AI Answer · AI Infrastructure
Do I need a vector database for my AI app?
Direct answer
Probably not at first. If the content your AI needs is small, put it directly in the prompt — no vector database required. If you do need semantic search over your own documents, most apps should start with a vector extension on the database they already run (such as pgvector on Postgres), which keeps embeddings next to your data and supports metadata filtering and per-user permissions. A dedicated vector database earns its cost only at large scale, high query volume, or strict latency targets — and you should let benchmarks, not hype, tell you when you have crossed that line. Adding one too early is a common, avoidable form of over-engineering.
Quick facts
- Many AI apps need no vector database at all — the data fits in the prompt.
- If you do need semantic search, you do not necessarily need a dedicated one.
- Postgres with pgvector handles small-to-mid corpora alongside your real data.
- Dedicated vector DBs earn their keep at large scale and high query volume.
- Keyword or hybrid search often beats pure vector search for many queries.
- Start with the simplest store that works; scale the index when metrics demand it.
Three tiers, simplest first
No vector store — fits in the prompt
If the relevant content is small (a few documents, a policy, a product list), just put it in the prompt or select it with normal queries. Adding a vector database here is pure over-engineering and extra cost to maintain.
Postgres + pgvector — most RAG apps start here
When you need semantic search over your own content, a vector column in the database you already run keeps embeddings next to your real data, supports metadata filtering and per-user permissions, and avoids a second system. This covers a large share of business RAG apps.
Dedicated vector database — at real scale
Purpose-built vector stores shine with very large corpora, high query throughput, and demanding latency targets. Reach for one when your benchmarks show the in-database approach is the bottleneck — not before.
Vector search is not always the right search
Vector (semantic) search is powerful for "find passages that mean something similar," but it is not universally best. For exact terms, product codes, names, or filtered lookups, keyword search is often more precise. Many strong systems use hybrid search — combining keyword and vector results — which can beat either alone. The takeaway: decide what kind of matching your queries actually need before assuming the answer is a vector database. Sometimes the best retrieval upgrade is better keyword search or smarter chunking, not new infrastructure.
A simple decision rule
- Content fits in a prompt or a normal query? No vector store.
- Need semantic search, small-to-mid corpus? Vector extension on your existing database.
- Large corpus, high QPS, strict latency, proven bottleneck? Dedicated vector database.
- Whatever you choose, enforce per-user permissions at retrieval time.
How QUANT LAB USA approaches it
QUANT LAB USA defaults to the simplest store that meets the need — usually a vector extension on the database you already operate — and introduces a dedicated vector database only when measurements justify it. The retrieval design is part of the larger RAG picture; see what RAG is for business, keep it secure with how to stop an AI app from leaking data, and budget it using the cost to build an AI chatbot.
Not sure whether your corpus needs a dedicated vector database or just a column in Postgres? Walk through the numbers.
Talk to QUANT LAB USASources and methodology
This guidance reflects QUANT LAB USA's engineering practice for US clients. For service detail see quantlabusa.dev/services, and the glossary defines vector database, embeddings, and hybrid search. Tools are referenced neutrally; no placement is paid.
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