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EnglishWhat Is Prediction Guard?
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Prediction Guard is a self-hosted, sovereign AI control plane that gives enterprises operational control over every AI agent action from inside their own security boundary. It is built for organizations running fleets of AI agents at scale that need runtime enforcement of behavioral policies and governance requirements aligned with NIST and OWASP — without sending agent traffic or control logic outside their environment.
The Core Idea
Most AI tooling sits in front of models as a gateway: a door that lets requests through. Prediction Guard positions itself differently — as the building's entire security and management system, not just the door. The control plane sits underneath your agents and enforces policy on every move they make, so neither human users nor the agents themselves have to make the "right" decision for controls to apply.
Two properties define the model:
- Sovereign and self-hosted — it runs inside your security boundary, in your operating environment, rather than as a vendor-hosted layer you route through.
- Zero trust for AI agents — no agent action is trusted by default; runtime controls constrain behavior continuously.
What It Actually Does
Agent Forge
Agents can be created on day one, either interactive or autonomous, through an API or natural-language prompts. Key systems connect via MCP, and human plus agent identities are tracked out of the box. Runtime controls then enforce behavioral policies on those agents.
API / SDK Compatibility
An OpenAI-, Anthropic-, and MCP-compatible API sits on top of the control plane, whether you use self-hosted or cloud endpoint models. Developers keep their existing frameworks — LangGraph, Pydantic AI, Crew AI, Claude Code, Hermes Agent, OpenAI SDKs — while the control plane enforces runtime controls underneath.
Governance Alignment
The platform targets alignment with NIST and OWASP governance best practices, and the vendor claims a single comprehensive solution replaces many of the distinct services otherwise needed in Azure to meet NIST 600-1 guidance.
The Claims Behind the Positioning
Prediction Guard publishes three headline figures. Treat these as vendor-reported and check the methodology before relying on them:
| Claim | Figure | Stated basis |
|---|---|---|
| Fewer services | 90% | Distinct services required in Azure to align with NIST 600-1, vs. a single solution |
| Faster to prod | 300% | Organizations moving from fragmented API management to unified AI gateway patterns |
| Lower TCO | 4× | TCO analysis of one control plane vs. integrating fragmented AI security point solutions |
The "faster to prod" figure is described as reducing system delivery from a traditional 2–6 months to immediate availability and eliminating up to 85% of API definition overhead.
Deployment and Ecosystem
Deployments are described as sovereign and suited to challenging environments. Prediction Guard was invited into the Docker Verified Publisher agentic program and released a verified Agent Control Kit on Docker Hub, so agents can run at scale inside Docker-verified Prediction Guard sandboxes on your own control plane.
Who It's For
Enterprises operating AI at scale that need to limit blast radius, contain problematic agent behavior, and satisfy internal or regulatory governance requirements inside their own boundary. If you are running a single prototype agent, the control-plane overhead is likely more than you need. If you are managing a fleet across environments with auditors or security teams asking how agent behavior is constrained, this is the problem it targets.
Pricing, licensing, and login requirements are not specified in the available material — book a demo or review the vendor's documentation to confirm commercial terms and deployment prerequisites.