MLflow

MLflow is the open-source AI engineering platform for managing the full lifecycle of machine learning and generative AI applications. It helps teams track experiments, evaluate and monitor agents and LLM applications, manage models and prompts, and keep quality and costs under control in production.

Overview

MLflow has more than 28,400 stars on GitHub and is one of the most widely adopted tools in the ML ecosystem, maintained by Databricks together with a large open-source community and updated continuously. It began as an experiment tracking system and has grown with the field: today it covers LLM evaluation, agent observability with tracing, prompt management, AI governance and classic model registry workflows in one platform. MLflow works with any framework or provider (LangChain, OpenAI, and hundreds of integrations), runs anywhere from a laptop to a large cluster, and stores everything in open formats so teams are not locked in. For organizations shipping GenAI features, it has become standard infrastructure.

Key Features

• Experiment tracking for parameters, metrics, artifacts and model versions
• LLM and agent evaluation with built-in and custom scorers
• End-to-end tracing and observability for GenAI applications in production
• Prompt registry and AI gateway features for governed model access
• Model registry with versioning, aliases and deployment workflows
• Framework-agnostic: works with PyTorch, scikit-learn, LangChain and more
• Free and open source under the Apache-2.0 license

💳 Pricing

MLflow is free and open source under the Apache-2.0 license. Enterprises can also buy managed MLflow as part of the Databricks platform, but the open-source edition is complete and unrestricted.

Who Is It For

MLflow is for ML engineers, data scientists and GenAI teams who need to track experiments, evaluate agent and LLM quality, and operate AI applications in production with proper observability and governance.