# AI governance that goes model deep > Compare Deeploy and IBM watsonx.governance: shared policy and framework governance, plus native model deployment, inline enforcement and EU-native compliance. **Source:** https://deeploy.ai/compare-ibm-watsonx/ **About this file:** Machine-readable markdown version of the page above, for LLMs and AI assistants. Regenerated automatically whenever the page is updated. ## The EU-based IBM watsonx.governance alternative built for AI-native speed IBM watsonx.governance gives large enterprises a broad, GRC-grade view of AI risk. Deeploy connects governance directly to the models running in production. ## The problem with governance built for a different scale Your risk team may already be looking at, or running, IBM watsonx.governance. It's a capable platform, built on IBM's OpenPages and Cloud Pak for Data foundations, and it does a genuinely good job of mapping AI risk into the wider enterprise GRC picture: IT risk, third-party risk, and business continuity. But that breadth comes at a cost - and it shows up fastest for teams that just want their AI models governed, monitored, and audit-ready. - ✗ Built for enterprise GRC, not AI-native speed - watsonx.governance sits on top of Cloud Pak for Data. Standing it up is an integration project, not a sign-up. - ✗ Governs from the outside - it connects to models deployed on SageMaker, Bedrock, Vertex AI, or watsonx.ai. It doesn't deploy or host models itself, so controls are applied after the fact rather than at the point of inference. - ✗ No inline enforcement - it evaluates and monitors, but doesn't sit in the request path. There's no native gateway to block a non-compliant call or contain an agent before it acts. - ✗ US-centric by design - strongest on US sectoral regulation and IBM's own compliance content network. European teams working to the EU AI Act and GDPR often need to bolt on extra coverage. ## One platform. Governance where your models run. watsonx.governance builds a governance graph around your AI estate. Deeploy does that too - and then goes one layer deeper: it's also where the models themselves run. ### Policy & framework layer (What Deeploy & watsonx.governance do) - AI and model inventory - Risk assessment workflows - Policy and control mapping - Compliance and audit reporting ### Model & runtime layer (What only Deeploy does) - Native model deployment and hosting - Inline, runtime enforcement of controls - Per-prediction audit trails, generated at the source - Native model explainability, no separate evaluation pipeline to wire up ## How Deeploy compares to watsonx.governance watsonx.governance governs models from the outside. Deeploy is also where they run. | Feature | Deeploy | watsonx.governance | | --- | --- | --- | | AI / model inventory | Yes | Via the Governance Graph | | Model deployment & hosting | Native | n/a | | Runtime / inline enforcement | Controls run at inference | n/a | | Bias & drift monitoring | Built-in | Via the evaluation stack | | Agent governance | Native registry, tracing & control | Via watsonx Orchestrate add-ons | | Enterprise-wide GRC | AI-focused, not a general GRC suite | Via IBM OpenPages | | EU AI Act & GDPR alignment | EU-native from day one | Available, alongside stronger US coverage | | Time to first value | Weeks, standalone SaaS | Cloud Pak for Data integration project | | Best fit | AI governance and deployment in one place, fast | Enterprises invested in IBM OpenPages / Cloud Pak | ### Already evaluating or using watsonx.governance? Here's how Deeploy can work with, or simplify, your current stack. #### Complement watsonx.governance If your organisation runs OpenPages for enterprise-wide risk, keep watsonx.governance for that broader remit and add Deeploy for the AI layer itself: - watsonx.governance: enterprise GRC, third-party risk, compliance content network. - Deeploy: model deployment, runtime controls, monitoring, per-prediction audit trails. - Result: Enterprise risk coverage, plus AI governance that actually reaches the model. #### Choose Deeploy as single solution For teams whose primary need is AI and agent governance, not a full enterprise GRC suite: - Everything you need for AI governance: inventory, risk classification, policy frameworks, audit evidence. - Plus what watsonx.governance can't do on its own: deploy models, enforce controls inline, and generate explainability and audit trails natively at the point of inference. - Result: Full AI governance, live in weeks. ## Why teams choose Deeploy over watsonx.governance ### Governance in the request path **watsonx.governance evaluates and reports on model behaviour after the fact. Deeploy hosts the model, so controls, monitoring, and explainability are applied as predictions happen, not reconstructed from logs afterwards.** ### Built for AI teams, not a GRC migration **watsonx.governance is strongest when it extends an existing IBM OpenPages or Cloud Pak investment. Deeploy is a standalone platform your AI and compliance teams can be running in weeks, with no wider enterprise stack required.** ### European by design **Deeploy is headquartered in the Netherlands and built from day one around the EU AI Act, ISO/IEC 42001, and European data sovereignty requirements, rather than adding EU coverage to a platform designed around US sectoral regulation.