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usage-based
Costs scale with inference volume; serverless SQL analytics up to $0.70/DBU
annual
Requires Databricks platform subscription; DBU consumption billed separately
Pricing sourced from Databricks Mosaic AI Agent Framework · Last verified March 2026
Mosaic AI is part of the Databricks platform and uses DBU-based pricing. Foundation model serving starts at $0.07 per DBU. Serverless SQL for agent analytics runs up to $0.70 per DBU. Total cost depends on inference volume, retrieval frequency, and compute tier. There is no flat monthly agent fee. Contact Databricks sales for a cost estimate based on your expected workload.
No. The Agent Framework is tightly integrated with the Databricks Lakehouse, Unity Catalog, and Model Serving. It is not available as a standalone product. If you are evaluating agent frameworks without an existing Databricks investment, platforms like LangChain, CrewAI, or AWS Bedrock Agents have lower entry barriers.
Model Context Protocol (MCP) is a standard for connecting AI agents to external tools. Mosaic AI supports MCP as both client (your agents can call external tools) and server (external agents can access your Lakehouse). This enables multi-platform agent architectures where Databricks handles data-heavy reasoning while other systems handle actions like sending emails or updating CRMs.
Agent Evaluation lets you define quality criteria using three methods: rule-based checks (response length, format compliance), LLM-as-judge scoring (an LLM grades agent responses for accuracy and relevance), and human review (team members rate responses in an integrated UI). You run evaluation sets against agent versions and compare scores before deploying to production.
AI builders and operators use Databricks Mosaic AI Agent Framework to streamline their workflow.
Try Databricks Mosaic AI Agent Framework Now →The industry-standard framework for building production-ready LLM applications with comprehensive tool integration, agent orchestration, and enterprise observability through LangSmith.
Compare Pricing →Open-source Python framework that orchestrates autonomous AI agents collaborating as teams to accomplish complex workflows. Define agents with specific roles and goals, then organize them into crews that execute sequential or parallel tasks. Agents delegate work, share context, and complete multi-step processes like market research, content creation, and data analysis. Supports 100+ LLM providers through LiteLLM integration and includes memory systems for agent learning. Features 48K+ GitHub stars with active community.
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