MLOps & experiment tracking · Weights & Biases, Inc.

Weights & Biases

Hosted experiment tracker and AI evaluation platform for logging training runs, metrics, and model lineage.

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Weights & Biases (W&B) is an experiment tracker used to log training runs, hyperparameters, metrics, and artifacts, then compare them in interactive dashboards — the same bucket as MLflow, Comet, and neptune.ai, not a production monitoring tool. Its Registry and lineage tools trace which dataset and code version produced a given model, and the Weave module extends the platform into tracing and evaluating LLM applications and agents. W&B is offered primarily as hosted SaaS, with a self-hosted deployment option (including a restricted free "Personal" self-hosted tier and a paid "Advanced Enterprise" tier) for organizations that need data to stay on their own infrastructure. Free cloud plans cover individual developers; paid team plans add unlimited teams, access controls, and automation, with storage and data-ingestion volume billed separately above included allowances.

At a glance

Vendor Weights & Biases, Inc.
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source No
Best for Teams wanting a polished, collaborative hosted experiment tracker with an enterprise self-hosting option.

Pricing

Free for individuals; Pro is a flat monthly rate for small teams with usage-based storage and data-ingestion overages; Enterprise and self-hosted plans are custom-quoted.

Plan Price Notes
Free $0/month Personal development; experiment tracking, tracing, registry & lineage, community support
Pro From $60/month For teams under 50 employees; 100GB storage included (extra at $0.03/GB), 1.5GB Weave ingestion included (extra at $0.10/MB)
Enterprise Custom Adds single-tenant hosting, HIPAA compliance, SSO, and audit logs

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • Experiment tracking with interactive dashboards
  • Hyperparameter sweeps
  • Model and dataset Registry with lineage
  • LLM/agent tracing and evaluation (Weave)
  • Team collaboration and reporting
  • CI/CD automations and alerts
  • Self-hosted deployment option
  • Artifact versioning

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

MLflow Open-source experiment tracker and model lifecycle toolkit, governed by the Linux Foundation. Comet Hosted experiment-tracking platform that also offers a separate LLM observability product (Opik). neptune.ai Experiment tracker built for high-throughput logging of large-scale and foundation-model training runs. ClearML Open-source experiment tracker that also bundles pipeline orchestration, dataset versioning, and model deployment.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems.

Related guides

How to choose an MLOps & experiment tracking tool "MLOps" covers five different jobs — tracking, monitoring, orchestration, serving, versioning — and most teams need more than one, not the biggest single tool.