# AI governance that goes model deep > See how Deeploy compares to Holistic AI: model-level controls, bias and drift monitoring, explainability, and regulator-ready evidence in one platform. **Source:** https://deeploy.ai/compare-holistic-ai/ **About this file:** Machine-readable markdown version of the page above, for LLMs and AI assistants. Regenerated automatically whenever the page is updated. ## The Holistic AI alternative that connects governance directly to AI models Holistic AI helps organisations assess AI risk and align governance frameworks. Deeploy combines governance, deployment, monitoring, and controls in a single platform where AI models actually run. ## The problem with governance detached from AI systems Your governance team uses Holistic AI to manage AI inventories, conduct impact assessments, and document compliance requirements. But there's a fundamental challenge: The governance platform sits separately from the AI systems it's governing. When a governance control needs to be enforced, monitored, or audited, you depend on engineering teams, external tooling, and manual processes to prove it happened. - ✗ Policies and assessments live in one system. Models run somewhere else. - ✗ Governance requirements are documented, but not automatically enforced on running AI systems. - ✗ Compliance teams gather information from multiple tools, teams, and environments. - ✗ Technical and governance teams work in silos. ## One platform. One source of truth. Holistic AI governs AI systems from the governance layer. Deeploy governs AI systems from the governance layer and the model layer. ### Policy & framework layer (What Deeploy & Holistic AI do) - AI Use Case inventory - Risk assessment workflows - Policy alignment ### Technical evidence layer (What only Deeploy does) - Live AI model inventory - Governance model controls - Bias & drift monitoring - Per-prediction audit trails - Model explainability ## How Deeploy compares to Holistic AI Holistic AI helps you track governance on paper. Deeploy turns it into real, operational controls. | Feature | Deeploy | Holistic AI | | --- | --- | --- | | AI Use Case Inventory | Yes | Yes | | AI Model Deployment | Yes | n/a | | AI Impact Assessments | Out-off the box templates + create your own | Yes | | Risk & Policy Workflows | Yes | Yes | | Evidence for Audits | Per prediction, lifecycle logs | n/a | | Bias & Drift Monitoring | Built-in | n/a | | Model Explainability | Native, regulator-ready | n/a | | EU AI Act Alignment | Technical & operational | Policy-level only | | Stakeholder Dashboards | For business, compliance & tech teams | Governance & policy focus | ### Already using Holistic AI? Here's how Deeploy can work with, or simplify, your current stack. #### Complement Holistic AI Keep Holistic AI for governance workflows and assessments. Add Deeploy for technical model governance: - Holistic AI: Inventory AI use cases, assessments, reporting. - Deeploy: Model deployment, technical controls, monitoring, audit evidence. - Result: Complete (but fragmented) governance. #### Replace Holistic AI entirely Deeploy offers most Holistic AI functionalities PLUS model-level control and monitoring: - Everything Holistic AI does: AI inventory, policy alignment, risk workflows. - Plus everything Holistic AI can't do: Model deployment, governance controls, continuous monitoring, explainability, audit trails. - Result: Full-stack governance in one platform. ## Why teams choose Deeploy over Holistic AI ### Governance that reaches the model **Holistic AI catalogs AI at the policy level. Deeploy monitors bias, drift, and explainability at the model level, generating regulator-ready logs that prove compliance.** ### Operational governance **Policy frameworks in Holistic AI stay as documentation. Deeploy takes those policies and maps them to operational controls, automated checks applied directly to models in production.** ### One platform for compliance and engineering **Compliance officers see policy alignment. Data scientists see model performance. Engineering sees deployment metrics. One platform, three critical perspectives.** **Bottom Line:** Holistic AI helps you govern AI systems. Deeploy lets you govern, deploy, monitor, and prove compliance from the same platform where AI models run. ## 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. Monitor and govern AI agents from a single platform, with full visibility into agent behaviour, tool usage, and decision flows. Apply governance controls and maintain audit-ready records as agents operate in production. 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 Holistic AI and Deeploy? Holistic AI focuses on policy frameworks and governance workflows, helping you catalog AI use cases, assess risks, and align with governance standards. Deeploy delivers **policy frameworks AND operational governance.** Use case inventory and risk workflows, plus model-level controls that enforce those policies in production. Map governance requirements to automated checks, monitor models for bias and drift, generate explainability for every prediction, and produce automatic audit trails. ### We already use Holistic AI for AI governance. Why would we need Deeploy? Holistic AI helps organisations establish governance processes through AI inventories, impact assessments, policy frameworks, and compliance reporting. Deeploy addresses a different challenge: governing AI systems directly where they are deployed and operating in production. Because AI models run directly on Deeploy, the platform can continuously monitor model behaviour, apply governance controls, generate audit evidence, and provide explainability at the model level. **Common scenario:** You document a bias monitoring policy in Holistic AI. Great. But who's actually monitoring for bias? What happens when drift occurs? Can you show regulators the evidence? **Your options:** 1. **Complement Holistic AI:** Keep it for policy frameworks, add Deeploy for technical monitoring & governance controls. 2. **Consolidate to Deeploy:** Deeploy provides you with policy frameworks + technical governance in one platform. All teams in one place. Regulatory ready. ### Can Deeploy replace Holistic AI entirely? For most organisations, yes. **What Deeploy includes from Holistic AI's core:** - AI system inventory - Risk classification workflows - Compliance documentation - Approval workflows - Stakeholder dashboards **What Deeploy adds that Holistic AI doesn't have:** - EU AI Act, ISO 42001, NIST control frameworks - Real-time technical controls - Audit trails - Model deployment capability - Model explainability - Bias & drift monitoring **Bottom line:** If you need governance frameworks + technical evidence (which most regulated organisations do), Deeploy delivers both. If you only need policy documentation without operational monitoring, Holistic AI might suffice, but that's increasingly insufficient for regulators. ### Do we need both Holistic AI and Deeploy? Not necessarily. Some organisations use both platforms because they serve different purposes. Holistic AI can be used to manage governance frameworks, assessments, and reporting. Deeploy can be used to deploy AI systems, monitor model behaviour, enforce governance controls, and generate technical evidence. For organisations with mature governance programs already built around Holistic AI, Deeploy often serves as the operational layer that extends governance into production AI systems. For organisations evaluating new platforms, Deeploy can often cover both governance and operational governance requirements within a single environment. ### Holistic AI already supports EU AI Act compliance. How is Deeploy different? Both platforms support organisations preparing for the EU AI Act. The difference is in how compliance is demonstrated. Holistic AI primarily focuses on governance processes, assessments, documentation, and compliance readiness. Deeploy focuses on operational compliance. Because AI systems are deployed and monitored directly within Deeploy, organisations can generate the technical evidence regulators increasingly expect, including: - Model documentation - Explainability outputs - Audit logs - Monitoring records - Risk control evidence - Model lifecycle tracking In practice, governance documentation alone is often not sufficient during audits. Organisations also need evidence showing how controls were implemented and maintained over time. ### What evidence can Deeploy provide for regulatory audits? **Comprehensive technical evidence that satisfies auditors:** **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, 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 - Data warehouses: Snowflake, Databricks, BigQuery - Open-source frameworks: Triton, PyTorch, Hugging Face, XGBoost. - Webhooks & slack integration for alerts and events Your ML stack is already complex. Deeploy complements it with a strong governance layer rather than forcing you to change everything. --- **Get in touch:** You can book a demo at https://deeploy.ai/book-demo/ or reach the team via https://deeploy.ai/contact/.