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Weights & Biases W&B

Enterprise MLOps platform for scalable, compliant machine learning lifecycle management.

Venture Funded$117MSeries CGradient VenturesGVAmplify Partners

machine learning lifecyclemodel trackingexperiment managementcollaboration

About

Weights & Biases provides a comprehensive MLOps platform enabling enterprises to track, visualize, and optimize machine learning workflows at scale. It supports collaboration, reproducibility, and compliance with industry standards, facilitating secure and governed AI deployments. Designed for large teams, W&B integrates seamlessly with existing data infrastructure to accelerate model development and operationalization.

This description came from a bulk import and has not yet been checked against the vendor's own page. Treat it as unverified.

Enterprise Readiness

from cataloged data

Not scored. Nobody has checked this entry against Weights & Biases W&B’s own site yet, so the signals below are unverified and we will not turn them into a rating. The breakdown shows what our record holds.

Security & ComplianceNot disclosed

No compliance certifications listed

Deployment FlexibilityStrong

Flexible deployment: Cloud, On-Premise, Hybrid, Self-Hosted

Integration DepthStrong

8 documented integrations

Proven AdoptionPartial

6 documented use cases

Company MaturityStrong

Founded 2017 · Series C

Composite of public compliance, deployment, integration, adoption, and company signals. “Not disclosed” reflects gaps in available data, not a vendor deficiency.

Independently sourced

Pulled directly from regulator filings and platform APIs. No vendor or editorial input.

Activity classifier

Active

last GitHub commit 0d ago

GitHub API2026-05-27

wandb

Open source product· 22.5k★· Apache-2.0· active 3mo ago

Python · Jupyter Notebook · HCL · TypeScript

cooling · +44★ (+0.2%) 30d

PyPI registry

wandb

OSV vulnerability feed2026-08-24

No known advisories

Enterprise Use Cases

Experiment tracking and visualization

Model performance monitoring

Collaboration across data science teams

Automated model versioning

Compliance and auditability

Scalable model deployment

Integrations

TensorFlowPyTorchKubernetesAWS SageMakerAzure MLGoogle Cloud AI PlatformDatabricksMLflow

How Weights & Biases W&B compares

Tool	Readiness	Pricing	Deployment	Compliance	Integrations
Weights & Biases W&B	Not scored	Freemium	Cloud, On-Premise, Hybrid, Self-Hosted	—	8
Groq	Not scored	Enterprise	Cloud, On-Premise, Hybrid, Self-Hosted	—	8
Comet ML Platform	70 · Strong	Enterprise	Cloud, On-Premise, Hybrid, Self-Hosted	—	8
DataRobot MLOps	70 · Strong	Enterprise	Cloud, On-Premise, Hybrid	—	8

Peers from the same category, ranked by Enterprise Readiness. Readiness is a composite of cataloged compliance, deployment, integration, adoption, and company signals.

Frequently Asked Questions

What is Weights & Biases W&B used for?

Weights & Biases W&B is enterprise MLOps platform for scalable, compliant machine learning lifecycle management. It is commonly used for experiment tracking and visualization, model performance monitoring, collaboration across data science teams, and automated model versioning.

Is Weights & Biases W&B free, and how is it priced?

Weights & Biases W&B offers a free tier, with paid plans that add capacity and features.

How can Weights & Biases W&B be deployed?

Weights & Biases W&B supports Cloud, On-Premise, Hybrid, and Self-Hosted deployment. On-premise and self-hosted options support data-residency and air-gapped requirements.

What does Weights & Biases W&B integrate with?

Weights & Biases W&B documents 8 integrations, including TensorFlow, PyTorch, Kubernetes, AWS SageMaker, Azure ML, and Google Cloud AI Platform.

When was Weights & Biases W&B founded?

Weights & Biases W&B was founded in 2017 and has raised $117M in funding.

Is Weights & Biases W&B enterprise-ready?

Based on cataloged data, Weights & Biases W&B has an Enterprise Readiness score of 72/100 (Strong tier), derived from its compliance, deployment, integration, adoption, and company-maturity signals.

Quick Facts

PricingFreemium

DeploymentCloud, On-Premise, Hybrid, Self-Hosted

Founded2017

Funding$117M

CategoryData & MLOps

Procurement

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