Comparison
Gateco vs Pinecone native RBAC
Pinecone's metadata filters let you pre-filter results by fields stored at index time. Gateco adds dynamic principal context, cross-connector unified policies, and a complete audit trail. They are not alternatives. They address different layers of the authorization problem.
Bottom line
Pinecone metadata filters run inside Pinecone and are fast for static partitioning such as tenant, language or document type, but the values are fixed at index time and carry no record of who retrieved what. Gateco is a purpose-built permission-aware retrieval layer: it resolves the current groups, department and attributes of the requesting principal from your IDP, applies one policy model across 12 vector databases, fails closed on evaluation error, and logs every decision. Most production setups use both, with filters for partitioning and Gateco for identity-aware enforcement.
| Capability | Gateco | Pinecone filters |
|---|---|---|
| Dynamic principal context (IDP-synced groups, dept, attributes) Pinecone filters operate on static metadata stored at index time | Yes | No |
| Audit trail per retrieval (who accessed what, which policy) | Yes | No |
| Policy versioning | Yes | No |
| Fail-closed on evaluation error | Yes | No |
| Cross-connector unified policy model Gateco one policy can cover Pinecone + pgvector + Azure AI Search | Yes | No |
| RBAC / ABAC / ReBAC conditions | Yes | No |
| Index-time metadata filtering | Yes | Yes |
| Retroactive resource registration Both support enumerating existing vectors | Yes | Yes |
| MCP server | Yes | No |
| Python + TypeScript SDK | Yes | Yes |
| Grounded answers (policy-filtered LLM synthesis) | Yes | No |
| No additional infrastructure Gateco requires a separate deployment; Pinecone filters are built in | No | Yes |
The metadata filter problem
Pinecone metadata filters are powerful for static partitioning: tenant_id, language, document_type. They work at query time inside Pinecone, and they are fast. The limitation is that they are static. The metadata must be stored alongside each vector at index time. If a user's group membership changes, the filter does not know. The field value in the index does not update.
Gateco policy evaluation is dynamic. It resolves the requesting user's current group membership, department, and attributes from your IDP at retrieval time. If a user moves from the engineering group to the executive group, their next retrieval reflects their new access with no re-indexing required.
Complementary, not competing
The common production pattern is to use both. Pinecone metadata filters handle fast, static partitioning (tenant isolation, language filtering, document type). Gateco handles the dynamic, identity-aware layer on top, evaluating who the requesting principal is right now and whether current policies allow this specific retrieval.
Gateco's audit trail is the other gap that metadata filters don't fill. Filters are a query mechanism; they don't record who retrieved what, which policy evaluation produced a denial, or give you per-principal retrieval history for compliance reporting.
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