL teams move quickly in Databricks.
Cost
Neither is cheaper by default. Idle clusters and endpoints, oversized instances and copying data between services drive most of the bill on both. See our data platform FinOps guide and Databricks cluster policies for the controls that matter.
The short version
Databricks for one multi-cloud platform covering data and ML with unified governance; SageMaker for an AWS-native estate built from AWS services. Many organisations run both, with open table formats on S3 and MLflow in common.
Related: Databricks vs BigQuery, MLflow vs Kubeflow and what is Unity Catalog. Our AI-ready data service prepares data platforms for machine learning.
Sources
- Amazon SageMaker Unified Studio is now generally available, AWS
- The next generation of Amazon SageMaker, AWS Big Data Blog
- What is Unity Catalog?, Databricks documentation
Further reading from other practitioners
- Databricks vs AWS SageMaker: key differences, TrueFoundry
Not sure where your own platform stands? Take the free 3-minute data platform scorecard, or see how we work on Databricks. Unfamiliar terms are explained in the data engineering glossary.