Cerbos vs Gateco: When to Use a Generic Policy Engine vs a Vector-DB-Native Middleware

We get asked about Cerbos regularly, usually from teams that have already evaluated or deployed it for application-level authorization. The question is almost always the same: "We use Cerbos for API authorization — do we still need Gateco for RAG?" The answer is usually yes, but it requires understanding why the two tools are shaped differently.

The core distinction: engine-shaped vs product-shaped

Cerbos solves a generic authorization problem. Given a principal, a resource, and an action — what is the decision? It is a powerful, well-designed engine that runs as a sidecar or remote service and integrates with any language that has an HTTP or gRPC client. The integration model is explicit: you write the integration. Cerbos does not know what a vector database is, what a retrieval pipeline does, or what metadata your chunks carry. It evaluates the attributes you pass it.

Gateco solves a specific problem: how do you enforce access policies on AI retrieval across a fleet of vector databases, with identity sync from enterprise IDPs, a full audit trail, and a developer experience that does not require building connector adapters? The integration model is also explicit, but the hard work is already done — 12 connectors, 4 IDP adapters, deny-by-default enforcement, and audit logging ship in the box.

The RAG integration gap for generic engines

Using Cerbos for RAG authorization requires solving several sub-problems that Cerbos does not address. First: how do you intercept vector queries? Vector database clients do not have an authorization hook. You need to build a middleware layer that sits between your application and the vector DB, executes the query, and then calls Cerbos to evaluate each returned chunk. Second: how do you carry chunk metadata into the Cerbos evaluation context? You need to resolve classification labels, owner IDs, and domain tags for each chunk and pass them as Cerbos resource attributes. Third: how do you handle the deny-by-default requirement? Cerbos returns a decision per resource; you need to implement the "missing metadata = deny" invariant in your integration layer. None of this is impossible — it is the per-vector-DB adapter problem.

When Cerbos is the right choice

Cerbos is the right choice when you need authorization across multiple services and languages beyond your RAG pipeline. If your authorization story spans Java microservices, a Go API gateway, a Python ML service, and a Node.js frontend — all with different resource types and action models — Cerbos gives you a centralized policy store with native SDKs for all four languages. If you need AuthZEN compliance today, or if your team manages policy via GitOps and wants Cerbos's native policy-file workflow, Cerbos is the right foundation. If a fully self-hosted, open-source authorization layer is a hard requirement, Cerbos ships that today.

When Gateco is the right choice

Gateco is the right choice when the primary authorization problem is retrieval. If you are building an AI product — a customer-facing assistant, an internal knowledge base, a compliance research tool — your engineers are calling a vector DB with a query and getting back chunks. Gateco intercepts that path with deny-by-default enforcement, resolves principal identity from your IDP, applies your policies, and logs every decision. No adapter to write, no metadata resolution to implement, no audit trail to build. If your team needs SSO/SCIM sync, classification suggestions, or MCP server integration for AI coding assistants — those ship in Gateco and would require significant custom work on top of Cerbos.

Using both: the layered architecture

Many enterprise teams use both — and it is a natural architecture. Cerbos handles application-level authorization: which users can perform which actions in your product, API authorization, feature flags. Gateco handles retrieval-level authorization: what knowledge can this AI see when answering this user's question. The two systems operate on different resources with different enforcement points. There is no overlap or conflict — you are adding a specialized layer for the RAG surface area that Cerbos was not designed to cover natively. See the [Cerbos comparison page](/compare/cerbos) for a full feature table.