Weights & Biases
ML experiment tracking, model monitoring, and dataset versioning platform - used by OpenAI, Toyota, and 1,000+ organizations to ship better models faster.
Weights & Biases (W&B) is the leading MLOps platform for tracking machine learning experiments, versioning datasets and models, and monitoring deployed models in production. Teams log metrics, hyperparameters, and artifacts during training runs and compare results in interactive dashboards. The platform supports PyTorch, TensorFlow, Keras, Hugging Face, and all major ML frameworks. W&B Weave, launched in 2024, extends the platform to LLM application tracing and evaluation - tracking prompt inputs, model outputs, and latency across the entire LLM pipeline. Used by 1,000+ organizations including OpenAI, Toyota, Samsung, and L'Oréal.
Key Features
- Experiment tracking - log metrics, hyperparameters, gradients, and system usage during training
- Interactive comparison dashboards - compare unlimited runs across hyperparameter sweeps
- Model Registry - version and stage-gate models from experiment to production
- W&B Artifacts - version datasets, models, and evaluation sets with full lineage
- W&B Weave - trace and evaluate LLM application calls with full input/output logging
- Hyperparameter sweeps - automated grid, random, and Bayesian optimization across runs
- Team collaboration - share reports, comment on runs, and reproduce experiments from config
Use Cases
- ML engineers tracking training runs across GPU clusters to find the best model configuration
- Research teams reproducing and comparing experiments with full artifact versioning
- LLM teams tracing and evaluating prompt performance with W&B Weave
- Organizations managing model lifecycle from development through production deployment
Pros
- Free tier covers solo researchers and small teams with unlimited logged runs
- W&B Weave fills the LLM observability gap - the same team uses one platform for ML and LLM work
- Integration breadth covers every major ML framework - no code rewrites to adopt
Cons
- Teams pricing at $50/user/month becomes expensive as the team and usage scales
- The full platform surface area is large - teams often use 20% of features
- Data egress and large artifact storage can add costs on top of seat pricing
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