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What is a vector database and when do you actually need one?

Q: What is a vector database and when do you actually need one?

**TL;DR.** A vector DB stores embeddings with **ANN indexes plus filtering, CRUD and scaling**. But under ~100k vectors a flat search is fine, and **pgvector** keeps vectors next to your relational data. Reach for a dedicated engine at **millions of vectors, high QPS, or heavy filtered search**. in-memory <100k vectors pgvector / SQLite <text class='d-sub' x='240' y='80' text

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Purpose-built ANN stores vs pgvector vs a numpy array — sizing the decision honestly.

vector-databasepgvectorinfrastructurerag

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pgvector schema keeping vectors beside the data

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE chunks (
  id         bigserial PRIMARY KEY,
  doc_id     bigint REFERENCES documents(id) ON DELETE CASCADE,
  tenant_id  bigint NOT NULL,
  content    text NOT NULL,
  embedding  vector(1536) NOT NULL
);
CREATE INDEX ON chunks USING hnsw (embedding vector_cosine_ops);
-- one transaction updates document + chunks: no cross-store drift

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