MLflow
Open-source AI engineering platformTrack, evaluate, and ship AI apps, free
Highlights
What you can do with it
- Manage LLM app development with tracking and evaluation to iterate faster
- Record machine learning experiments for research and team sharing
- Deploy AI apps through the gateway with cost control and routing
How to use it
- 1 Start the MLflow server with a single command, or use Docker.
- 2 Add a few lines of code to begin logging tracking, metrics, and parameters.
- 3 Run your code, and MLflow records the data for debugging and evaluation.
Who it is for
Pros
- Open source under Apache 2.0 and permanently free
- No vendor lock-in; works with any cloud, framework, or tool
- Covers tracking, evaluation, prompts, gateway, and deployment in one place
Limitations
- It is developer-focused, so there is a learning curve to set up
- Running your own server and infrastructure is your responsibility
- Feature breadth means more moving parts to configure and maintain
What MLflow is
MLflow is an open-source AI engineering platform supported by the Linux Foundation. According to the site, it has been open source for about five years and is used by thousands of organizations and research teams worldwide. Its purpose is to simplify the development of AI applications so teams can focus on building products rather than managing infrastructure. It is licensed under Apache 2.0 and described as permanently free, with no vendor lock-in, meaning it works with any cloud, framework, or tool and you can switch providers freely. The platform is production-ready and aimed at teams of all sizes.
What you can do with it
- Track and observe LLM applications and agents in detail.
- Run evaluations with built-in metrics and LLM judges to catch regressions.
- Version, test, and optimize prompts with full lineage tracking.
- Route requests across providers through a gateway and deploy agents with one command.
Who it is for
- AI development teams that want a smoother workflow.
- Research institutions that need to record and share experiments.
- Enterprises deploying AI apps with cost and routing control.
What to watch out for
MLflow is a developer tool, so there is a real learning curve, and you or your team will need to run and maintain the server yourself. Its wide feature set, covering tracking, evaluation, prompts, gateways, and deployment, means there are many moving parts to configure, which can be heavy for a small project. Being free and open source does not remove the operational work: hosting, scaling, and security stay on you. The site cites an active community and broad adoption, but specific results will depend on your own setup and use case.
FAQ
Is MLflow free to use?
Yes, it is open source under the Apache 2.0 license and the site describes it as permanently free.
Does it lock me into one cloud or vendor?
No, the site says there is no vendor lock-in and it works with any cloud, framework, or tool.
What can it track?
It tracks LLM apps and agents, experiments, metrics, prompts, and model lifecycle, with evaluation tools built in.
Why it is free
What the official page says： 永久免费免费
Free limits, trial length and features are decided by the official site and can change at any time.
Product preview
Screenshot archived from the listing page, for preview only.
