MLflow vs Weights & Biases

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	MLflow Evaluation	Weights & Biases Evaluation
Tagline	Open-source platform for tracking, evaluating, and deploying ML models and LLM applications.	The ML experiment tracker, now with LLM eval features.
Category	Evaluation	Evaluation
Pricing	Free· Free and open source (Apache 2.0); managed offering via Databricks	Freemium· Free personal; team from $50/mo per seat
Model	Multi-model	Platform (any LLM)
Editorial score	—	8.4 / 10
Use cases	llm-evaluationexperiment-trackingprompt-managementagent-observabilitymodel-registry	ML experimentsLLM evalWeave
Pros	Fully open source under Apache 2.0 with no usage caps Covers eval, tracing, prompts, and registry in one tool Massive ecosystem with 100+ integrations including LangChain and OpenAI Multi-language SDKs (Python, TS, Java, R) Battle-tested at Fortune 500 scale	Industry-standard for ML tracking Weave adds LLM-native eval Mature, reliable Strong enterprise features
Cons	Self-hosting and ops burden unless you pay for Databricks UI feels engineering-first rather than polished LLM features layered onto a classical-ML core can feel bolted-on	Heavier UX than LLM-native tools LLM features still catching up
Website	mlflow.org	wandb.ai

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