Vector Database Showdown 2026: Pinecone vs Weaviate vs Qdrant vs pgvector
Your RAG system is only as good as its vector database. Here's a real-world comparison.
1. Feature Comparison
| Feature | Pinecone | Weaviate | Qdrant | pgvector |
|---|---|---|---|---|
| Hosting | Managed only | Self-host or cloud | Self-host or cloud | Self-host (PostgreSQL) |
| Hybrid Search | ✅ | ✅ | ✅ | ❌ (BM25 separate) |
| Filtering | Metadata filters | GraphQL + filters | Payload filters | SQL WHERE clauses |
| Max Dimensions | 20,000 | Unlimited | 65,535 | 2,000 |
| Pricing Model | Per-pod | Per-node | Per-node | Free (PostgreSQL extension) |
| Latency (p99, 1M vectors) | ~15ms | ~20ms | ~12ms | ~40ms |
2. Decision Guide
Already using PostgreSQL? → pgvector (zero operational overhead)
Need managed + zero ops? → Pinecone (but vendor lock-in)
Need hybrid search + open source? → Weaviate or Qdrant
Maximum performance? → Qdrant (fastest benchmarks)
Enterprise compliance needs? → Self-hosted Qdrant or Weaviate
3. pgvector Quick Start (Best for Most Teams)
CREATE EXTENSION vector;
CREATE TABLE documents (
id SERIAL PRIMARY KEY,
content TEXT,
embedding vector(1536) -- OpenAI ada-002 dimensions
);
CREATE INDEX ON documents USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);
-- Similarity search
SELECT content, 1 - (embedding <=> '[0.1, 0.2, ...]'::vector) AS similarity
FROM documents
ORDER BY embedding <=> '[0.1, 0.2, ...]'::vector
LIMIT 5;
For most teams, pgvector is the right starting point — it eliminates a separate database dependency while providing sufficient performance for applications under 10M vectors.
