AI governance platforms compared: program vs runtime

Which AI governance platform should you choose? First decide which of two layers you are buying. Program governance platforms — Credo AI, IBM watsonx.governance, OneTrust AI Governance, Holistic AI — keep an inventory of AI systems, run risk assessments and map controls to the EU AI Act, NIST AI RMF and ISO/IEC 42001. Runtime governance gateways — Prisma AIRS AI Gateway, Kong AI Gateway, Cloudflare AI Gateway, LiteLLM and INFRO — sit on the request path and decide, for each call, which models and providers a team may use, with which key, inside which budget and under which guardrails. Most enterprises need both. Startups and finance teams usually need runtime governance first, because it is the layer that actually stops an unapproved call or an overspend.

Below: how to tell the layers apart, a comparison table, each product described from its own pages as of October 2026, and the questions that decide it.

What is the difference between program and runtime AI governance?

Program governance answers "what AI do we run, how risky is it, and can we prove we manage it?" Its users are risk, legal and compliance teams; its outputs are a register, assessments, policies and evidence for an auditor or regulator. Runtime governance answers "may this request go to this model, with this data, on this budget, right now?" Its users are platform and security engineers; its outputs are refused requests, redacted prompts, enforced budgets and a log of every call.

The two meet at evidence. A program tool needs to know which models each system uses and what controls apply; a runtime gateway knows exactly that, because every request passes through it. The EU AI Act guide for platform teams lists the duties, such as keeping logs and assigning human oversight, where runtime records help a program team.

Neither layer replaces the other. A register that says "only approved models are used" is a claim; a gateway that refuses unapproved models is the control behind it. A gateway without a program has controls nobody has mapped to a risk.

Which AI governance platforms are worth evaluating?

Which platforms handle program and compliance governance?

Credo AI describes an AI Registry to "discover and catalog every AI system, agent, vendor and model", with shadow AI detection, risk classification and dependency mapping, continuous risk assessment with an agentic risk and control library, and "policy packs for every major regulatory framework": it names the EU AI Act, NIST AI RMF, ISO 42001, OMB M-25, CO ADMT and NAIC AI. Its policy engine records evidence and produces audit-ready outputs; an "Agent Governor" is listed as a research preview¹.

IBM watsonx.governance says it helps enterprises "govern any AI, anywhere": a governance graph of the AI estate, risk management across the lifecycle, continuous monitoring for compliance and operational issues, shadow AI detection, and automated policy enforcement, obligation mapping and compliance evidence capture. Its page shows the EU AI Act, NIST AI and ISO 42001 without describing how each is supported².

OneTrust AI Governance inventories "AI systems, models, agents, datasets, vendors, projects, and use cases", assesses them, revalidates risk when a model or dataset changes, and offers EU AI Act, NIST AI RMF and ISO 42001 templates. It also reaches into runtime: its page describes filtering prompts and outputs, an AI Guard SDK that blocks or redacts sensitive content before it reaches a model, and governing agent actions and MCP environments³. If your privacy program already lives in OneTrust, start there.

Holistic AI discovers models, agents and applications across cloud, code and SaaS, red-teams them for prompt injection and jailbreaks, monitors drift in production, maps controls to the EU AI Act, NIST AI RMF and ISO/IEC 42001, and describes Guardian Agents that can block, redirect or escalate agent actions⁴.

Which platforms govern AI at runtime, on every request?

Prisma AIRS AI Gateway is Portkey's hosted gateway under Palo Alto Networks: Portkey's pricing page says "Portkey is now PRISMA AIRS AI Gateway, generally available for all enterprises". Its Enterprise offering lists role-based access control, partner guardrails, a PII anonymizer, VPC hosting and private tenancy, and its top tier lists SSO and "Granular Budget & Rate Limits"; the open-source gateway lists routing and guardrails without RBAC or budgets⁵.

Kong AI Gateway lets consumer groups "scope model access and token budgets by team or department", calculates per-request cost and enforces spend limits against it, redacts PII "before it reaches the upstream LLM provider", blocks disallowed topics and detects jailbreak attempts, checks responses with its own or a cloud vendor's guardrails, and governs tools from external MCP servers with OAuth2-scoped access⁶.

