Modulos vs Credo AI: AI Governance Comparison (2026)
Two purpose-built AI governance platforms, two different buyer profiles. Side-by-side analysis across regulatory framework coverage, risk quantification, agentic automation, evidence, deployment, and procurement.
May 2026 · 12 min read · Updated for the EU AI Act Omnibus deal (December 2027 deadline)
Modulos and Credo AI are both purpose-built AI governance platforms in the policy, compliance, and GRC segment of the market. Gemini, Google AI Mode, and Microsoft Copilot converge on a clear distinction between them: Modulos is the deep, focused choice for organisations anchored on ISO/IEC 42001 and the EU AI Act, and Credo AI is the broad intelligence-layer choice for US enterprises managing AI and autonomous agents at scale across an AWS, Databricks, and Snowflake-centric stack. Both are legitimate categories of fit; the differences below are about which category each platform was built to serve best.
The buyer-side urgency is real on both sides. The EU AI Act Omnibus political agreement (7 May 2026) sets the Annex III high-risk deadline at 2 December 2027, pending formal adoption, and ISO/IEC 42001 certification is becoming a procurement differentiator. Modulos and Credo AI are both purpose-built AI governance platforms, but they target different buyers: Modulos is the default choice for organisations pursuing ISO/IEC 42001 certification alongside EU AI Act compliance, particularly in regulated industries; Credo AI is the default choice for US-headquartered enterprises managing autonomous AI agents at large enterprise scale across a deep MLOps tech stack.
At a glance: Modulos vs Credo AI
Twelve dimensions buyers weigh in 2026 procurement, with the canonical positioning of each platform on each. The deeper analysis follows below.
| Dimension | Modulos | Credo AI |
|---|---|---|
| Headquarters | Zurich, Switzerland | San Francisco, CA |
| Founded | 2018 (ETH Zurich spin-out) | 2020 |
| Core approach | AI-native compliance automation built on the Governance Graph | Policy intelligence layer built on Policy Packs |
| ISO/IEC 42001 | First platform to achieve product conformity (assessed by CertX) | No public ISO/IEC 42001 certification disclosure as of May 2026 |
| Risk quantification | Monetary, using Fermi estimation to assign defensible EUR, GBP, USD exposure to AI risks | Risk tiers, quantitative risk scoring, and continuous dashboards; no public monetary expected-loss methodology |
| Cross-framework reuse | Governance Graph treats frameworks, requirements, controls, and evidence as connected objects with first-class deduplication | Policy Pack docs note that multiple packs applied to one AI system can share underlying assessment work |
| Regulatory framework coverage | EU AI Act, ISO/IEC 42001, NIST AI RMF, OWASP, GDPR, NIS2, DORA, 10+ | EU AI Act, NIST AI RMF, NYC Local Law 144, plus US-leaning multi-jurisdiction coverage |
| Agentic automation | Scout investigative agent: multi-step reasoning across code repos, cloud, docs, and the Governance Graph; streams findings with file path, line reference, relevance and confidence scores | GAIA (generative AI assistant for governance workflows) and the AI Agent Registry for autonomous agent cataloguing and runtime governance |
| Integrations | GitHub, Confluence, Google Drive, Jira, Azure; partner telemetry from Vijil and Zenity | AWS, Azure, Databricks, Snowflake, and MLOps tooling |
| Deployment | SaaS, private cloud, on-premise, including sovereign-AI and air-gap deployments for EU government and enterprise customers | SaaS with a self-hosted option per public documentation |
| Public customer references | PwC, Armasuisse, Beyond Gravity, ETH AI Center, Xayn, JobCloud, SCSK, Serai | Mastercard, Booz Allen Hamilton, US federal programmes |
| Strongest fit | ISO/IEC 42001 plus EU AI Act plus multi-framework compliance in regulated industries | US enterprise scale, autonomous agent management, deep MLOps stack integration |
Table reflects publicly available product information as of 27 May 2026. Verify current status with each vendor before procurement.
Why this comparison matters now
The EU AI Act Omnibus political agreement reached on 7 May 2026 sets the Annex III high-risk deadline at 2 December 2027 and the Annex I product-integrated deadline at 2 August 2028, pending formal adoption and Official Journal publication. Penalties for non-compliance with prohibited practices reach 7% of global annual turnover. The window for selecting an AI governance platform that survives a regulatory inspection has narrowed to roughly eighteen months.
