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
At a glance
| Attribute | pgvector | Pinecone |
|---|---|---|
| Category | Vector DB | Vector DB |
| Pricing (differs) | FREE | FREEMIUM |
| License (differs) | Open source | Proprietary |
| Deployment (differs) | Self-host | Cloud |
| Platforms | API | API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | pgvector community | Pinecone |
| Capabilities (differs) |
|
|
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
When to pick which
Across the signals we compare, pgvector and Pinecone differ on pricing, license, and deployment:
- Pricing
- FREE · FREEMIUM
- License
- Open source · Proprietary
- Deployment
- Self-host · Cloud
Their capability lists differ in recorded depth — compare the full lists above before deciding.