MLflow

What is MLflow?

MLflow is an open-source AI engineering platform for agents, LLM applications and traditional machine learning models. It covers experiment tracking, tracing, evaluation, prompt management and model deployment, and is released under the Apache 2.0 license.

Key capabilities of MLflow

• Observability: Capture complete traces of LLM apps and agents using OpenTelemetry, across any provider or agent framework.
• Evaluation: Run assessments with 50+ built-in metrics and LLM judges.
• Prompt management: Version, test and deploy prompts with lineage tracking.
• AI Gateway: OpenAI-compatible gateway with route management, rate limiting and cost control.
• Experiment tracking: Log parameters, metrics and artifacts for classic ML runs.

How MLflow works

Teams add MLflow to their Python, TypeScript/JavaScript, Java or R code, and it records traces, metrics and artifacts to a tracking server they run themselves. Evaluation jobs score outputs with built-in metrics or LLM judges, while the Agent Server deploys agents through FastAPI with automatic validation and built-in tracing.

Who uses MLflow?

Data scientists, ML engineers and AI platform teams use MLflow to keep experiments reproducible and to debug LLM agents in production. The project reports 900+ contributors and 30+ million monthly downloads.

MLflow pricing

MLflow itself is free and open source. The vendor site does not list managed hosting prices, so hosted MLflow costs depend on the platform you run it on and your own infrastructure.

MLflow alternatives

Weights and Biases focuses on hosted experiment tracking, Langfuse is centered on LLM observability, and Hugging Face covers model hosting and sharing. MLflow differs by combining ML tracking, LLM tracing and a gateway in one Apache 2.0 project.