Comparison
Gateco vs Cerbos
Cerbos is a general-purpose authorization engine. Gateco is a retrieval-specific security layer for AI and RAG pipelines. They solve different problems, and can be used together.
Bottom line
Cerbos is a general-purpose authorization engine: Apache 2.0 core, SDKs for six languages, self-hostable today, and no built-in vector database connectors. Gateco is a purpose-built permission-aware retrieval layer with 12 native vector database connectors, IDP sync, deny-by-default enforcement, and an audit trail of every retrieval decision. Pick Cerbos to build authorization across your services; pick Gateco to enforce it on AI retrieval, or run both at their respective layers.
| Capability | Gateco | Cerbos |
|---|---|---|
| Vector-DB-native deny-by-default Cerbos is a generic engine; RAG integration requires custom query-plan adapters per DB | Yes | No |
| 12 vector DB connectors out of the box Cerbos has no built-in vector DB connectors | Yes | No |
| Classification suggestions (rule-based) | Yes | No |
| L0–L4 semantic readiness model | Yes | No |
| MCP server (Claude, Cursor, etc.) | Yes | No |
| Multi-mode search (vector/keyword/hybrid/grep) | Yes | N/A |
| ABAC & RBAC policies | Yes | Yes |
| Audit trail of denied decisions Cerbos: decision logs; Gateco: 50+ audit event types with deny-by-default context | Yes | Yes |
| Open-source SDK / policy engine Cerbos core is Apache 2.0 open source | On roadmap | Yes |
| AuthZEN compliance | On roadmap | Yes |
| Multi-language SDK | Python + TS | 6 languages |
| GitOps policy distribution | partial | Yes |
| Self-host / on-premises Cerbos PDP can be self-hosted today | roadmap (Q3 2026) | Yes |
| Pricing transparency | Public | Contact sales |
| SOC 2 Type II | In progress (H2 2026) | Yes |
Gateco is product-shaped. Cerbos is engine-shaped.
Cerbos solves a generic authorization problem: given a principal, a resource, and an action, what's the decision? It's a powerful, well-designed engine, and it requires you to build the integration layer for every system you want to protect.
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 doesn't require building connector adapters?
The framing that holds: Cerbos is what you build with. Gateco is what you deploy. If you want maximum control over your authorization model across multiple languages and services, Cerbos is the right foundation. If you want RAG retrieval security running in production in an afternoon, Gateco is the right choice.
When to choose Cerbos
- You need authorization across multiple services and languages (Java, Go, Node, Python, etc.)
- Your policies are managed via GitOps and you want Cerbos's native policy-file workflow
- You need a fully self-hosted, open-source authorization layer today
- Your authorization requirements go well beyond retrieval (API authorization, feature flags, UI visibility)
- AuthZEN compliance is a hard requirement today
When to choose Gateco
- You're building AI/RAG pipelines and need retrieval-specific security out of the box
- You need deny-by-default enforcement across multiple vector databases with no custom adapters
- Your security team needs an audit trail for every retrieval decision, exportable for compliance
- You need IDP sync (Azure Entra, AWS IAM, Okta, GCP) feeding principal data into your policies
- Your team uses Claude, Cursor, or other MCP-compatible AI tools and needs a Gateco MCP server
- EU AI Act compliance is a near-term requirement and you need the audit evidence today
Can you use both?
Yes, and it's a natural architecture for enterprise teams. Cerbos handles application-level authorization (who can perform which actions in your product). 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's no overlap or conflict. You're adding a layer specifically for the RAG/AI surface area that Cerbos was not designed to cover natively.
See Gateco in your stack
Five lines of Python to first secured retrieval. No adapters to build.