Databricks Named a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms

We are excited to announce that for the fourth consecutive time, Gartner has recognized Databricks as a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms. Databricks has received the highest position in Ability to Execute and the furthest position in Completeness of Vision.

Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling. These platforms support the independent use and collaboration among data scientists and their business and IT counterparts, with automation and AI assistance through all stages of the data science life cycle, including business understanding, data access and preparation, model creation and sharing of insights. They also support engineering workflows, including the creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances or as a fully managed cloud offering.

Download a complimentary copy of the report here.

We are thrilled about this recognition from Gartner and believe it is due to the success of the thousands of Databricks customers who have built and deployed high-quality AI projects into production. For many years, enterprises have struggled to put their data science and machine learning projects into production. GenAI has only made it more difficult because AI foundation models are not aware of enterprise data and fail to deliver business-specific, accurate, and well-governed outputs.

At Databricks, our focus has been to help enterprises build and deploy AI in high-value, mission-critical applications while ensuring accuracy, governance, and ease of use. Our innovation pillars are:

• AI Agents that reason over your data: Databricks provides the most efficient and secure way to connect your enterprise data to agents. With the AI platform built on the lakehouse, there is no need to duplicate data. This makes it easy to customize AI models with your data.
• Custom evaluation for your use case: Databricks offers a built-in evaluation for agents. You can evaluate and use any combination of open source and commercial GenAI models, as well as ML models for your AI Agents. We help you measure the output quality of the agents and give you robust ways to trace the root cause, evaluate fixes, and redeploy quickly to improve quality.
• Unified governance across data, AI models, and tools: Customers can govern and apply guardrails across all AI models, including those hosted outside of Databricks. We automatically enforce proper access controls, set rate limits to manage costs, prevent harmful content, and track lineage throughout the entire AI workflow from data to models.

Databricks on Databricks

At Databricks, we’re big proponents of using our own technology internally. Interestingly, the tools being evaluated in this Magic Quadrant report were the tools we leveraged to complete our Magic Quadrant questionnaire. Anyone who has worked on a Magic Quadrant knows that the questionnaires are incredibly rigorous and require ample time from stakeholders across the company. Leveraging the Databricks Data Intelligence Platform, we built our own custom knowledge base AI agent named ARIA - Analyst Relations Intelligent Assistant - to write high-quality and high-accuracy first drafts for nearly 700 pages worth of technical product questions. This saved the team tens of collective hours of writing time and enabled our leadership team to focus on more high-value, strategic components of the evaluation.

ARIA is built on a Retrieval-Augmented Generation (RAG) architecture, wrapped in a user-friendly Streamlit interface and hosted on Databricks Apps. It ingests RFI documents in HTML format, extracts questions, and generates high-quality responses using Mosaic AI Agent Framework, Vector Search, and batch inference with Claude 3.7-Sonnet. The system leverages prior Q&A pairs, Databricks documentation, and a product-to-keyword mapping table to enhance search relevance. DSPy is used for prompt optimization to ensure consistency in tone and format, and the final output is exportable to Google Docs or Excel for collaboration.

What's next

We believe our recognition as a Leader with the highest scores for Ability to Execute and Completeness of Vision is a testament to our ability to bring together teams and enable them to create the next generation of data and AI applications with quality, speed, and agility. 

As a leader across multiple Magic Quadrants and other analyst reports, we believe the uniqueness of the achievement is in how it was accomplished. It is not uncommon for vendors to show up in multiple Magic Quadrants each year across many domains. But, they are assessed on disparate products in their portfolio that individually accomplish the specific criteria of the report. Databricks’ results show definitively that you can be a leader with a unified approach to Data + AI, with one copy of data, one processing engine, one approach to management and governance that’s built on open source and open standards across all clouds. 

With a single solution, you can deliver class-leading outcomes for data warehousing and data science/machine learning workloads. We believe that ML and GenAI will continue to transform data platforms, and we thank our customers and partners for joining us on this journey.

Learn more

To learn more about Mosaic AI, visit our website and follow @Databricks for the latest news and updates. You can also join us at the Data + AI Summit 2025, where we will make significant announcements across our innovation pillars for AI. 

Read the Gartner Magic Quadrant for Data Science and Machine Learning Platforms.

Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, Afraz Jaffri, Maryam Hassanlou, Tong Zhang, Deepak Seth, Yogesh Bhatt, May 28 2025.

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