Private RAG

What is Private RAG?

Private RAG differs from cloud RAG in three ways: where the embeddings live, who controls retrieval policy, and whether retrieval is audited as part of the AI platform. See the Private RAG pillar and Private RAG vs Enterprise Search.

What is an example of Private RAG?

A legal team indexes contracts inside its private environment, carries matter-level access controls into each chunk, uses a locally hosted embedding model, and generates answers with citations without exposing contract text to a public API.

How is Private RAG different from related concepts?

RAG describes the retrieval-plus-generation pattern. Private RAG adds control over data location, access, processing, and evidence. Enterprise search may retrieve documents without generating a synthesized answer.

Why it matters for on-premise & regulated AI

RAG is where your most sensitive data — contracts, patient records, source code — meets the model. Private RAG keeps embeddings, the vector index, and retrieval inside your boundary, and adds permission-aware retrieval so an agent can only surface passages its requesting user is authorised to see. Cloud RAG services replicate your documents into someone else’s infrastructure; private RAG makes that replication unnecessary.

What should enterprises evaluate for Private RAG?

• Map the full data path, including OCR, embeddings, reranking, model inference, telemetry, backups, and support access.
• Evaluate retrieval recall, citation correctness, permission enforcement, freshness, and abstention on unanswerable questions.
• Separate indexes, caches, credentials, and logs where tenant or information-barrier rules require it.

Related terms

• Permission-Aware Retrieval
• Vector Database
• Embeddings
• Self-Hosted RAG
• Sovereign RAG