Executive SummaryUpdated on Apr 20, 2025
Databricks and Amazon SageMaker compete in the data analytics and machine learning platform category. Databricks seems to have the upper hand in terms of flexible integration and performance optimization features.
Features: Databricks offers scalable Spark clusters, a versatile notebook environment, and support for multiple programming languages, enhancing big data and machine learning capabilities. Delta data format ensures fast performance and built-in optimization recommendations streamline analytics. Amazon SageMaker provides excellent AWS integration, diverse built-in algorithms, and features like Autopilot for automated processes, serverless execution, and easy API deployment, catering to non-coding users effectively.
Room for Improvement: Databricks should improve Git integration, expand machine learning libraries, and provide advanced data visualization. Additional cloud platform support and better BI tool integration are needed alongside more intuitive notebook usability. SageMaker could improve its pricing model, enhance documentation, and provide better GPU model integration and hyperparameter tuning suggestions.
Ease of Deployment and Customer Service: Databricks is mainly deployed on public clouds, offering flexibility with hybrid setups, though support can be inconsistent. SageMaker is available on public clouds, with satisfactory technical support, though users seek more comprehensive documentation and timely responses.
Pricing and ROI: Databricks pricing varies, with a pay-as-you-go model benefiting cloud users, providing positive ROI due to efficient data processing. SageMaker faces criticism for high costs, especially with adding resources, but costs are often justified when fully utilizing the AWS ecosystem.
To learn more, read our detailed
Amazon SageMaker vs. Databricks Report (Updated: April 2025).
