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How Gateco compares
Gateco is the permission-aware retrieval layer for AI. Here is how it lines up against policy engines, database row-level security, enterprise search, and the option of building it yourself.
Gateco vs Cerbos
Vector-DB-native, deny-by-default retrieval enforcement against a generic authorization engine. When to use each, and when to use both.
Read the comparison →Gateco vs pgvector RLS
Where Postgres row-level security gets you, and what breaks once you pass a handful of tenants in a RAG pipeline.
Read the comparison →Gateco vs Microsoft Purview
Securing the AI you ship in your own product versus controlling what M365 Copilot can see inside the Microsoft stack.
Read the comparison →Gateco vs Glean
An authorization layer for the RAG stack you build yourself versus a full, closed enterprise search product.
Read the comparison →Gateco vs Pinecone RBAC
Per-retrieval, per-principal policy enforcement against index and namespace-level API key permissions.
Read the comparison →Gateco vs Oso
Retrieval-time authorization purpose-built for vector search against a general application authorization library.
Read the comparison →Gateco vs OPA / Rego
Purpose-built RAG authorization against general-purpose policy as code, and how the two fit together.
Read the comparison →Build vs buy
What it actually takes to build RAG authorization in-house: policy engine, metadata resolution, audit, connectors, and identity sync.
Read the comparison →Not sure which fits?
Start with the build-vs-buy breakdown, or talk to us about your stack and requirements.