Purpose-built AI governance platforms, setup guides, and vendor risk assessment frameworks. Know which tool solves which problem before you buy.
The AI governance platform market has grown rapidly since 2023, but many teams conflate two distinct categories: model monitoring (tracking live model performance, drift, and fairness in production) and AI governance (managing policy compliance, regulatory framework mapping, risk documentation, and audit evidence). These are related but different problems requiring different tools.
Credo AI and Holistic AI are primarily governance and compliance platforms. Arthur AI and Fiddler AI are primarily model monitoring platforms with governance overlays. Understanding this distinction before evaluating tools will save significant evaluation time and budget.
What each platform actually covers, a feature comparison table, the critical model monitoring vs governance distinction, regulatory framework mapping for EU AI Act and NIST RMF, and a decision framework by team size and regulatory exposure.
Step-by-step: create an AI system record, connect your model via SDK or API, configure the EU AI Act and NIST RMF policy packs, run assessments, and generate compliance evidence. Common setup mistakes that teams make covered in detail.
EU AI Act Article 28 deployer obligations when using vendor AI. A 5-step vendor assessment process, a 12-question due diligence questionnaire template, model card red flags, and contract clauses that actually protect your organisation.
| Platform | Primary Focus | Best For | Model |
|---|---|---|---|
| Credo AI | Policy governance, regulatory mapping | Enterprise with EU AI Act / NIST RMF exposure | Enterprise SaaS |
| Holistic AI | AI audit, enterprise assurance | Regulated sectors (finance, HR), SMCR-relevant | Enterprise SaaS |
| Arthur AI | Production model monitoring + governance | ML engineering teams needing monitoring-first approach | Enterprise SaaS |
| Fiddler AI | Explainability, drift, performance monitoring | Teams prioritising model explainability and observability | Enterprise SaaS |