Weights & Biases
AI developer platform for experiment tracking, model management, and LLM observability used by OpenAI, NVIDIA, and thousands of ML teams
Overview
Weights & Biases (W&B) is the leading AI developer platform for machine learning experiment tracking, model management, and LLM observability. Founded in 2017, W&B provides tools to track, compare, and visualize ML experiments at scale. The platform is used by OpenAI for training GPT models, NVIDIA for deep learning research, and thousands of ML teams at companies like Toyota, Samsung, and Lyft. W&B's core products include Experiments (tracking), Sweeps (hyperparameter tuning), Artifacts (dataset/model versioning), and Weave (LLM-specific observability and evaluations). With over $250M in funding at a $1.25B+ valuation, W&B has become the de facto standard for ML experiment tracking in both research and production environments.
The Verdict
Who Should Use Weights & Biases?
Best For
- ML teams doing heavy experimentation
- Research labs training large models
- Organizations needing model lineage tracking
- Teams migrating from TensorBoard at scale
- LLM developers needing end-to-end observability
Not Ideal For
- Simple LLM apps (Langfuse is lighter)
- Self-hosted requirements (limited options)
- Teams on tight budgets (MLflow is free)
- Pure LangChain workflows (LangSmith more native)
What's Great
- Best-in-class experiment tracking and visualization
- Seamless integration with PyTorch, TensorFlow, Keras
- Real-time collaborative dashboards
- Robust artifact versioning for models & datasets
- Hyperparameter sweep automation (Bayesian, grid, random)
- Enterprise-proven - used by OpenAI, NVIDIA, Microsoft
- Weave provides LLM-specific tracing and evals
Watch Out For
- Pricing can escalate with heavy usage
- Self-hosted option is enterprise-only
- Learning curve for full platform utilization
- Weave (LLM product) newer than pure LLM tools
- Some features require Teams tier or higher
Pricing
View all features & details
Core Products
- Experiments - Track & visualize ML runs
- Sweeps - Hyperparameter optimization
- Artifacts - Dataset & model versioning
- Tables - Interactive data visualization
- Reports - Collaborative documentation
- Weave - LLM tracing & evaluations
- Launch - ML job orchestration
- Model Registry - Production model management
Framework Integrations
- PyTorch / PyTorch Lightning
- TensorFlow / Keras
- Hugging Face Transformers
- scikit-learn
- XGBoost / LightGBM
- JAX / Flax
- OpenAI / Anthropic SDKs
- LangChain / LlamaIndex
LLM Features (Weave)
- LLM call tracing & spans
- Prompt versioning & management
- LLM-as-judge evaluations
- Cost tracking per request
- Latency analytics
- RAG pipeline debugging
- Agent workflow visualization
Enterprise & Security
- SOC 2 Type II certified
- HIPAA compliant
- SSO (SAML, OIDC)
- Self-hosted deployment
- Private cloud options
- RBAC & team permissions
- Audit logging
Enterprise Adoption
Notable Customers
- OpenAI - GPT model training
- NVIDIA - Deep learning research
- Microsoft - Azure ML integration
- Toyota Research Institute
- Samsung AI Center
- Lyft, Shopify, Instacart
Platform Scale
- 1M+ users globally
- 30,000+ teams
- 500+ enterprise customers
- 50,000+ ML models tracked daily
- $250M+ total funding
- $1.25B+ valuation (2023)
How It Compares
| Feature | W&B | MLflow | Langfuse | Langsmith |
|---|---|---|---|---|
| Open Source | Partial (SDK) | Yes (Apache 2) | Yes (MIT) | No |
| Self-Hosted | Enterprise only | Free | Free | No |
| Experiment Tracking | Best-in-class | Good | Basic | Basic |
| LLM Observability | Weave product | Via plugins | Native focus | Native focus |
| Hyperparameter Tuning | Sweeps built-in | Via Optuna | No | No |
| Model Registry | Production-ready | Good | No | No |
| Free Tier | 100GB storage | Unlimited | 50K obs/mo | 5K traces/mo |
| Enterprise Proven | OpenAI, NVIDIA | Databricks | Growing | LangChain |
| Best For | Full ML lifecycle | Self-hosted MLOps | LLM-first teams | LangChain users |