Pinecone vs Weaviate vs pgvector: Vector DB Comparison for RAG (2026)

Vector database choice locks you in for years. Migrating 10M embeddings is painful. Pick once based on real workload, not vibes.

Pricing at 1M vectors (1536-dim, ~6 GB)

| Provider | Monthly | |---|---| | Pinecone (Serverless) | ~$70 | | Weaviate Cloud | ~$110 | | pgvector on Supabase | $0–25 (fits free tier at 1M, $25/mo Pro adds room) | | Qdrant Cloud | ~$95 |

Query latency (P95, k=10, 1M vectors)

• Pinecone: 35 ms
• Weaviate: 42 ms
• pgvector (HNSW index): 28 ms
• Qdrant: 31 ms

pgvector is fastest because Supabase pgvector lives next to your Postgres — no extra network hop. Pinecone serverless adds a transcontinental round-trip.

Recall (similarity quality)

All three deliver 95%+ recall@10 at default index settings. Tweakable on all platforms. No meaningful quality difference at this scale.

Features that matter in production

• Pinecone: best metadata filtering UX, mature SDK, namespace-per-tenant isolation
• Weaviate: built-in hybrid search (BM25 + vector), generative module (embedded RAG)
• pgvector: ACID transactions, joins with your real data, single backup story
• Qdrant: best on-prem deployment story, payload filtering rivals Pinecone

Honest recommendation

For 80% of new RAG projects in 2026, pgvector on Supabase is the right answer. You already have Postgres for your auth/orders/whatever. Adding embeddings is a single migration. Vendor lock-in: zero.

Pick Pinecone when you cross 50M vectors or need multi-tenant namespaces at scale. Pick Weaviate if hybrid search is a hard requirement on day one.

Migration paths

pgvector → Pinecone: easy (script + bulk upsert). Pinecone → pgvector: same. Weaviate → anything: hardest because of schema differences.

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