Side-by-Side Comparison · 2026

Amazon SageMaker vs Databricks: Full Comparison (2026)

Amazon SageMaker is aws's fully managed platform to build, train, and deploy ml models at scale. Databricks is unified analytics and ai platform for enterprise data teams. Use the breakdown below to find the right fit for your needs.

Amazon SageMaker

Amazon Web Services

Paid
vs

Databricks

Databricks

Enterprise
This page presents factual information sourced from publicly available vendor documentation and product pages. AIHub does not endorse either product. The right tool depends on your specific use case, team, and requirements — we recommend evaluating both tools directly before making a decision.

Side-by-Side Overview

Pricing Model
Amazon SageMaker: PaidDatabricks: Enterprise
API Access
Amazon SageMaker: AvailableDatabricks: Not available
Platforms
Amazon SageMaker: AWS Console, SageMaker Studio, Python SDKDatabricks: Web
Integrations
Amazon SageMaker: 8 integrationsDatabricks: —
Vendor
Amazon SageMaker: Amazon Web ServicesDatabricks: Databricks
Category
Amazon SageMaker: PlatformsDatabricks: Infrastructure
Launch
Amazon SageMaker: Nov 29, 2017Databricks: —

About Amazon SageMaker

Amazon SageMaker is a fully managed machine learning service that covers the complete ML lifecycle: data labeling, feature engineering, model training, hyperparameter tuning, model evaluation, deployment, and monitoring. SageMaker JumpStart provides one-click access to hundreds of foundation models including Llama, Falcon, and Stable Diffusion.

Designed For

  • Enterprise ML model development
  • Foundation model fine-tuning
  • Batch ML inference
  • ML experiment management
Full Amazon SageMaker details

About Databricks

Databricks provides a lakehouse platform combining data engineering, ML, and analytics. Features MLflow for experiment tracking, Delta Lake for reliable data, and Dolly open-source LLMs.

Designed For

  • ML model training
  • Data pipelines
  • Feature engineering
  • Model serving
Full Databricks details

Strengths & Limitations

Amazon SageMaker

Strengths

  • Deep AWS integration
  • Comprehensive end-to-end platform
  • SageMaker JumpStart (foundation models)
  • Auto-scaling
  • Managed notebooks

Limitations

  • Complex pricing
  • Steep learning curve
  • AWS vendor lock-in
  • Can be expensive

Databricks

Strengths

  • Unified platform
  • MLflow integration
  • Scalable infrastructure

Limitations

  • Complex and expensive
  • Overkill for small teams

Frequently Asked Questions

What is the difference between Amazon SageMaker and Databricks?

Amazon SageMaker is aws's fully managed platform to build, train, and deploy ml models at scale, while Databricks is unified analytics and ai platform for enterprise data teams. Amazon SageMaker is designed for Enterprise ML teams, Data scientists at AWS-centric companies; Databricks is designed for Infrastructure. The right fit depends on your specific requirements.

How do the pricing models compare?

Amazon SageMaker is available under a Paid model. Databricks is available under a Enterprise model. Amazon SageMaker's entry tier starts at From $0.05/hr (t2.medium). Always verify pricing on each vendor's official website as it may change.

What integrations does each tool support?

Amazon SageMaker integrates with Amazon S3, Amazon EKS, Amazon Redshift, Apache Spark. Databricks integrates with various tools. Check each vendor's documentation for the full and current list.

How do I choose between Amazon SageMaker and Databricks?

Consider your team's technical requirements, budget, existing tooling, and use case before deciding. We recommend signing up for free trials or demos of both tools where available, and consulting each vendor's documentation. AIHub provides this comparison for informational purposes only.