# 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.

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.

## pgvector fits when

- You already run Postgres and want one database to back up and operate
- Your corpus is under roughly 1-5 million vectors
- You need to filter or join vectors against relational data in the same query
- You want zero extra vendor and zero extra monthly bill

## Pinecone fits when

- Vector count is in the tens of millions or more
- Query latency at high QPS matters more than operational simplicity
- You want managed scaling, replicas and uptime off your plate
- Your team would rather not tune a Postgres index

| Factor | 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 |

## What to take away

- pgvector · You already run Postgres and want one database to back up and operate
- pgvector · Your corpus is under roughly 1-5 million vectors
- pgvector · You need to filter or join vectors against relational data in the same query
- Pinecone · Vector count is in the tens of millions or more
- Pinecone · Query latency at high QPS matters more than operational simplicity
- Pinecone · You want managed scaling, replicas and uptime off your plate

## Tags

pgvector, Pinecone

## We build this for clients

https://dfieldsolutions.com/en/services/ai-automation

## More from the lab

- https://dfieldsolutions.com/en/lab/postgres-pgvector-at-10m-rows.md — pgvector at 10M+ rows: index, queries, real numbers
- https://dfieldsolutions.com/en/lab/vector-db-pick-2026.md — Picking a vector DB in 2026: pgvector, Pinecone, Weaviate
- https://dfieldsolutions.com/en/lab/n8n-self-hosted-vs-zapier.md — Self-hosted n8n or Zapier — where the bill actually differs
- https://dfieldsolutions.com/en/lab/mcp-servers-production.md — MCP servers in production, not in the demo

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Source: https://dfieldsolutions.com/en/lab/pgvector-vs-pinecone-rag-2026
DField Solutions — Torony köz 5-1.a, 2120 Dunakeszi, Hungary — +36 20 722 0002 — dezso@dfieldsolutions.com
Booking: see https://dfieldsolutions.com/en/contact