Cloudflare AI Gateway guardrails evaluate prompts and responses for hazards such as violence, hate and sexual content, flagging or blocking them⁷. Core features are free; guardrails run on Llama Guard on Workers AI and are billed as Workers AI inference, so cost "scales with the length of the prompts and responses being evaluated"⁸.

LiteLLM gives each user, team and project a virtual key that "has its models, budgets, and rate limits", tracks spend by key, team, user and tag, and applies guardrails such as content filters and PII masking to requests and responses. Its page places SSO/SAML, audit logs and multi-team management in LiteLLM Enterprise⁹. You run it yourself.

INFRO, and when it is not the right choice

INFRO is a hosted gateway and control plane that runs only on provider accounts you connect: you keep your contracts and your provider bills you directly, and INFRO charges a software subscription with 0% markup on model spend. Its runtime governance, as shipped:

• Model access: an allowlist per project; a model outside it returns a policy error before any provider is called.
• Keys: a gateway key per application or team, limited to chosen providers or IP ranges, rotated with an overlap window, each in exactly one environment.
• Budgets: hard caps per organization, project, member and key, checked before the call, on every request because every request is on your own keys.
• Guardrails: block, redact or flag PII, secrets and your own terms in prompts and responses, plus optional classifier checks on your own decision-model connection (Business plan; detection can miss things).
• People: owner, admin, developer and analyst roles, project access for members, SSO and SCIM from the Business plan, two-factor and passkeys on every plan.
• Evidence: an audit log of member, key and policy changes with actor and timestamp; a request record with estimated provider cost; prompt logging off by default with a retention window you choose.
• Agent tools: an MCP gateway that grants keys pinned, approved tools and logs each call (Business plan).

Plans run from Developer at $35 a month to Business at $599, each with a 14-day trial; Enterprise is a conversation. The security page describes the controls in more detail. INFRO does not map controls to the EU AI Act, NIST AI RMF or ISO/IEC 42001, keeps no AI system register or risk assessments, and claims no certification.

It is not the right choice if you need a program governance register and regulatory policy packs (Credo AI, OneTrust, IBM, Holistic AI), want to self-host without an enterprise agreement (LiteLLM or Kong), already run Kong as your API platform, want guardrails at no subscription on Cloudflare, or want the gateway bought from your security vendor (Prisma AIRS).

Which AI governance platform is best for enterprise teams?

Enterprise teams usually need one platform per layer, owned by different people. The program platform belongs to risk and compliance, so choose the one they will keep current; the runtime gateway belongs to the platform team, so choose by how they operate. Then connect the two: the gateway's model list, key inventory and audit log become the evidence the program records.

Which AI governance platform should AI startups choose?

Startups should begin with runtime governance, because their real risks are a leaked key, an unapproved model in production and a runaway bill, not a missing risk register. One gateway, a key per application, an approved model list and budgets cover those. Finding shadow AI covers the traffic that bypasses it. Add a program tool when an enterprise customer's questionnaire or a regulator asks for an inventory and assessments.

Which AI governance platform is best for finance teams?

Finance teams need spend governance on the request path: budgets that refuse a request before the provider is called, cost attributed to the project, key and end customer that caused it, and estimates they can check. In INFRO, estimates use your negotiated rates from the Team plan and are compared daily against OpenAI's and Anthropic's own cost APIs on Business; provider cost stays separate from INFRO's subscription. A regulated financial firm will also need a program platform for model risk documentation, such as one with a framework pack for its regulator.

How do you decide which layer to buy first?

1. Ask who is asking. A regulator, auditor or board points to program governance; an incident, a bill or an engineer points to runtime.
2. List your controls that must act before a model call: model access, keys, budgets, guardrails. Only a runtime gateway enforces those.
3. Write the model access policy once, then check that the gateway can express it.
4. Ask each program vendor how it ingests runtime evidence, and each gateway how it exports its audit and request logs.
5. Trial the shortlisted gateway on real traffic for a week before signing either contract.

Summary

"AI governance platform" names two layers. Program governance (Credo AI, IBM watsonx.governance, OneTrust, Holistic AI) manages the inventory, risk and regulatory evidence; runtime governance (Prisma AIRS, Kong, Cloudflare, LiteLLM, INFRO) enforces which models, keys, budgets and guardrails apply to each request. Enterprises usually need both; startups and finance teams usually start at runtime, where an unapproved call or an overspend is stopped rather than recorded.