AI Guides

Vector databases for AI

Understand vector databases beyond buzzwords.

Guide notice

Guides provide decision frameworks and topic overviews. They link to related comparisons, tools, pricing, and benchmarks so you can verify details in context.

Editorial status

Published 2026-07-30 · Last reviewed 2026-07-30 · Next review due 2027-01-26

  • Review cadence: Every 6 months
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  • Review status: Current
  • Evidence level: editorial
  • Content owner: ONULSURI Editorial

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Introduction

A vector database stores embeddings and supports nearest-neighbor search, often with metadata filters. In AI products it usually sits behind RAG or semantic search rather than replacing a primary system of record.

Evaluate freshness, filtering, hybrid search, operations, and security—not only advertised index speed.

Vector databases store embeddings and support similarity search at scale. They are infrastructure, not a strategy: chunking, metadata filters, hybrid retrieval, and evaluation decide whether users trust results. Start with the retrieval job and corpus owners before shopping vendors.

Who it is for

  • Engineers designing RAG backends.
  • Architects comparing managed vector services.
  • Buyers reviewing AI search infrastructure claims.
  • Platform teams comparing managed vector stores vs built-in product search.

Decision framework

  1. Confirm you need a dedicated store

    Small corpora may live in existing search stacks; large or frequent RAG often needs specialized indexes.

  2. Require metadata filters

    Support tenancy, document type, and ACL fields beside vectors.

  3. Plan upsert and delete paths

    Knowledge bases change; indexes must stay synchronized.

  4. Review security boundaries

    Treat vectors and metadata as sensitive if source docs are sensitive.

  5. Plan metadata and access control

    Filter by tenant, ACL, and document type so similarity search cannot leak restricted content across teams.

Comparison overview

Purpose-built vector DB

Optimized for similarity search and RAG patterns.

Search engine with vectors

Useful when hybrid keyword + vector already exists in-house.

App database extensions

Fine for prototypes; watch scale and operational limits.

Common mistake to avoid

Buying a vector database before defining chunking, evaluation queries, and who updates the corpus.

FAQ

Is a vector database an LLM?

No. It stores and searches vectors; an LLM may consume retrieved text afterward.

Do I always need one for RAG?

Not always. Small document sets can use simpler retrieval, but vector indexes help as corpora grow.

What is metadata filtering?

Restricting search to rows that match fields like user, project, or document type.

How do I evaluate a vendor?

Test recall on your queries, check hybrid search, and review security and sync tooling.

Do I always need a dedicated vector database?

No. Small corpora may live in an application database or a product's built-in retrieval. Dedicated stores help at scale and with hybrid search needs.

Further reading

  • AI Hub — Overview of ONULSURI AI guides and where each section fits.
  • AI Compare — Side-by-side comparisons of assistants and tools.
  • AI Tool Directory — Category directory and tool overviews.
  • AI Pricing — Plan structure and upgrade guidance without fabricated prices.
  • AI Benchmarks — Transparent evaluation frameworks and scenario suites.
  • Prompt Library — Reusable prompts for coding, writing, and everyday work.