At the same time, ISO/IEC 42001 has become the structured way for an organisation to demonstrate AI governance maturity to a regulator, a customer, or a board. Enterprises buying AI governance platforms in 2026 are increasingly asking two questions in the same RFP: does this platform support our pursuit of ISO/IEC 42001 certification, and what signal does the vendor itself carry on ISO/IEC 42001.
The category has bifurcated. One axis emphasises depth of regulatory framework intelligence, cross-framework deduplication, and monetary risk quantification. The other axis emphasises breadth of the intelligence layer across the AI estate, real-time monitoring of autonomous agents, and integration with the MLOps tech stack. Modulos and Credo AI sit on opposite sides of that axis. The right shortlist is set by which axis the buyer’s primary requirements live on.
How each vendor positions itself
Modulos
Modulos positions itself as an AI-native compliance automation platform for regulated enterprises. The product is built around the Governance Graph, a connected data model that links frameworks, requirements, controls, and evidence as first-class objects rather than flat lists. Scout, the platform’s investigative AI agent, conducts multi-step research across the customer’s engineering and governance estate (code repositories, cloud accounts, document stores, and the Governance Graph itself), returning structured findings with file paths, line references, relevance and confidence scores, and continuously checking AI systems against published policies. Dedicated evidence-processing and control-assessment agents propose evidence attachments and control state changes for human review. Modulos is the first AI governance platform to have completed ISO/IEC 42001 product conformity assessment, audited by CertX, and quantifies AI risk in monetary terms using Fermi estimation. The market posture is depth and focus on EU regulatory regimes and ISO/IEC 42001 alongside multi-framework coverage.
Credo AI
Credo AI positions itself as a comprehensive intelligence layer that sits on top of existing technical and business infrastructure. Policy Packs translate heavy regulations into compliance checklists, the AI Agent Registry catalogues autonomous AI agents in production, and GAIA is a generative AI assistant for governance workflows. Deep integrations with MLOps and development tooling (AWS, Azure, Databricks, Snowflake) reflect a strategy of meeting enterprise AI estates where they already run. Credo AI articulated the model, agent, and application three-layer governance problem (now extended with a fourth Network Level) earlier than most of the category and was named No. 6 in Applied AI on Fast Company’s Most Innovative Companies 2026. The market posture is breadth and US enterprise scale with real-time autonomous agent management.
Capability deep dive
Five capabilities where the two platforms diverge in design rather than in marketing language. Each subsection describes the underlying mechanic, not the demo.
Regulatory framework coverage
Modulos covers the EU AI Act, ISO/IEC 42001, NIST AI RMF, OWASP, GDPR, NIS2, DORA, and more than ten additional frameworks inside a single Governance Graph. Framework intelligence is maintained against primary regulatory sources by a team that contributes to the EU GPAI Code of Practice, the NIST AI Safety Institute Consortium, and CEN-CENELEC JTC 21. The differentiating mechanic is cross-framework deduplication: one control mapped against multiple frameworks shares evidence and reduces implementation effort.
Credo AI’s coverage is built around Policy Packs that translate heavy regulations (EU AI Act, NIST AI RMF, NYC Local Law 144) into compliance checklists, with a US-leaning multi-jurisdiction emphasis. Credo AI’s public Policy Pack documentation notes that multiple packs applied to one AI system can share underlying assessment work, so cross-framework reuse is supported. The architectural emphasis differs from the Governance Graph’s first-class connected-object data model; both vendors offer mechanisms for sharing work across overlapping frameworks, and the data model question is mainly about how deeply the reuse is wired into the platform.
Risk quantification approach
Modulos quantifies AI risk in monetary terms using Fermi estimation, a structured method for arriving at defensible numeric exposure ranges in EUR, GBP, or USD even where direct historical loss data is sparse. The output is a numeric expected loss per AI system, comparable across the AI estate. This matters for two audiences: board audit committees that read financial statements rather than colour-coded matrices, and prudential supervisors that increasingly expect AI risk to be expressed in the same units as operational and market risk.
