Buying snapshot
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| Decision point | What Adeptiv AI states |
|---|---|
Deployment | SaaS plans, plus hybrid and on-prem deployments for data sovereignty and regulated environments |
Features by plan | Every plan includes the full feature suite; higher plans differ only in users/projects, integrations and deployment options |
Integrations | REST API and SDKs for validation gates in CI/CD pipelines; ingests metadata from data layers, model registries, code repositories and security infrastructure; integrates with SHAP, LIME and other explainability libraries |
Access control | Granular roles and permissions, and roles and access levels per AI project team |
Time to start | Typical piloting and inventory can begin within 2–6 weeks, depending on scale and integration complexity; custom AI compliance policies onboard in under 24 hours |
Support | Starter: email and onboarding support. Enterprise and On-Prem: dedicated customer success managers, priority SLAs and optional 24×7 coverage. All paid plans include guided onboarding; Enterprise customers get dedicated training sessions for admin, risk and compliance teams |
Our take
Adeptiv AI is worth a trial if you need one register that ties each AI system to its risks, controls and applicable regulations, and you can run a sales-led evaluation. If you only need to govern one or two AI systems, the trial itself may already cover what you need to learn.
Evaluate Adeptiv AI
Comparisons
Where it stands out
Regulation mapping. Adeptiv AI says it maps your AI inventory to every applicable regulation across the EU AI Act and 30+ global frameworks, and cross-maps controls to avoid redundant work. Its homepage says it supports 38+ global AI regulations, and the platform page lists NIST AI RMF, GDPR, DORA, HIPAA, Colorado AI Act and NYC LL 144 among them. This matters most when one AI system falls under several rules at once.
Controls and policy drafts. It suggests controls, and the documents that back them, from each AI system's risk profile and regulatory scope, and drafts AI policies aligned with major global standards, so teams do not start from a blank page.
Shadow AI discovery. The platform surfaces AI systems, models, agents and third-party applications, including shadow AI, into a continuously updated central AI register. Each use case is then managed from onboarding and approval through production, retirement and archival.
Structured risk scoring. Risk assessment uses Adeptiv's 12-Pillar Responsible AI Risk Framework and a database of 2,000+ AI risks, covering bias, explainability, privacy, security, operational and compliance risk. It auto-detects the risk level of each AI use case, then tracks mitigation with workflows for ownership, actions and compensating controls.
Runtime monitoring. An Observability SDK monitors live systems for hallucinations, bias drift, PII exposure and prompt injections, so governance does not stop at the approval step.
Third-party AI. Vendor risk assessment covers third-party AI models and embedded AI in enterprise software, with automated assurance checks and risk gap assessments for AI procurement.
Audit readiness. The platform collects, validates and organizes compliance records against mapped controls and exports audit-ready packs and regulator-ready reports on demand.
Product screenshot
Trade-offs to weigh
The trial tests a workflow, not a rollout. With one user seat and two AI use cases, a cross-functional team cannot try approval and ownership workflows together before buying.
Enterprise deployments can add project cost. Cloud and on-prem enterprise deployments may include implementation costs, depending on scope and integration needs, so the subscription is not the whole bill there.
Headline savings are the vendor's own. Adeptiv AI claims up to 70% lower AI compliance costs and 60% faster AI deployment versus traditional programs; treat these as marketing figures to test in your pilot.
Integration effort sets the timeline. The 2–6 week start depends on scale and integration complexity, so an estate with many model registries and repositories should plan for the longer end.
Pricing and plan conditions
Custom pricing depends on the scope of work, number of users and projects, required integrations, security posture and deployment type (cloud or on-prem). Prepaid annual subscriptions get up to a 15–20% discount, and Enterprise clients can choose multi-year commitments with custom terms. SaaS plans have no setup fee. You can upgrade or downgrade at any time; the change can be immediate or take time, depending on data and integrations. When the 30-day trial ends, you upgrade to a paid plan to keep your data. The page introduction says to "start with a free Starter plan", while its FAQ describes the trial as the step before a paid plan, so confirm the Starter terms with sales.
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| Plan | Price basis | Limits and inclusions |
|---|---|---|
30-Day Trial | Free for 30 days | 1 user seat, 2 AI use cases, full access to all features |
Starter | Called a "free Starter plan" in the page introduction; confirm with sales | For small teams: up to 10 user seats, up to 20 AI use cases, full access to all features |
Enterprise (larger organisations) | Custom pricing | Custom users and projects limits, tailored workflow and integrations, dedicated training and support |
Enterprise (on-prem control) | Custom pricing | For highly regulated organisations needing full data residency and on-prem control: unlimited users and projects limits, full data isolation and control, tailored workflow and integrations, dedicated training and support |
Alternatives to Adeptiv AI
The reviewed substitutes for Adeptiv AI among the AI governance, and what would make each one the better answer.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- DataHub
- An AI governance tool and a data catalog overlap on policy, ownership and documentation, and differ on whether the subject is models or data assets. The decision is whether governance for AI needs its own system or extends the catalog already in place.Applies to: Whether AI governance extends the existing data catalog or needs its own system.
- Alation
- An AI governance tool and a data catalog overlap on policy, ownership and documentation, and differ on whether the subject is models or data assets. The decision is whether governance for AI needs its own system or extends the catalog already in place.Applies to: Whether AI governance extends the existing data catalog or needs its own system.
- Collibra
- An AI governance tool and a data catalog overlap on policy, ownership and documentation, and differ on whether the subject is models or data assets. The decision is whether governance for AI needs its own system or extends the catalog already in place.Applies to: Whether AI governance extends the existing data catalog or needs its own system.