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pgvector vs Qdrant

A side-by-side comparison of pgvector and Qdrant, 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

Qdrant

Vector DB

Open-source, Rust-based vector DB. Fast, predictable, self-hostable.

View Qdrant

At a glance

Feature comparison of pgvector and Qdrant
AttributepgvectorQdrant
CategoryVector DBVector DB
Pricing (differs)FREEFREEMIUM
License (differs)Open sourceOpen core
Deployment (differs)Self-hostHybrid
PlatformsAPIAPI
Model supportModel-agnosticModel-agnostic
Vendor (differs)pgvector communityQdrant

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

Qdrant

Rust single-binary you can self-host, with payload filtering strong enough that teams pick it for metadata-heavy search.

  • Open source, written in Rust
  • Self-host or managed cloud
  • Strong payload/metadata filtering
  • Predictable latency at scale
  • More ops than fully-managed rivals
  • Smaller ecosystem than Pinecone
  • Advanced features lean on managed cloud