** ## Deeploy adds the governance infrastructure your AI stack has been missing Centralise and govern all AI activity with a unified registry that captures every model and use case across your organisation, from in-house developments to vendor solutions, ensuring nothing goes into production without proper oversight. Implement comprehensive governance across teams with built-in frameworks like the EU AI Act, ISO/IEC 42001, and NIST AI RMF or create custom frameworks tailored to your organisation’s specific requirements. Classify any AI use case instantly with the built-in EU AI Act risk classification assessment, and know exactly which controls apply to your use case. Maintain complete transparency and compliance with automatic logging that records every action, ensuring full traceability across your system. Build authorisation workflows with approval rules that distribute responsibility across teams, requiring sign-off for new use cases, models and updates. Monitor AI performance with real-time tracking. Detect issues early and ensure your models remain compliant, accurate, and reliable. ## Ready to see model-level AI governance in action? Book a demo: https://deeploy.ai/book-demo/ ## Frequently asked questions ### What's the main difference between watsonx.governance and Deeploy? watsonx.governance is an enterprise GRC platform: it builds a governance graph across your AI estate and folds AI risk into IT, third-party, and business continuity risk, typically as an extension of IBM's OpenPages and Cloud Pak for Data. Deeploy is an AI governance platform that **also deploys and hosts your models**. That means controls, monitoring, and explainability are enforced where predictions actually happen, not assembled afterwards from separate evaluation pipelines. ### We're already evaluating (or running) watsonx.governance. Why look at Deeploy? It depends on what you need governed. If your priority is folding AI risk into an existing enterprise GRC programme - alongside IT risk, third-party risk, and compliance content spanning hundreds of frameworks - watsonx.governance is built for that, especially if OpenPages is already part of your stack. If your priority is getting AI models themselves under control quickly, with deployment, runtime enforcement, and audit trails in one place, that's a narrower and faster problem to solve - and it's the one Deeploy is built to solve. **Common scenario:** you need to show a regulator not just that a bias policy exists, but that it's enforced on a specific model, in production, today. watsonx.governance can report on that if the monitoring is wired up. Deeploy enforces it directly, because the model runs on the platform doing the governing. ### Does watsonx.governance deploy AI models? No. watsonx.governance connects to models deployed on platforms such as watsonx.ai, Amazon SageMaker, Amazon Bedrock, or Google Vertex AI, and governs them from there. Deeploy deploys and hosts the models directly, which is what allows it to apply controls inline rather than through a separate integration. ### Can Deeploy replace watsonx.governance entirely? For organisations whose main goal is AI and agent governance rather than enterprise-wide GRC: **yes**. Deeploy covers AI use case inventory, risk classification, policy frameworks, approval workflows, and audit-ready reporting, and adds native model deployment, inline enforcement, and explainability that watsonx.governance doesn't do on its own. For organisations that specifically need AI risk unified with broader enterprise risk management - including IT, vendor, and continuity risk across a large IBM estate - watsonx.governance's OpenPages foundation is purpose-built for that, and Deeploy is a strong complement to it rather than a wholesale replacement. ### How does Deeploy handle the EU AI Act differently from watsonx.governance? Both platforms offer EU AI Act risk classification and documentation support. The difference is in where enforcement happens. **watsonx.governance:** - Policy frameworks aligned to the EU AI Act - Governance graph mapping AI assets to obligations - Strongest compliance content network for US sectoral regulation **Deeploy:** - Policy frameworks aligned to the EU AI Act - Built and headquartered in the Netherlands, with EU data sovereignty as a design principle rather than an add-on - Technical controls enforced inline (accuracy, robustness, bias monitoring), since the model runs on the platform **Key difference:** the EU AI Act requires both policy alignment and technical evidence that controls actually run. watsonx.governance documents policy well and monitors models once integrated. Deeploy generates that technical evidence directly, because deployment and governance sit in the same place. ### What evidence can Deeploy provide for regulatory audits? **Audit trail evidence:** - Per-prediction logs with timestamps - Complete input/output records - Explainability outputs for individual decisions - User actions and human oversight records **Performance evidence:** - Bias metrics across demographic groups - Model accuracy over time - Drift detection history - Fairness assessments **Governance evidence:** - Control compliance status - Approval workflows and attestations - Risk classifications and justifications - Documentation (model cards, data cards, policies) **Export capabilities:** - PDF reports for regulators - Real-time dashboards for live audits ### Can Deeploy integrate with our existing tools (MLOps, data platforms, watsonx, etc.)? Yes. Deeploy is built for integration. **Native integrations:** - Cloud platforms: AWS, GCP, Azure - MLOps tools: MLflow, Kubeflow, SageMaker - Model stores: HuggingFace, your internal registries - LLM providers: OpenAI, Mistral, Anthropic, and IBM's own Granite models - Data warehouses: Snowflake, Databricks, BigQuery - Open-source frameworks: Triton, PyTorch, Hugging Face, XGBoost - Webhooks and Slack integration for alerts and events Your ML stack is already complex. Deeploy adds a governance and deployment layer to it rather than asking you to replace what's already working. --- **Get in touch:** You can book a demo at https://deeploy.ai/book-demo/ or reach the team via https://deeploy.ai/contact/.