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pgvector vs Pinecone

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

Pinecone

Vector DB

Fully-managed serverless vector database for RAG and semantic search.

View Pinecone

At a glance

Feature comparison of pgvector and Pinecone
AttributepgvectorPinecone
CategoryVector DBVector DB
Pricing (differs)FREEFREEMIUM
License (differs)Open sourceProprietary
Deployment (differs)Self-hostCloud
PlatformsAPIAPI
Model supportModel-agnosticModel-agnostic
Vendor (differs)pgvector communityPinecone

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

Pinecone

The zero-ops default: fully managed serverless with no infra to run, so teams ship RAG fast without a platform engineer.

  • No infra to provision or operate
  • Fast time-to-production
  • Low-latency reads at scale
  • Integrates with every major framework
  • No self-host option
  • Cost climbs at large scale
  • Closed source; potential lock-in