pgvector vs Pinecone in 2026: Which One Actually Wins?
After running both at scale, here's the honest breakdown of cost, latency, and when Postgres is enough.
Vector DB choice is the #1 place startups overspend in 2026. The default question is Pinecone vs pgvector. After building on both in production, here's what actually holds up.
The 30-second answer
- < 10M vectors, mostly one workload: pgvector on Postgres. Always.
- 10–100M vectors or multi-tenant scale: pgvector with HNSW + partitioning, or a managed vendor.
- 100M+ vectors, latency-critical: Pinecone, Turbopuffer, or Qdrant Cloud.
2026 cost reality
| Store | 1M vectors (1536d) | Notes |
|---|---|---|
| pgvector on Supabase/RDS | ~$25–70/mo | Shared with your app DB |
| Pinecone serverless | ~$70–200/mo | Free tier decent |
| Turbopuffer | ~$15–40/mo | Cheapest at scale |
| Qdrant Cloud | ~$60–150/mo | Great filtering |
Where pgvector actually shines
- You already run Postgres — no new system to operate.
- You need SQL joins between vectors and metadata.
- Transactional consistency between rows and embeddings.
- HNSW index brings latency to 5–30ms on millions of rows.
Where Pinecone still earns its price
- You have > 50M vectors and don't want to think about ops.
- You need namespace-per-tenant at massive scale.
- You're serving global reads with strict p99 latency SLAs.
The migration pain nobody talks about
Moving from Pinecone to pgvector later is easy. Moving from pgvector to Pinecone at 50M vectors is a 2-week project. Start on Postgres unless you're already sure you'll blow past 20M rows.
Our default in 2026
Postgres + pgvector with HNSW indexes, dimension = 1536, partitioned by tenant. It handles 95% of AI apps we build.
Not sure what fits?
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FAQ
Frequently asked questions
Can pgvector handle 100M vectors?+
Yes with careful indexing (HNSW), partitioning, and sufficient RAM. But operationally most teams move to a managed vendor around 50–100M.
Is Pinecone worth the price?+
Only if you're at scale where ops overhead of self-hosting is real. Under 10M vectors, it's usually overkill.
What dimensions should I use for embeddings?+
1024–1536 covers most use cases. Higher dimensions cost more storage and query time with diminishing returns.
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