AI Glossary · Last reviewed August 2026

Vector Database

Hand-written by a real person. Reviewed against current practice in August 2026.

Definition

A database optimized to store and search embeddings.

Why it matters

Vector databases are the infrastructure behind semantic search and RAG. They store data as mathematical vectors (embeddings) and can find similar items in milliseconds across millions of records. This is what powers "find similar" features and intelligent document retrieval.

As more AI tools rely on custom knowledge bases, vector databases have become essential infrastructure. Understanding them helps you evaluate AI search and knowledge management tools.

How it works

4 steps

STEP 01

Data is embedded

Documents, images, or other data are converted into high-dimensional vectors using an embedding model.

STEP 02

Vectors are indexed

The database builds a special index (like HNSW or IVF) that enables fast approximate nearest-neighbor search across millions of vectors.

STEP 03

Query is embedded and searched

When you search, your query is embedded into the same vector space and the index finds the most similar stored vectors in milliseconds.

STEP 04

Results are returned

The matching vectors and their associated metadata (original text, URLs, etc.) are returned ranked by similarity.

Frequently asked questions

What is a vector database?+

A vector database stores and searches data using numerical representations called vectors or embeddings.

Why are vector databases used in AI?+

They help AI applications quickly find similar information for search, recommendations, and retrieval tasks.

How do vector databases work?+

They compare vector embeddings to identify similarities between data points and return relevant results.

New to Vector Database?

See the tools that use it.

The fastest way to understand Vector Database is to see it inside real products. Browse hand-reviewed tools that put it to work, each one checked by a person before it was listed.

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