Pinecone vs pgvector
for RAG builders.
pgvector by default; Pinecone only when scale demands it.
What this actually means for RAG builders.
For RAG builders, pgvector is the right default for almost every project. It runs in your existing Postgres, supports hybrid search (full-text + vector), and avoids the operational cost of a separate database. Pinecone is the right pick only at extreme scale (>10M vectors with strict latency requirements) or when your team genuinely doesn't want to manage Postgres. Most production RAG systems we see ship on pgvector and never need to migrate.
RAG builders-specific gotchas
- pgvector index choice (HNSW vs IVFFlat) matters at scale
- Pinecone's metadata filters are simpler but less flexible than SQL joins
- pgvector's embedding dimension limits matter for newer models
- Pinecone's API design adds another vendor to your stack
- Both support hybrid search; pgvector's is more flexible via SQL
A RAG team builds on pgvector, scales to 8M vectors. Total infrastructure cost: ~$300/month for Postgres. The same workload on Pinecone would cost $1,200+/month and add a second system to monitor.
Pick by use case.
Pinecone
You need extreme scale (>50M vectors) and managed simplicity.
pgvector
Almost everyone — you already have Postgres.
Direct comparison.
| Feature | Pinecone | pgvector |
|---|---|---|
| Operational simplicity | Managed | Just Postgres |
| Cost | Higher | Free if you have PG |
| Hybrid search | Yes | Yes (full-text + vector) |
| Filters & joins | Limited | Native SQL |
| Scale ceiling | Very high | ~10-50M comfortable |
| Vendor lock-in | Yes | No |
We've shipped both.
If you're evaluating these as a RAG builders, brief us — we can save you weeks.
Talk to usCommon RAG builders questions.
When should we actually use Pinecone?
Multi-region with strict latency, very high vector count (>50M), or specific Pinecone features like namespaces.
What about Qdrant or Weaviate?
Real alternatives. Qdrant for self-hosted preference; Weaviate for richer schema features.