Collibra AI Governance vs SAS Viya AI 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 SAS Viya AI Governanceleads with “Model management, monitoring and governance across the analytics lifecycle on SAS Viya”. The table below compares what each publishes.

Where Collibra AI Governance pulls ahead

Publishes support for EU AI Act, NIST AI RMF, which SAS Viya AI 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 SAS Viya AI Governance pulls ahead

Regulated, analytics-heavy organizations in banking, insurance and government that need rigorous model management, validation and monitoring within the SAS ecosystem.

	Collibra AI Governance	SAS Viya AI Governance
Positioning	AI governance layered on Collibra's data catalog and lineage for trusted, compliant AI	Model management, monitoring and governance across the analytics lifecycle on SAS Viya
Category	Enterprise Incumbents	Enterprise Incumbents
Frameworks	EU AI Act, NIST AI RMF	None published
Deployment	SaaS, Cloud	SaaS, Cloud, On-prem
Built for	Data Science / ML, GRC, Compliance, Risk	Data Science / ML, Risk, Compliance
Founded	2008	1976
Headquarters	Brussels, Belgium and New York, New York, USA	Cary, North Carolina, USA
Ownership	Independent (private, VC-backed)	Independent (private)
Funding	~$640M raised; ~$5.25B valuation (2021)	Private (independently held)
Pricing	Custom / enterprise	Custom / enterprise
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	Model registry, versioning and lineage Drift monitoring with explainability Model 'nutrition labels' for accuracy and fairness Bias detection across sensitive variables Automated documentation and audit trails Governance for generative AI and agentic workflows
Integrations	AWS Bedrock, AWS SageMaker, Azure AI Foundry, Azure ML, Databricks Unity Catalog, Google Vertex AI, MLflow, SAP AI Core	SAS Viya, Python, R, Open-source model formats, MLOps/CI-CD pipelines
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.	Regulated, analytics-heavy organizations in banking, insurance and government that need rigorous model management, validation and monitoring within the SAS ecosystem.
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 strength is tied to the SAS platform and analytics stack, and the product emphasizes statistical model lifecycle management over the regulation-mapping and policy-workflow features of dedicated GRC suites.

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 SAS Viya AI Governance if regulated, analytics-heavy organizations in banking, insurance and government that need rigorous model management, validation and monitoring within the SAS ecosystem.

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 SAS Viya AI Governance profile →

AI Governance Tool Selection Kit

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