Comparison
Both platforms decide who can do what. Cerbos gives your developers a decision engine to wire into every app, agent, and query. PlainID enforces the decision for them, natively inside agent frameworks and data platforms.
Cerbos covers more AI ground than most policy engines: MCP tool access, retrieval filtering, agent identity. The gap is in what ships ready to enforce, and in what happens to data after an agent touches it.
Full coverage: Included
Discover, Manage, Authorize: see which agents exist and what tools and data they reach, before you write a single policy.
Gap / limitation: Not documented
No inventory of agents, tools, or data sources described. Policies start from what developers already know exists.
Full coverage: Out-of-the-box Authorizers
Native enforcement inside LangChain, LangGraph, and CrewAI, plus MCP tool and parameter governance.
Partial coverage: Developer-wired
MCP servers call Cerbos through an SDK and enable or disable tools in code. Tool-level control; parameter-level not described.
Full coverage: Included
Evaluates the user's intent and prompt scope before the agent acts.
Partial coverage: Mentioned, not detailed
References policy-driven prompt filtering; no documented mechanism.
Full coverage: Enforced for you
Pre-retrieval filtering across structured and unstructured sources, applied by the Authorizer, not by your code.
Partial coverage: Query plans you apply
Returns a query plan that developers convert through ORM or vector-store adapters (Prisma, SQLAlchemy, Pinecone, Chroma, and others).
Full coverage: Included
Central policy management with distributed enforcement, authored in a console or as code.
Full coverage: Established
Open-source (Apache 2.0) stateless engine. Git-native YAML policies, SDKs in 8 languages.
Full coverage: Production-ready
Running today, with named enterprise customers and analyst recognition from Gartner and KuppingerCole.
Partial coverage: General proof only
Named customers for application and workload authorization; none found for agentic AI use cases.
Cerbos and PlainID compete for the same job. The right choice depends on who owns enforcement, and how far it has to reach.
Developer-owned decision engine
Enterprise enforcement for agents and data
50%+
Of successful cybersecurity attacks against AI agents will exploit access control issues through 2029 — Gartner, "How to Secure Custom-Built AI Agents," 11 June 2025
2B+
Authorization decisions processed annually, built for high-scale, real-time enforcement across complex environments
F500
Fortune 500 enterprises trust PlainID to secure millions of identities, recognized by Gartner and KuppingerCole in authorization and security platforms
Both platforms are policy-based, so the logic you've written carries over. What changes is who maintains the enforcement code.
PlainID's team inventories your Cerbos policies and every place your code calls the engine or applies a query plan.
Surface the agents, tools, and data platforms no developer has wired in yet.
Swap hand-built data and MCP integrations for Authorizers, then migrate application policies on your timeline, with one audit trail throughout.
Comparison
Privilege management versus runtime authorization.
Comparison
Agentic Fabric versus full-flow enforcement.
Comparison
Policy engine versus enforcement layer.
Yes. Both are policy-based runtime authorization platforms. Cerbos gives developers an open-source decision engine to call from their code. PlainID delivers the decision and the enforcement, with pre-built Authorizers for agent frameworks, data platforms, APIs, and applications.
Yes, in part. Cerbos documents tool-level access control for MCP servers, retrieval filtering for vector stores, and identifying the human behind an agent request, all integrated by developers through its SDKs. What isn't publicly documented: discovery of agents and tools, masking of AI-generated output, and named customers running agentic AI use cases in production.
Cerbos returns a query plan that developers translate into filters through ORM or vector-store adapters. PlainID enforces natively inside Snowflake and Databricks, with row-level filtering and column and cell-level masking, and syncs policies with those platforms in both directions.
The open-source engine makes decisions. Enforcing them, in every service, database, MCP server, and vector store, is code your teams write and maintain. PlainID ships that enforcement layer, with discovery and central governance on top, so coverage doesn't depend on every team wiring it in correctly.
Yes. Both use policy-based authorization, so existing logic maps across. Most teams start by replacing custom data and MCP integrations with Authorizers, then migrate application policies on their own timeline.
See runtime authorization on your own AI agents. 30 minutes, your environment, no generic slide deck.
