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pgvector vs sqlite-vec

A side-by-side comparison of pgvector and sqlite-vec, two Vector DB tools, drawn from Ignaite's continuously-verified listings.

Compared from listings verified as of

pgvector

Vector DB

Vector similarity search inside Postgres. The pragmatic default.

View pgvector

sqlite-vec

Vector DB

Vector search as a zero-dependency SQLite extension.

View sqlite-vec

At a glance

Feature comparison of pgvector and sqlite-vec
Attributepgvectorsqlite-vec
CategoryVector DBVector DB
PricingFREEFREE
LicenseOpen sourceOpen source
Deployment (differs)Self-hostLocal
PlatformsAPIAPI
Model supportModel-agnosticModel-agnostic
Vendor (differs)pgvector communityAlex Garcia

The honest brief

pgvector

Keeps vectors in your existing Postgres, so you JOIN against relational data and back it all up together.

  • No new database to operate
  • JOIN embeddings with relational data
  • Free and open source
  • Works on Supabase, Neon, any managed Postgres
  • Scales worse than dedicated vector DBs
  • Tuning HNSW/IVFFlat is on you
  • No built-in hybrid search out of the box

sqlite-vec

Embeds vector search inside the SQLite file itself, so RAG can run fully local — in the browser via WASM or on a Raspberry Pi — with no server.

  • Zero dependencies, pure C
  • Runs anywhere SQLite runs
  • Bindings for Python, JS, Ruby, Go, Rust
  • Local-first, no server needed
  • MIT / Apache 2.0 licensed
  • Exhaustive (brute-force) search, not ANN
  • Not built for very large datasets
  • Single-node, embedded only