Compare Weights & Biases alternatives
Weights & Biases (W&B) is a widely used MLOps platform for tracking machine learning experiments, hyperparameter sweeps, and model artifacts. It's closed-source and cloud-hosted by default — the free tier is generous for individuals, but team usage and self-hosted deployments require paid enterprise plans, and your experiment data is stored on W&B's servers unless you pay for that option.
mlop is the most direct alternative since it specifically offers a W&B-compatible API, making migration low-friction if you're already using W&B's SDK. Langfuse and Helicone are worth considering instead if your actual need is tracing and observability for LLM applications rather than classic ML experiment tracking, and Laminar if agent evaluation is your primary use case.
FAQ
Is there a free, open-source alternative to Weights & Biases?
Yes. mlop, Langfuse, and Helicone are all open source and free to self-host.
What's the best open-source alternative to Weights & Biases?
mlop is the closest match since it mirrors W&B's API, easing migration. Langfuse is the better pick if you're tracking LLM application behavior rather than traditional model training runs.
What are the top open-source alternatives to Weights & Biases?
Browse all open-source alternatives to Weights & Biases above, or explore the full Developer Tools category for more options.