Weights & Biases
by Weights & Biases (a CoreWeave company)
The AI developer platform to build better models faster and ship GenAI apps with confidence.
Weights & Biases (W&B) is an MLOps and LLMOps platform that gives ML and AI teams experiment tracking, model and dataset versioning, hyperparameter optimization, and a system of record for training models and developing GenAI applications. It pairs W&B Models for the ML lifecycle with W&B Weave for tracing and evaluating LLM and agent applications.
At a Glance
- Category
- Developer Tools
- Pricing
- Freemium, Subscription, Usage-based, Contact for pricing
- Target Market
- Data Scientists, ML Engineers, AI Researchers, Enterprise Developers, CTOs
- Founded
- 2017
- Headquarters
- San Francisco, California, USA
- Customers
- 1,000+ companies and over 1 million AI engineers/practitioners
Key Features
- ✓Experiment Tracking
Logs metrics, hyperparameters, code, model weights, and sample predictions so training runs can be visualized, compared, and reproduced.
- ✓Sweeps (Hyperparameter Optimization)
Automates hyperparameter search across runs to find optimal model configurations.
- ✓Model & Artifact Registry
Versions and manages datasets, models, and ML pipelines with lineage tracking for governance and reproducibility.
- ✓W&B Weave
Provides tracing, evaluations, guardrails, and production monitoring for LLM and agent applications.
- ✓Reports
Creates interactive, shareable dashboards to document and collaborate on ML insights across a team.
- ✓Serverless Inference & Training
Offers hosted open-source LLMs plus serverless fine-tuning with RL and SFT, powered by CoreWeave infrastructure.
Capabilities
Use Cases
- •Foundation model training
AI labs use W&B Models to track large-scale pre-training and fine-tuning runs and debug infrastructure issues.
- •GenAI application development
Teams use W&B Weave to trace, evaluate, and monitor LLM and agent applications from experimentation to production.
- •Hyperparameter tuning at scale
Data scientists run Sweeps to systematically optimize model performance across many configurations.
Ideal For
Best For
- ✓Tracking and reproducing ML training experiments
- ✓Evaluating and monitoring LLM and agent applications in production
- ✓Collaborative model development across ML teams
Integrations
Deployment
Market & Ratings
1,000+ companies and over 1 million AI engineers/practitioners
Market Analysis
Pros
- ✓Best-in-class experiment tracking and visualization
- ✓Strong adoption among top AI labs and enterprises
- ✓Broad native framework integrations and easy instrumentation
- ✓Flexible deployment (SaaS, dedicated cloud, self-hosted)
Cons
- ✗Storage and data-ingestion overages can raise costs at scale
- ✗Primarily built for the ML R&D/experimentation workflow rather than operational agent/task management
- ✗Pro tier rate-limited and aimed at small teams, pushing larger orgs to Enterprise
Pricing
Free
$0
- ✓Up to 5 model seats
- ✓5 GB/mo storage
- ✓1 GB/mo Weave data ingestion
- ✓Experiment tracking, registry, evaluations and tracing
- ✓Community/email support
Pro
From $60/mo
- ✓Up to 10 model seats
- ✓100 GB/mo storage
- ✓Team-based access controls and service accounts
- ✓CI/CD automations and Slack/email alerts
- ✓30-day free trial
Enterprise
Contact for pricing
- ✓Customizable seats and storage
- ✓Single-tenant and self-hosted options
- ✓SSO, SCIM, audit logs, custom roles
- ✓HIPAA compliance and secure private connectivity
- ✓Enterprise support
Free tier and a self-hosted Personal tier are $0; Pro starts around $60/mo with usage-based overages for storage ($0.03/GB) and Weave data ingestion ($0.10/MB). Enterprise and Advanced (self-hosted) tiers are custom-priced. Free Pro-tier access is available for qualifying academics.
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