Weights & Biases
AI experiment tracking and MLOps platform
Weights & Biases (W&B) is a leading MLOps platform that provides experiment tracking, model management, and AI application evaluation tools for machine learning teams. With W&B Weave for LLM application development and W&B Models for traditional ML workflows, the platform helps teams coordinate all aspects of the ML lifecycle from training through deployment.
Key Capabilities
W&B offers comprehensive experiment tracking with real-time dashboards, hyperparameter sweep management, and automatic logging of metrics, code, and system resources. W&B Weave provides evaluation, monitoring, and iteration tools for LLM applications and AI agents. The platform includes artifact versioning for datasets and models, collaborative reports for sharing findings, and integrations with all major ML frameworks. Teams can track training runs, compare experiments side-by-side, and reproduce results with full lineage tracking.
Who Should Use Weights & Biases
W&B is ideal for ML engineers and research teams who need robust experiment tracking and collaboration tools. It serves individual researchers exploring models as well as enterprise teams managing complex ML pipelines. Academic institutions can access a free Pro license for research purposes.
Getting Started
Sign up for a free account at wandb.ai and install the Python client with pip. Add just a few lines of code to your training script to start logging experiments. W&B integrates natively with PyTorch, TensorFlow, Keras, Hugging Face, and many other frameworks, making adoption straightforward.
Pricing & Accessibility: W&B is free for personal projects with unlimited tracking hours and 100GB cloud storage. The Pro plan costs $50/user/month with enhanced storage and team features. Academic institutions receive free Pro licenses with 200GB storage and up to 100 seats.
Why Consider Weights & Biases: W&B has become the industry standard for experiment tracking, offering an intuitive interface, extensive framework integrations, and a generous free tier that makes it accessible for individuals while scaling to enterprise teams.
Pros
- Industry-leading experiment tracking with intuitive visualization dashboards
- Generous free tier with unlimited tracking hours for personal projects
- Extensive integrations with all major ML frameworks and cloud platforms
- Free Pro license for academic institutions with up to 100 seats
- W&B Weave adds powerful LLM application evaluation and monitoring
Cons
- Pro plan at $50/user/month can be expensive for growing teams
- Cloud storage limits on free tier may require paid upgrade for large projects
- Learning curve for advanced features like sweeps and artifacts
Who is this for?
Tracking and comparing ML experiments across team members, managing hyperparameter sweeps for model optimization, versioning datasets and model artifacts, evaluating and monitoring LLM applications with Weave, creating collaborative reports to share research findings
Frequently Asked Questions about Weights & Biases
Is Weights & Biases free for individual use?
Yes, W&B offers a free tier for personal projects that includes unlimited tracking hours, unlimited teams and projects, and 100GB of cloud storage. Academic researchers can access a free Pro license with enhanced features and up to 100 seats.
What ML frameworks does W&B integrate with?
W&B integrates natively with PyTorch, TensorFlow, Keras, Hugging Face Transformers, JAX, scikit-learn, XGBoost, LightGBM, and many more. It also supports custom integrations through its Python SDK.
What is the difference between W&B Models and W&B Weave?
W&B Models focuses on traditional ML workflow management including experiment tracking, hyperparameter sweeps, and model registry. W&B Weave is designed for LLM application development, providing evaluation, monitoring, and iteration tools for AI agents and LLM-based systems.
Pricing
freemium
$50/user/month
Free tier: Unlimited tracking hours, 100GB cloud storage, personal projects
Details
APIYes
Open SourceNo
CollaborationYes
LanguagesPython
Learning CurveEasy to Moderate
Integrations
PyTorchTensorFlowHugging FaceKerasJAX+4 more