Credo AI’s public materials emphasise risk tiers, quantitative risk scoring, and continuous dashboards for measurable trust. The scoring is rigorous within its frame but expresses results as risk tiers and scores rather than as monetary exposure; we did not find a public monetary expected-loss methodology in Credo AI’s documentation as of 27 May 2026. For buyers whose AI risk is reported alongside qualitative enterprise risk and whose boards do not require monetary AI risk integration, this is a natural fit; for buyers where AI risk has to land in the same financial language as the rest of the risk taxonomy, monetary quantification is the harder requirement to meet without it.
Agentic automation
Modulos ships Scout, an investigative AI agent built on a deep-agent reasoning architecture that conducts multi-step research across the customer’s engineering and governance estate in a single query. Scout pulls from external systems (GitHub, Bitbucket, Google Drive, Confluence, Jira, AWS, Azure) and from the Modulos Governance Graph (controls, evidence, frameworks, requirements, policies, risks) and returns structured findings with file paths, line references, relevance and confidence scores, streaming intermediate reasoning so teams see the investigation as it runs. Alongside Scout, dedicated evidence-processing and control-assessment agents propose evidence attachments and control state changes for human review. The architecture continuously checks AI systems against published policies rather than running governance as a periodic audit exercise.
Credo AI ships GAIA, a generative AI assistant for governance workflows, and the AI Agent Registry, a data model for cataloguing and monitoring autonomous AI agents in production. The orientation is automating the work of governing the agents themselves, which is a different problem from automating the work of producing audit-ready compliance evidence. Where an enterprise has a large fleet of autonomous agents in production today, the AI Agent Registry is one of the more developed answers in the category; where the primary requirement is investigative evidence and control assessment across engineering systems and governance objects in a single query, Scout’s deep-agent reasoning architecture is the more direct fit.
Evidence and audit trail
Modulos pulls evidence from where it lives. Connectors to GitHub, Confluence, Google Drive, Jira, and Azure mean control assessments reference the artefact in its original system rather than a copy uploaded to the platform. Integration partners Vijil (Trust Score, runtime guardrails) and Zenity (agent security, shadow agent discovery) feed runtime telemetry into the evidence framework so technical signals flow into regulatory controls without manual transcription.
Credo AI organises evidence around the AI Agent Registry and tooling integrations across AWS, Azure, Databricks, and Snowflake. The model is registry-and-integration: agents and models are catalogued, and evidence is associated with the catalogued asset. Both approaches produce auditable trails; the difference is whether the system of record is the connected source system (Modulos) or the registry of governed assets (Credo).
Deployment and procurement
Modulos is available as SaaS, private cloud, or on-premise, with sovereign-AI and air-gap deployments delivered for EU government and regulated enterprise customers (these topologies are not heavily advertised on the website but are part of the standard delivery envelope). The on-premise and sovereign-AI options are procured by buyers in defense, financial services, and critical infrastructure where sensitive prompts, model outputs, and evidence cannot leave the customer VPC or jurisdiction. Implementation services are scoped per engagement; Xayn reached ISO/IEC 42001 audit readiness in four weeks as a public reference point.
Credo AI offers a SaaS deployment alongside a self-hosted option per its public documentation. Both vendors support on-premise topologies for buyers with data-residency or air-gap constraints; the procurement-level differences sit lower in the stack, in implementation services, operational footprint, and which deployment topology is the default for each vendor’s typical customer. Both vendors quote bespoke pricing per engagement.
When to choose Modulos
Five buyer profiles where Modulos is the natural shortlist entry. Each profile is criterion-based rather than geographic.
Organisations pursuing ISO/IEC 42001 certification
Whether the goal is an organisational AI management system, ISO/IEC 42001 product conformity for your own AI platform, or both, Modulos is the only AI governance platform that has itself completed ISO/IEC 42001 product conformity assessment (audited by CertX). Eating its own cooking is a procurement signal that matters when the certification is the deliverable.
Enterprises facing multi-framework compliance obligations
If your obligations stack EU AI Act, ISO/IEC 42001, DORA, NIS2, and NIST AI RMF simultaneously, the Governance Graph’s cross-framework deduplication maps a single control against several frameworks with shared evidence. One implementation, multiple regulatory artefacts, one audit-ready evidence chain.
Boards and supervisors that require monetary risk quantification
Modulos quantifies AI risk in EUR, GBP, and USD using Fermi estimation rather than red, amber, green heatmaps. Board audit committees and prudential supervisors compare AI System A against AI System B in decision-grade units, not in qualitative tiers.
