Databricks MLflow / Mosaic AI

A Mosaic AI product for building production-ready AI agents that continuously improve quality and accuracy, optimized on enterprise data. Designed to help teams deploy agents grounded in their own data.

Build production-quality AI agents and assess output quality with AI-assisted evaluations and human-in-the-loop feedback via an intuitive UI.

A highly performant vector database with real-time syncing of source data, designed for RAG and semantic search applications.

Scalable monitoring that detects anomalies, tracks freshness, and delivers consistent quality signals across AI and data assets.

Full-featured unified platform integrating data and AI tools across the organization, including lakehouses, analytics, and agentic capabilities.

Open, intelligent data warehouse built on lakehouse architecture combining the reliability of traditional warehouses with the scale and governance of data lakes.

Collaborative notebooks that boost team productivity with real-time collaboration and streamlined data science workflows.

A serverless data warehouse built on the lakehouse architecture. Provides SQL analytics with built-in governance and AI capabilities.

A data-aware AI partner for conversational analytics, enabling natural language dashboard creation and deep conversational data exploration. Provides unified search, chat, dashboards and apps for business productivity.

Serverless Postgres database integrated with the lakehouse, built for AI agents and applications. It merges Postgres reliability with lakehouse-scale governance and analytics, enabling transactional workloads that sync seamlessly to analytics and AI systems.

A data engineering tool for building reliable ETL pipelines for batch and streaming workloads. Enables ingestion, transformation, and orchestration of data at scale.

An open agentic SIEM built for the AI era, providing security information and event management capabilities on the Databricks platform.

Enterprise-grade extension of open source MLflow providing a unified MLOps platform for building models and generative AI apps with reliability, security, and scalability.

An open-source platform for managing the machine learning lifecycle, including experiment tracking, model registry, and deployment.

Unified deployment platform for agents, GenAI, and classical ML models, providing scalable inference infrastructure.

Fine-tune open source LLMs, pretrain custom LLMs, or build classical ML models with enterprise-grade tools and infrastructure.

Fine-tune open source LLMs, pretrain custom LLMs, or build classical ML models using Databricks infrastructure.

Databricks' AI platform for building, deploying, and managing generative AI applications and agents.

A single place to apply data governance across every LLM and MCP in the enterprise, providing unified control over AI model access.

A unified governance layer for managing compliance and access control across data, ML models, dashboards, and AI agents on a single platform.

A highly performant vector database with real-time syncing of source data, enabling semantic search for AI applications.