pgvector vs. Pinecone
pgvector vs. Pinecone · choosing a vector store for RAG
One lives inside the Postgres you already run; the other is a managed vector database you pay for separately. The choice is mostly about scale and operational appetite.
option Apgvectoroption BPineconeserviceAI solutions
→ Verdict
For most RAG builds under a few million vectors, pgvector wins — one database, one backup, no extra bill, and SQL joins against your real data. Reach for Pinecone when vector count and query volume outgrow what a single Postgres can serve comfortably.
Pick a topic
When to pick which
A · Pick this when…
pgvector
- 01You already run Postgres and want one database to back up and operate
- 02Your corpus is under roughly 1–5 million vectors
- 03You need to filter or join vectors against relational data in the same query
- 04You want zero extra vendor and zero extra monthly bill
B · Pick that when…
Pinecone
- 01Vector count is in the tens of millions or more
- 02Query latency at high QPS matters more than operational simplicity
- 03You want managed scaling, replicas and uptime off your plate
- 04Your team would rather not tune a Postgres index
Factors to weigh
Factor-by-factor
| Factors to weigh | pgvector | Pinecone |
|---|---|---|
| Operations | Lives in your existing Postgres · one backup, one connection | Separate managed service · its own dashboard, keys, billing |
| Cost | Free · just Postgres storage and CPU | Usage-priced · scales with vectors stored and queried |
| Scale ceiling | Comfortable to a few million vectors with HNSW indexing | Built for tens to hundreds of millions |
| Metadata filtering | Full SQL · join and WHERE against any column you own | Native metadata filters, but no joins to relational data |
| Latency at high QPS | Good · depends on the index and the box | Consistently low, purpose-built for it |
| Data residency | Wherever your Postgres lives · easy EU-only | Region-pinned, but a third-party processor |
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