Weights & Biases
Streamline AI development with experiment tracking and model management
Overview
Weights & Biases helps teams build better AI systems by tracking experiments, visualizing results, and managing model versions. It simplifies collaboration for machine learning projects with tools to optimize hyperparameters, debug LLM applications, and maintain production models. The platform scales from individual researchers to enterprise teams deploying AI at scale.
Key Features
Experiment Tracking
Log and visualize ML training runs in real-time
Model Registry
Central hub for versioning and deploying models
Hyperparameter Optimization
Automatically find optimal model configurations
LLM Debugging
Track prompts and analyze language model behavior
Collaborative Dashboards
Share findings through interactive reports
Use Cases
Track machine learning experiments
Optimize neural network hyperparameters
Visualize model performance metrics
Debug LLM-powered applications
Version datasets and models
Pros & Cons
Pros
- Comprehensive MLOps/LLMOps toolkit
- Strong collaboration features for teams
- Scalable from research to production
- Enterprise-grade security and support
Cons
- Steep learning curve for new users
- Pricing unclear without trial access
- Primarily focused on technical users
Pricing Plans
Team
monthlyFeatures
- Unlimited experiments
- Collaborative projects
- Basic support
Enterprise
monthlyFeatures
- SSO/SAML
- Private cloud options
- Dedicated support
Academic
monthlyFeatures
- Education discounts
- Research support
- Classroom resources
Pricing may have changed
For the most up-to-date pricing information, please visit the official website.
Visit websiteFrequently Asked Questions
What is Weights & Biases used for?
W&B helps machine learning teams track experiments, manage models, and collaborate on AI development projects.
What's the difference between Models and Weave?
Models handles traditional ML workflows while Weave focuses on LLM application development and debugging.
Is there a free tier?
Yes, free plans are available for individual users and academic researchers.
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