Pinecone vs pgvector
Both store and search embeddings, but Pinecone is a managed vector database and pgvector is an extension that adds vector search to Postgres you already run. The trade is managed scale versus keeping everything in one database.
At a glance
Pinecone
- Model
- Managed vector database
- Ops overhead
- Low — fully managed
- Scale
- Built for very large indexes
pgvector
- Model
- Postgres extension
- Ops overhead
- Reuses your existing Postgres
- Scale
- Great to mid-scale; tune for large
Full comparison
| Pinecone | pgvector | |
|---|---|---|
| Model | Managed vector database | Postgres extension |
| Ops overhead | Low — fully managed | Reuses your existing Postgres |
| Scale | Built for very large indexes | Great to mid-scale; tune for large |
| Data locality | Separate service | Lives with your relational data |
| Cost shape | Per-pod / usage pricing | Your existing DB cost |
Which should you choose?
Start with pgvector if you already run Postgres and your corpus is small-to-mid scale — it keeps data in one place and is often plenty. Move to Pinecone (or similar) when index size and query volume outgrow what your database handles comfortably.
Frequently asked questions
What's the difference between Pinecone and pgvector?
Both store and search embeddings, but Pinecone is a managed vector database and pgvector is an extension that adds vector search to Postgres you already run. The trade is managed scale versus keeping everything in one database.
Which should I choose, Pinecone or pgvector?
Start with pgvector if you already run Postgres and your corpus is small-to-mid scale — it keeps data in one place and is often plenty. Move to Pinecone (or similar) when index size and query volume outgrow what your database handles comfortably.