How to Choose a Vector Database

Vector databases store the embeddings behind RAG. Here's how to choose one—and why retrieval quality matters more than the database.

What a vector database does

A vector database stores embeddings and finds the nearest ones to a query—the retrieval engine behind RAG and semantic search.

What to weigh

Factor	Question
Scale	How many vectors?
Latency	Fast enough at scale?
Metadata filtering	Filter by source, date, etc.?
Cost	Managed vs self-hosted
Integration	Fits your stack?

Match these to your use case, not the most-hyped option.

The database is rarely the bottleneck

Here's the key insight: the vector database rarely determines RAG accuracy. Chunking, embeddings, and reranking matter far more—the database mainly affects scale and latency.

Choose reliably, then invest in retrieval

Pick a database that meets your scale and latency reliably—managed for simplicity, self-hosted for control and privacy—then invest your effort in retrieval quality, which actually decides whether answers are accurate.

Don't over-optimize the database

Teams often obsess over the database while retrieval quality languishes. Get a solid, sufficient database, then spend your time where accuracy is won—see how to build a RAG system.

Why FISTA

FISTA Solutions builds RAG on the right infrastructure for your scale—then focuses on the retrieval quality that drives accuracy—through AI enablement, backed by 150+ projects across 12+ countries.

Building your RAG infrastructure? Talk to FISTA.