Collibra AI Governance vs Weights & Biases

Both compete in Enterprise Incumbents. Collibra AI Governance positions itself as “AI governance layered on Collibra's data catalog and lineage for trusted, compliant AI”, while Weights & Biasesleads with “AI developer platform for experiment tracking, model management, and LLM observability”. The table below compares what each publishes.

Where Collibra AI Governance pulls ahead

Publishes support for EU AI Act, NIST AI RMF, which Weights & Biases does not. Data-mature enterprises already using Collibra for data governance that want AI oversight anchored to catalog, metadata and lineage across many model platforms.

Where Weights & Biases pulls ahead

Publishes support for SOC 2, HIPAA, which Collibra AI Governance does not. ML and LLM engineering teams needing best-in-class experiment tracking, model lineage, and observability across the training-to-production lifecycle

	Collibra AI Governance	Weights & Biases
Positioning	AI governance layered on Collibra's data catalog and lineage for trusted, compliant AI	AI developer platform for experiment tracking, model management, and LLM observability
Category	Enterprise Incumbents	Enterprise Incumbents
Frameworks	EU AI Act, NIST AI RMF	SOC 2, HIPAA
Deployment	SaaS, Cloud	SaaS, Cloud, On-prem, API
Built for	Data Science / ML, GRC, Compliance, Risk	Data Science / ML, Security
Founded	2008	2017
Headquarters	Brussels, Belgium and New York, New York, USA	San Francisco, California, USA
Ownership	Independent (private, VC-backed)	Acquired by CoreWeave; deal announced March 2025 and completed May 5, 2025 (reported at roughly $1.7B). Operates as part of CoreWeave as of mid-2026.
Funding	~$640M raised; ~$5.25B valuation (2021)	Raised approximately $250M in venture funding pre-acquisition (investors included Coatue, Insight Partners, Felicis, NVIDIA); now owned by CoreWeave (NASDAQ: CRWV)
Pricing	Custom / enterprise	Freemium with usage-based paid tiers; enterprise subscription for Dedicated Cloud and self-hosted deployments (custom quote)
Key capabilities	Model and agent registries with lifecycle stages EU AI Act and NIST AI RMF assessment templates End-to-end data-to-model lineage Metadata capture and documentation Agentic AI compliance assessments Broad AI platform integration	Experiment tracking and run logging W&B Registry with model/dataset versioning, aliases, and lineage W&B Weave LLM tracing and observability Online evaluations for production agents OpenTelemetry trace ingestion Hyperparameter sweeps and artifact management
Integrations	AWS Bedrock, AWS SageMaker, Azure AI Foundry, Azure ML, Databricks Unity Catalog, Google Vertex AI, MLflow, SAP AI Core	OpenAI, Hugging Face, Azure, AWS, NVIDIA NIM, PyTorch, TensorFlow, Keras, CoreWeave cloud, OpenTelemetry
Notable customers	None published	OpenAI, NVIDIA, AstraZeneca, Cohere, Toyota Research Institute
Best for	Data-mature enterprises already using Collibra for data governance that want AI oversight anchored to catalog, metadata and lineage across many model platforms.	ML and LLM engineering teams needing best-in-class experiment tracking, model lineage, and observability across the training-to-production lifecycle
Limitations	Strongest for organizations invested in Collibra's data governance suite; it emphasizes cataloging and assessment over runtime enforcement and real-time model monitoring.	Governance features are developer- and lifecycle-focused rather than purpose-built for GRC/compliance reporting; it lacks the regulatory-framework mapping of dedicated governance tools. Its acquisition by GPU-cloud provider CoreWeave raises some neutrality/roadmap questions for teams on competing infrastructure, and self-hosted deployment is discouraged by the vendor in favor of its managed cloud.

Which should you shortlist?

Choose Collibra AI Governance if data-mature enterprises already using Collibra for data governance that want AI oversight anchored to catalog, metadata and lineage across many model platforms.

Choose Weights & Biases if mL and LLM engineering teams needing best-in-class experiment tracking, model lineage, and observability across the training-to-production lifecycle

Neither is a substitute for a governance program. Whichever you pick, you still need people who can define the policies the tool enforces.

Full Collibra AI Governance profile →Full Weights & Biases profile →

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