Develop data and AI applications with speed and confidence
Databricks provides an integrated environment for building low-latency apps and agents directly on your enterprise data. Stay flexible with open standards and use your preferred frameworks.
Databricks Apps
Use the open frameworks and tools you already love to create secure, interactive UIs. Databricks Apps provides a managed serverless runtime with built-in auth and governance, eliminating the need for manual infra management or data movement.
Build, deploy and orchestrate AI agents that run on your enterprise data. Combine models, retrieval and tools in a unified environment, with agents grounded in your business logic. Ship them as Databricks Apps and persist memory and state in Lakebase.
Build apps on a serverless Postgres engine that lives inside your lakehouse. Eliminate data silos and ETL pipelines with instant branching for testing and agent experimentation. One storage layer for both apps and analytics means no more synchronization bugs.
An agentic app goes beyond static RAG by using Agent Bricks to reason, plan and execute tasks. While a standard app might only retrieve data, an agentic app can use tools to perform actions like updating a record in Lakebase, triggering a workflow or calling an external API.
Yes. Databricks is an open platform. While we provide Agent Bricks for a streamlined, production-ready experience with built-in observability, you can deploy any framework (LangChain, AutoGPT, Pydantic AI) within the Databricks Data + AI Platform. You get the same serverless benefits and secure access to your data regardless of the library you choose.
When an agent uses a tool to access data or perform an action, it inherits the identity and access controls of the user.
Databricks provides a native MCP server. When you use an AI-native IDE like Cursor or Claude Code, the IDE can query your lakehouse data. This means your AI assistant knows your table schemas, column types and even your agentic tool definitions, allowing it to write accurate SQL and Python without you copy-pasting schemas.
Databricks provides a native MCP server. When you use an AI-native IDE like Cursor or Claude Code, the IDE can query your lakehouse data. This means your AI assistant knows your table schemas, column types and even your agentic tool definitions, allowing it to write accurate SQL and Python without you copy-pasting schemas.
Start building on the Databricks Data + AI Platform today
Try Databricks with a free trial or get started for personal use – then deploy your agentic apps with governed data access, evaluation and production workflows