What is MLflow used for?

MLflow is an open-source platform for debugging, evaluating and monitoring AI agents, LLM applications and machine learning models. It is a good fit for debugging agent behavior from captured traces, tracking experiments and managing ML models, and governing LLM access across a team.

Is MLflow open source?

Yes. MLflow is open source under the Apache-2.0 licence. Its source code is on GitHub at mlflow/mlflow and is written mainly in Python.

Yes. MLflow is open source, so the software itself is free to use. A managed cloud version is also available.

Can I self-host MLflow?

Yes. MLflow can be self-hosted on your own server or infrastructure.

What is MLflow an alternative to?

MLflow is an open-source alternative to Weights & Biases, Amazon SageMaker, DataRobot and Dataiku. Other open-source alternatives to Weights & Biases include Opik, Langfuse and Arize Phoenix.

Is MLflow actively maintained?

Yes. The most recent commit to MLflow was on 2 October 2026, and the latest release is v3.16.1, published on 17 September 2026. The project has 28k stars on GitHub.

MLflow: open-source alternative to Weights & Biases

About MLflow

MLflow is an open-source AI engineering platform for agents, LLM applications and ML models. It helps teams debug, evaluate, monitor and optimize AI applications in production while keeping control of costs and access to models and data. It is written in Python, released under the Apache-2.0 license, and has been developed since 2018, with topics covering MLOps, model management and LLMOps.

For LLM and agent work it offers production observability with traces, plus evaluation, prompt management and prompt optimization, and an AI Gateway that governs costs and model access. It works with Python, TypeScript and JavaScript, Java and other languages, and integrates with OpenTelemetry and MCP. Getting started means starting an MLflow server, enabling logging in your code and running it, then exploring traces and metrics in the web UI on port 5000. A setup wizard can connect to an MLflow server or a Databricks workspace and add tracing with a coding agent.

Key features

Tracing and observability for LLM apps and agents
Evaluation tools for models and agents
Prompt management and prompt optimization
AI Gateway for cost and model access control
OpenTelemetry and MCP integration
Web UI for exploring traces and metrics

Good fit for

→Debugging agent behavior from captured traces
→Tracking experiments and managing ML models
→Governing LLM access across a team

Built with: Python
Tags: mlops; llmops; machine-learning; observability; evaluation; ai-gateway; tracing; python

MLflow: questions and answers

What is MLflow used for?: MLflow is an open-source platform for debugging, evaluating and monitoring AI agents, LLM applications and machine learning models. It is a good fit for debugging agent behavior from captured traces, tracking experiments and managing ML models, and governing LLM access across a team.
Is MLflow open source?: Yes. MLflow is open source under the Apache-2.0 licence. Its source code is on GitHub at mlflow/mlflow and is written mainly in Python.
Is MLflow free?: Yes. MLflow is open source, so the software itself is free to use. A managed cloud version is also available.
Can I self-host MLflow?: Yes. MLflow can be self-hosted on your own server or infrastructure.
What is MLflow an alternative to?: MLflow is an open-source alternative to Weights & Biases, Amazon SageMaker, DataRobot and Dataiku. Other open-source alternatives to Weights & Biases include Opik, Langfuse and Arize Phoenix.
Is MLflow actively maintained?: Yes. The most recent commit to MLflow was on 2 October 2026, and the latest release is v3.16.1, published on 17 September 2026. The project has 28k stars on GitHub.