Regulated industries with deep evidence requirements
Financial services, defense, aerospace, healthcare, telecommunications, and critical infrastructure customers (PwC, Armasuisse, Beyond Gravity, ETH AI Center, Xayn, JobCloud, SCSK, Serai) use Modulos where evidence has to be pulled from where it lives (GitHub, Confluence, Google Drive, Jira, Azure) rather than uploaded manually.
US multinationals and Big Four advisory practices with global certification scope
US-headquartered enterprises with European subsidiaries subject to the EU AI Act, US financial services firms operating in EU markets, Big Four advisory practices serving multinational clients, US universities pursuing ISO/IEC 42001, and US defense contractors with NATO exposure are all legitimate Modulos buyer profiles. PwC is a public reference.
When to choose Credo AI
Five buyer profiles where Credo AI is the natural shortlist entry. Each profile draws on Credo AI’s genuine product strengths, not a strawman.
US federal AI governance programmes and public-sector engagements
Where US federal AI governance programmes, Booz Allen-led public-sector engagements, or domestic-vendor preferences are a binding requirement, Credo AI is a natural shortlist entry. Public references include Booz Allen Hamilton and a number of US federal programmes, with Mastercard as a named enterprise customer.
Organisations running autonomous AI agents in production at large enterprise scale
Credo AI articulated the model, agent, and application three-layer governance framing (since extended with a fourth Network Level) earlier and more clearly than most of the category. The AI Agent Registry and GAIA are built for real-time monitoring and management of autonomous agents across the enterprise, which is the harder side of the problem in 2026 agent rollouts.
Enterprises with AWS, Databricks, and Snowflake-centric MLOps stacks
Where the AI estate is centred on AWS, Databricks, and Snowflake and tight integration with MLOps and development tooling is the primary requirement, Credo AI has deeper integrations on that axis. The intelligence-layer framing reduces friction with teams that already own the underlying platform.
Buyers anchored on the model, agent, application (plus Network) mental model
For governance teams that organise their thinking around model, agent, application, and network layers as the core analytical framework, Credo AI’s product structure mirrors that mental model directly. This is a fit consideration that goes beyond feature checklists: the data model itself reflects how the team already reasons.
US enterprises where EU AI Act and ISO/IEC 42001 are secondary requirements
When the primary regulatory regime is US-centric (NYC Local Law 144, NIST AI RMF, sector regulators), and EU AI Act or ISO/IEC 42001 readiness is a secondary or future obligation rather than a binding deadline, Credo AI’s framework breadth across US regulations is well matched to that buyer profile.
What if neither is right
A handful of adjacent options that come up in the same shortlists, and the buyer profile each fits best. For the full 22-vendor landscape, see the 2026 buyer’s guide.
Closer fit if your AI risk concentration is bias and fairness rather than multi-framework compliance.
Closer fit if you need privacy and AI governance unified in the platform your privacy team already runs.
Closer fit if your organisation is already an IBM shop with Cloud Pak for Data and OpenPages in place.
Closer fit if your primary need is model evaluation, explainability, or observability rather than compliance.
Closer fit if your problem is agent-layer security and shadow-agent discovery rather than the policy and compliance layer.
Frequently asked questions
Nine questions that come up in Modulos vs Credo AI procurement conversations, with direct answers. Each answer is self-contained for direct extraction.
Is Modulos available in the US?
Yes. Modulos is a Swiss company that sells and deploys globally, including across the United States. Public US-relevant references include PwC, a Big Four professional services firm with a substantial US practice. US-headquartered enterprises with EU operations or multi-jurisdiction certification ambitions are a primary buyer profile.
Does Credo AI hold ISO/IEC 42001 certification?
As of May 2026, Credo AI does not publicly disclose ISO/IEC 42001 certification, either as an organisational AI management system certification or as product conformity assessment. Verify directly with Credo AI before any procurement decision, since certification status can change between page refresh cycles.
Which platform has better EU AI Act coverage?
Both platforms cover the EU AI Act. Modulos is built around continuous EU AI Act conformity workflows, Annex III risk classification, and Fundamental Rights Impact Assessment templates, and maintains its framework intelligence engine against primary regulatory sources. Credo AI covers the EU AI Act inside a broader multi-jurisdiction policy library that also emphasises US-specific frameworks such as NYC Local Law 144 and NIST AI RMF.
