Why it matters
- The de facto standard for ML experiment tracking — if you're training models, wandb is the tool researchers use.
- Used by top research labs (DeepMind, OpenAI, NVIDIA, Meta AI) and a half million developers — the results are reproducible and shareable.
- W&B Weave extends the platform to LLM applications, adding tracing, evals, and prompt management to a system teams already use for model training.
- Integrates with virtually every ML framework in one SDK —
pip install wandband you're logging in minutes.
Key capabilities
- Experiment tracking: Log training metrics (loss, accuracy), hyperparameters, and hardware stats automatically per run.
- Visualizations: Interactive charts, confusion matrices, media logs (images, audio, 3D point clouds) in the dashboard.
- Artifact versioning: Version and share datasets, models, and evaluation results with full lineage tracking.
- Model registry: Centralized repository for model versions with staging, production, and deployment tracking.
- Hyperparameter sweeps: Automated hyperparameter optimization (Bayesian, grid, random) with parallel run management.
- W&B Weave (LLMs): Trace LLM calls, version prompts, run evaluations, and analyze LLM outputs across model versions.
- Reports: Collaborative, shareable reports combining charts, text, and media — used for ML research sharing.
- Integrations: PyTorch, TensorFlow, Keras, Hugging Face, scikit-learn, XGBoost, Spark, Ray, and 50+ more.
Technical notes
- SDK: Python (
pip install wandb); JavaScript/TypeScript and CLI also available - Platforms: SaaS cloud (wandb.ai); W&B Server (self-hosted on Kubernetes or Docker)
- Storage: 100GB free artifact storage; Teams includes more; Enterprise unlimited
- Pricing: Free (individuals, unlimited experiments, 100GB); Teams $50/user/mo; Enterprise custom
- Data residency: US cloud by default; European region available; self-hosted for full data control
- Founded: 2017 by Lukas Biewald, Chris Van Pelt, and Shawn Lewis; San Francisco
Ideal for
- ML teams and researchers who train models and need reproducible, comparable experiment records.
- Organizations building LLM applications who want a single platform for both model training and LLM evaluation.
- Teams using Hugging Face Trainer, PyTorch Lightning, or Keras who want automatic integration with zero config.
Not ideal for
- Pure LLM/prompt engineering teams who only need LLM tracing — LangSmith or LangFuse may be simpler.
- Teams on tight budgets with large teams — $50/user/month can add up; MLFlow (open source) is free.
- Projects that don't involve ML training — if you're only doing inference/prompting, W&B adds overhead.
See also
- LangSmith — LangChain's LLM debugging and evaluation platform.
- LangFuse — Open-source LLM observability and tracing.
- Braintrust — LLM evaluation and experimentation platform.