MLflow

✓ Editorially verified

Open-source platform for tracking, evaluating, and deploying ML models and LLM applications.

Free· Free and open source (Apache 2.0); managed offering via DatabricksEvaluationMulti-model

Best for

Pick MLflow if you want a self-hosted, vendor-neutral home for LLM traces, evals, and prompts without per-event SaaS pricing.

Skip if

Skip it if you want a zero-ops hosted observability product with a polished UI and don't mind paying LangSmith or Langfuse Cloud.

MLflow is an Apache 2.0 licensed AI engineering platform that started as a classical ML experiment tracker and has expanded into one of the most widely adopted open-source stacks for LLM and agent observability. It handles experiment tracking, model registry, prompt versioning, OpenTelemetry-based tracing, and systematic evaluation with 50+ built-in metrics and LLM-as-judge scorers across correctness, relevance, latency, and safety dimensions.

It's aimed at ML and platform engineers who want to run evals and observability on their own infrastructure rather than pay for a SaaS observability vendor. The free, self-hosted nature is the main draw: no per-trace pricing, no enterprise paywall, and SDKs in Python, TypeScript, Java, and R. The ecosystem is huge, with 20,000+ GitHub stars and integrations with LangChain, OpenAI, PyTorch, and ~100 other tools.

The newer additions, an AI Gateway for unified LLM provider access and an Agent Server with FastAPI hosting, push MLflow beyond pure tracking into runtime infrastructure. Hosted versions exist via Databricks if you don't want to operate it yourself, but the open-source server runs fine on a single VM for small teams.

Editor's take

MLflow is the safe, boring, durable choice for ML and LLM tracking, and that's the compliment. The eval and tracing additions are genuinely competitive with the hosted observability vendors, and the price (zero) is hard to beat if you have anyone on staff who can run a Postgres-backed service.

— The AI Tool Bible editorial team