tools
MLflow Review 2026 — Open-Source ML Experiment Tracking and LLM Evaluation
MLflow is the open-source ML lifecycle platform for experiment tracking, model registry, and LLM evaluation. Free Apache 2.0, integrates with every ML framework.
MLflow helps data scientists and AI engineers track experiments, package and register models, evaluate outputs, manage prompts, trace LLM applications, and deploy agents. It includes observability, an AI Gateway, model serving, and APIs for Python, TypeScript, Java, and R workflows.
The core project is free and self-hosted under the Apache 2.0 license. Self-hosting requires your own infrastructure; managed MLflow services are offered separately through MLflow Cloud and cloud providers.
Features
- Trace LLM applications and agents with OpenTelemetry-based observability
- Evaluate models, agents, prompts, and datasets with built-in metrics and judges
- Track experiments, parameters, metrics, artifacts, and model versions
- Register, package, serve, and deploy machine learning models
- Manage prompt versions with lineage tracking and prompt optimization
- Route model requests through an OpenAI-compatible AI Gateway
- Deploy agents with the FastAPI-based MLflow Agent Server
- Open-source under the Apache 2.0 license
Use cases
- Track machine learning experiments and compare model runs
- Evaluate chatbot and agent quality before production release
- Monitor production traces, latency, safety, usage, and costs
- Register and deploy models across different serving environments
- Govern prompts and model access across an AI engineering team
- Host and observe production agents through an API endpoint