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Weights & Biases

AI Developer Tools

MLOps platform for tracking, visualizing, and collaborating on ML experiments

Founded 2017San Francisco, CA, USA201–500 employeesfreemium · Free planFrom Free (3 users, 100GB, unlimited experiments); Team $50/user/month; Enterprise customVerified · Sep 2026
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About Weights & Biases

Weights & Biases (W&B) is an MLOps platform used by machine learning teams to track experiments, visualize model performance, manage datasets and artifacts, and collaborate on model development. W&B integrates with PyTorch, TensorFlow, Keras, and major ML frameworks, and is used by researchers and engineers at companies including OpenAI, NVIDIA, Samsung, and leading academic labs.

Editorial Assessment

Weights & Biases (W&B) is the leading MLOps platform for AI practitioners and machine learning teams, providing experiment tracking, dataset versioning, model registry, hyperparameter sweeps, and LLM evaluation tools through a developer-native interface that has become the de facto standard in the ML research community. Founded in 2017 by Lukas Biewald, Chris Van Pelt, and Shawn Lewis in San Francisco, W&B was initially embraced by academic researchers and individual ML practitioners for its frictionless experiment logging — a single pip install and two lines of code log metrics, gradients, and artifacts from any ML framework. The platform grew through researcher adoption at universities and labs, with papers citing W&B appearing in NeurIPS, ICML, and ICLR. Enterprise customers including OpenAI, NVIDIA, and Samsung use W&B at scale for model development workflows. The Weave product adds LLM application tracing, evaluation, and debugging for teams building on top of foundation models. W&B's free tier is generous: teams of up to 3 users with unlimited experiments. Pricing scales by team size and storage. The Team plan ($50/user/month) adds privacy controls and advanced collaboration. Enterprise adds SSO, dedicated support, and audit logging. W&B competes with MLflow (open-source, self-hosted), Comet ML, and Neptune.ai, but has maintained market leadership through product velocity and community adoption.

Strengths
  • De facto standard for experiment tracking in the ML research community — cited in thousands of papers
  • Frictionless integration: two lines of code log experiments from PyTorch, TensorFlow, JAX, or any framework
  • Weave for LLM application tracing, evaluation datasets, and debugging production LLM apps
  • W&B Sweeps for automated hyperparameter optimization across cloud compute
  • Artifacts for versioning datasets, models, and evaluation sets with lineage tracking
  • Model Registry for managing production model lifecycle from experiment to deployment
  • Generous free tier: 3 users, unlimited experiments, 100GB storage
Limitations
  • Can become expensive for large teams — $50/user/month on Team plan
  • Self-hosted (W&B Server) requires significant infrastructure management effort
  • Data stored on W&B cloud by default — sensitive orgs may need on-prem or private cloud
  • UI complexity can overwhelm newcomers to MLOps
  • Open-source alternative MLflow provides similar core tracking at zero cost

Best For

ML engineers and data scientists tracking experiments across training runs AI research teams collaborating on model development with reproducibility requirements LLM application developers needing tracing and evaluation tooling for production apps Enterprise ML platforms teams managing model lifecycle from experiment to production registry

Not Ideal For

Small teams or individuals where the free tier of MLflow or a local experiment database is sufficientStrictly air-gapped environments where cloud experiment tracking is not permittedOrganizations that need full end-to-end MLOps including training infrastructure (Kubeflow or SageMaker better)

Key Features

Experiment tracking with automatic metric and artifact loggingW&B Sweeps for hyperparameter optimizationArtifacts for dataset and model versioningModel Registry for production model managementWeave for LLM tracing and evaluationReports for sharing experiment resultsIntegration with all major ML frameworks

Who It's For

ML engineers and data scientistsAI research teams at companies and universitiesLLM application developersEnterprise MLOps and model governance teams

Industries Served

TechnologyHealthcareFinanceAcademiaAutomotive

Platforms

web api

Support

Documentation Community Slack Enterprise support

Founders

  • Lukas Biewald
  • Chris Van Pelt
  • Shawn Lewis

Products & Tools

Weights & Biases PlatformMLOps / AI Development
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MLOps platform for experiment tracking, hyperparameter sweeps, artifact versioning, model registry, and LLM evaluation.

freemium · Free planFrom Free (3 users, 100GB); Team $50/user/month; Enterprise custom
Experiment trackingSweepsArtifactsModel RegistryWeave LLM tracing

Sources & Verification

Last reviewed September 2026

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Weights & Biases FAQs

Is W&B free?

Weights & Biases has a free personal plan for individual projects with unlimited tracking. Team plans and enterprise plans are paid and include additional collaboration features, access controls, and support.

What ML frameworks does W&B support?

W&B integrates natively with PyTorch, TensorFlow, Keras, JAX, Scikit-learn, Hugging Face Transformers, Lightning, and most major ML frameworks through simple Python callbacks.

Tags

MLOpsExperiment TrackingML MonitoringModel RegistryHyperparameter Tuning

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Official Website

wandb.ai

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