Weights & Biases
FreemiumThe AI developer platform to build AI agents, applications, and models with confidence.
Weights & Biases is an AI developer platform that helps teams build, evaluate, and govern AI agents, applications, and models through a shared system of record. It combines experiment tracking, model management, dataset and model versioning, hyperparameter optimization, and LLM observability in a single workspace. Thousands of companies and research labs use W&B to log training runs, compare results, reproduce models, and deploy AI workloads with confidence.
What is it
An AI developer platform that tracks experiments, versions AI assets, and observes AI agents and models throughout their lifecycle.
What it can do
Log metrics, hyperparameters, and artifacts with a few lines of code; compare training runs visually; optimize hyperparameters; version models and datasets; trace and evaluate LLM and agent behavior; and deploy SaaS, dedicated, or customer-managed.
Who is it for
Machine learning engineers, AI researchers, MLOps teams, and organizations that need reproducible, collaborative, and governed AI development.
Key Features
W&B Experiments — Training run tracking
Log metrics, hyperparameters, system metrics, code state, and media outputs from training scripts with minimal instrumentation. Live dashboards let teams compare runs, spot regressions, and reproduce results quickly.
W&B Sweeps — Hyperparameter optimization
Run Bayesian and other search strategies to explore hyperparameter spaces efficiently. Teams can launch thousands of runs and visualize which configurations achieve the strongest results.
W&B Registry — Model and dataset governance
Publish, version, and alias models, datasets, prompts, and code as curated collections. Registry acts as the single source of truth for CI/CD and production promotion workflows.
W&B Artifacts — Pipeline versioning
Version datasets, models, and dependencies with immutable lineage and storage-efficient deduplication. Artifact graphs show exactly how each model was produced and what data it depends on.
Use Cases
Track deep learning experiments
ML engineers add a few lines of code to training scripts and automatically capture every metric, hyperparameter, and artifact needed to reproduce a model later.
Optimize hyperparameters at scale
Data scientists run systematic sweeps across learning rates, architectures, and data configurations to find the strongest model without manual run bookkeeping.
Version datasets and models
Teams use Artifacts and Registry to maintain immutable versions of training data, checkpoints, and prompts, ensuring reproducibility and clear lineage.
Evaluate LLM applications
AI teams trace prompt-response pairs, latency, token usage, and custom evaluation metrics in Weave to compare models and catch regressions before release.
Pricing plans
Frequently Asked Questions
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