RAG Knowledge Base Tools: Build vs Buy, and How to Choose

1. Permissions — can retrieval respect per-user or per-tenant access, applied inside the search rather than after it? A filter bolted on after top-k retrieval can silently return nothing, or leak.
2. Source coverage — does it ingest the formats and systems you actually have (PDFs with tables, Confluence, SharePoint, ticket history), and keep them fresh?
3. Answer quality — citations, handling of 'I don't know', and behaviour on conflicting documents.
4. Data location and retention — where embeddings and queries are stored, who can see them, and whether they're used for training.
5. Integration — can the answers be embedded in your product, help desk or internal tools via API?
6. Evaluation and observability — can you see retrieved passages, failures and costs per query?
7. Exit cost — how hard is it to move your index and prompts if you change vendor?

If the corpus is public or low-sensitivity, access is the same for everyone, and the goal is employee or customer FAQ, a hosted tool is usually the right first step. Prove the value, learn which questions matter, then decide whether you've outgrown it.

Build when permissions are complex, data cannot leave your environment, answers must be tightly cited, retrieval must combine documents with structured data, or the knowledge base is a feature of your own product. Our write-ups on Enterprise RAG architecture and building production RAG with PostgreSQL and pgvector cover what to plan for. A common middle path is to start on a managed vector store and keep the ingestion and retrieval logic in your own code, so the pieces stay swappable. If you decide to build, see Enterprise RAG Architecture and our RAG knowledge systems service.

If you build, it helps to treat the pipeline as stages, each with its own failure modes and its own tests. Most 'the AI got it wrong' reports trace back to ingestion or retrieval, not the language model.

The vector database is often the least important decision, but it affects operations and cost. A few honest rules of thumb:

For a deeper look at the Postgres route, see our guide to building a production RAG system with PostgreSQL and pgvector.

Treat these as categories to price at your own volume, not as quotes. Vendor pricing changes often, and the cheapest option at demo scale is frequently not the cheapest at production scale.

A small, honest evaluation set is the single most useful asset in any RAG project. It lets you compare vendors, tune retrieval and catch regressions. Aim for real questions from real users, including ones the corpus cannot answer.

{
  "id": "q_017",
  "question": "What is the notice period for terminating the master services agreement?",
  "expected_sources": ["MSA_v3.pdf#section-14.2"],
  "expected_answer_contains": ["60 days", "written notice"],
  "user_role": "legal_ops",
  "must_not_cite": ["MSA_v1_superseded.pdf"],
  "answerable": true
}

1. Collect 30–50 real questions from the people who will use it, with the expected source document for each.
2. Load the same representative document set into each candidate.
3. Score answers for correctness, citation accuracy and appropriate refusals; include questions the corpus cannot answer.
4. Test permissions with at least two users who should see different results.
5. Estimate monthly cost at your expected query volume, including ingestion and upkeep.