What is Vector Database? Definition & Guide

What is Vector Database?

vector database is a database system optimised for storing and querying embedding vectors at scale. Unlike traditional databases that match exact values, vector databases find semantically similar items using approximate nearest-neighbour algorithms. Pinecone, Weaviate, Qdrant, pgvector, and Chroma are common vector databases. Enterprise RAG pipelines use vector databases to index knowledge bases and retrieve relevant documents for AI agent responses.

Vector databases differ from traditional relational or document databases in their fundamental query model. A relational database answers 'find the row where customer_id = 12345'. A vector database answers 'find the 5 documents most semantically similar to this query', measured by cosine similarity or Euclidean distance between embedding vectors. This nearest-neighbour query is the core operation of semantic search and RAG retrieval. Approximate Nearest Neighbour (ANN) algorithms like HNSW (Hierarchical Navigable Small World) enable this query to run in milliseconds on millions of vectors — far faster than exact nearest-neighbour computation, with negligible accuracy loss.

For enterprise RAG deployments, the choice of vector database affects sovereignty, scalability, and operational complexity. Cloud-managed vector databases (Pinecone, Weaviate Cloud, Qdrant Cloud) are operationally simple but send document embeddings to external services — a compliance concern. Self-hosted vector databases (Qdrant, Weaviate, Chroma, or pgvector in an existing PostgreSQL instance) keep embedding data inside the enterprise perimeter. PGVector is particularly attractive for enterprises already running PostgreSQL because it adds vector search as a PostgreSQL extension, avoiding a new database system entirely. Metadata filtering — combining vector similarity search with SQL-like filters on document metadata — is an important capability that not all vector databases implement equally well.

Also known as: Vector Store, Embedding Database

Key Points

Core idea
Vector databases retrieve the most semantically similar documents to a query, not exact keyword matches. This enables meaning-based retrieval regardless of phrasing differences.
Why it matters
Hierarchical Navigable Small World (HNSW) indexing enables sub-10ms nearest-neighbour queries across millions of vectors — fast enough for real-time retrieval in production RAG systems.
Enterprise use
Cloud-managed vector databases send document embeddings to external services. Self-hosted options (Qdrant, pgvector) keep all embedding data inside the enterprise perimeter — required for regulated industries.

How It Works

How Vector Database works

Define the purpose, inputs, and success criteria that Vector Database must support.
Apply Vector Database in the relevant workflow while recording its inputs, configuration, and outputs.
Evaluate the result against representative data, operational constraints, and human review before expanding production use.

How Fluid AI Uses This

On-premise vector databases for enterprise RAG.

Fluid AI deploys vector databases inside enterprise infrastructure for private semantic search and RAG. No embedding data leaves the customer perimeter.

Explore Architecture

Topics Covered

vector database enterprise RAG
on-premise vector database
pgvector enterprise AI
Qdrant Weaviate enterprise
vector database semantic search
ANN nearest neighbour enterprise
HNSW vector search enterprise
enterprise knowledge base vector database

Vector Database