Collibra AI Governance vs Databricks Unity Catalog (Governance)

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 Databricks Unity Catalog (Governance)leads with “Unified governance for data, ML models, and AI agents on the Databricks lakehouse”. The table below compares what each publishes.

Where Collibra AI Governance pulls ahead

Publishes support for EU AI Act, NIST AI RMF, which Databricks Unity Catalog (Governance) 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 Databricks Unity Catalog (Governance) pulls ahead

Organizations building and operating AI on Databricks that want one governance layer spanning data, ML models and agents with continuous lineage from source to deployment.

	Collibra AI Governance	Databricks Unity Catalog (Governance)
Positioning	AI governance layered on Collibra's data catalog and lineage for trusted, compliant AI	Unified governance for data, ML models, and AI agents on the Databricks lakehouse
Category	Enterprise Incumbents	Enterprise Incumbents
Frameworks	EU AI Act, NIST AI RMF	None published
Deployment	SaaS, Cloud	Cloud, SaaS, API
Built for	Data Science / ML, GRC, Compliance, Risk	Data Science / ML, Security, GRC
Founded	2008	2013
Headquarters	Brussels, Belgium and New York, New York, USA	San Francisco, California, USA
Ownership	Independent (private, VC-backed)	Independent (private, VC-backed)
Funding	~$640M raised; ~$5.25B valuation (2021)	Private; multiple large rounds at a $100B+ valuation
Pricing	Custom / enterprise	Included with the Databricks platform (consumption-based); Unity Catalog available as open-source core
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	Fine-grained access control with row/column masking Model registry with versioning and lineage Unity AI Gateway for models, agents, tools and MCPs Audit logs and inference traces Data-quality monitoring and classification Centralized, federated and hybrid governance models
Integrations	AWS Bedrock, AWS SageMaker, Azure AI Foundry, Azure ML, Databricks Unity Catalog, Google Vertex AI, MLflow, SAP AI Core	Databricks Data Intelligence Platform, MLflow, AWS, Azure, Google Cloud, Delta Sharing
Notable customers	None published	None published
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.	Organizations building and operating AI on Databricks that want one governance layer spanning data, ML models and agents with continuous lineage from source to deployment.
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 is centered on assets managed within the Databricks lakehouse, and it is a technical, engineering-oriented layer rather than a regulation-mapping GRC suite with pre-built compliance frameworks.

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 Databricks Unity Catalog (Governance) if organizations building and operating AI on Databricks that want one governance layer spanning data, ML models and agents with continuous lineage from source to deployment.

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 Databricks Unity Catalog (Governance) profile →

AI Governance Tool Selection Kit

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