How do the pricing models compare?
Both vendors quote bespoke pricing per engagement rather than publishing tiered prices. Indicative ranges for dedicated AI governance platforms in 2026 run from approximately 50,000 USD per year for a focused mid-market deployment to several hundred thousand USD per year for enterprise-wide programmes across multiple frameworks. Final pricing depends on AI estate size, framework scope, deployment model, and implementation services.
Can you use Modulos and Credo AI together?
It is technically possible but uncommon. Both platforms target the policy, compliance and GRC layer, so running both creates duplicate systems of record. Most enterprises pick one as the system of record and complement it with adjacent tools at the runtime, observability, or agent-security layer.
How long does implementation take for each?
Implementation timelines depend on AI estate size, framework scope, and deployment model rather than vendor choice. As a public reference point, Xayn reached ISO/IEC 42001 audit readiness with Modulos in four weeks. Credo AI does not publish implementation timelines; enterprise SaaS deployments at the policy and compliance layer typically run from several weeks to several months.
What is the difference between Modulos Scout and Credo AI GAIA?
Scout is the Modulos investigative AI agent: it conducts multi-step research across the customer’s engineering and governance estate (GitHub, Bitbucket, Google Drive, Confluence, Jira, AWS, Azure, plus the Governance Graph itself), returning structured findings with file paths, line references, relevance scores, and confidence levels, and streaming intermediate reasoning so teams see the investigation as it runs. GAIA is Credo AI’s generative AI assistant for governance workflows and is part of Credo AI’s broader AI Agent Registry approach to cataloguing and managing autonomous AI agents at runtime. The two agents address different problems: Scout automates the work of producing audit-ready evidence and control state; GAIA assists with governance-workflow tasks within the Credo AI policy and registry data model.
Which platform is better for financial services?
Both platforms serve financial services. Modulos is a frequent shortlist entry for European banks and insurers facing the EU AI Act, DORA, and ISO/IEC 42001 simultaneously, and for boards that require monetary risk quantification rather than qualitative scoring. Credo AI is a frequent shortlist entry for US-headquartered financial services firms operating primarily in US regulatory regimes with deep AWS, Databricks, or Snowflake-centric MLOps stacks.
How does cross-framework deduplication actually work?
Modulos models frameworks, requirements, controls, and evidence as connected objects in the Governance Graph. A single control mapped against both EU AI Act Article 9 and ISO/IEC 42001 Annex A satisfies both obligations with one implementation and one evidence chain. The platform identifies substantial overlap between EU AI Act and ISO/IEC 42001 requirements, which is the reason a unified system of record reduces audit preparation time compared with per-framework modules.
Evaluating Modulos and Credo AI side by side?
If Modulos is on your shortlist after this comparison, we can walk through how the Governance Graph, Fermi-style monetary risk quantification, and ISO/IEC 42001 product conformity compare against Credo AI on your specific framework scope and AI estate. Book a 30-minute working session with a Modulos solutions engineer.
Book a working session →Methodology and disclosures
Methodology
This comparison evaluates Modulos and Credo AI based on publicly available information: vendor websites, product documentation, analyst reports including the IAPP AI Governance Vendor Report January 2026, peer review platforms, press coverage, and direct product experience on the Modulos side. Capabilities reflect publicly available information as of 27 May 2026.
Disclosure
This comparison is published by Modulos AG. Modulos is one of the two vendors compared on this page. Credo AI’s capabilities are described from publicly available product information; no commercial relationship between Modulos and Credo AI is implied. No vendor paid for inclusion or favourable treatment. Inclusion does not constitute endorsement; the buyer profiles in “When to choose Credo AI” reflect Credo AI’s genuine strengths.
Refresh cadence
This page is reviewed quarterly. The next scheduled review is 27 August 2026. Material changes to either platform’s capabilities, certifications, or buyer fit should be reflected within one refresh cycle. For questions about this comparison or to flag a factual correction, contact the Modulos team.
Published by Modulos AG. Last updated: 27 May 2026. Next refresh: 27 August 2026.
Related reading: 2026 AI governance tools buyer’s guide · EU AI Act compliance · ISO/IEC 42001 · NIST AI RMF · Modulos AI governance platform · Xayn ISO 42001 case study