MLflow vs Weights & Biases

Verdict

MLflow and Weights & Biases both do the core job of experiment tracking — logging parameters, metrics and artifacts from training runs so they can be compared and reproduced — and either one will serve a team well. The difference is in deployment default and polish, not in what gets logged.

MLflow is open source under the Linux Foundation, free, and framework-agnostic; it runs self-hosted on a tracking server you manage, or as a managed component inside Databricks and other platforms. It's the default choice for teams that want tracking without a vendor SaaS commitment, and its Model Registry and Projects components extend it into packaging and versioning, not just logging. W&B is hosted-first with interactive, shareable dashboards, hyperparameter sweeps, and a Registry/lineage layer, plus Weave for tracing and evaluating LLM applications and agents. A self-hosted deployment option exists, including a restricted free "Personal" tier and a paid "Advanced Enterprise" tier, for teams that need data to stay on their own infrastructure.

Choose MLflow if

• You want tracking with zero licensing cost and no vendor lock-in, running on infrastructure you already operate.
• You're already on Databricks, where MLflow is natively integrated.
• Your team is comfortable running and maintaining a tracking server rather than paying for a managed one.

Choose Weights & Biases if

• You want a more polished, collaborative dashboard experience out of the box, with less setup.
• Hyperparameter sweeps and shareable, interactive reports matter to how your team works day to day.
• You also want LLM/agent tracing (Weave) from the same vendor as your experiment tracker.

What they share

Both integrate with the standard ML frameworks (PyTorch, TensorFlow, Hugging Face), both offer a registry that tracks lineage from dataset and code version to model, and both have extended into LLM observability as an adjacent product rather than their original core. Neither is a production model-monitoring tool — for watching deployed models over time, see Arize AI vs Fiddler AI instead. See choosing an MLOps & experiment tracking tool for how tracking fits against monitoring, orchestration and serving.