pgvectorvsQdrant
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
pgvector stores embeddings as a native column type and queries them with standard SQL, avoiding the operational complexity of a separate vector database for applications already running on Postgres.
Qdrant's HNSW indexes integrate payload filtering natively, executing filtered nearest-neighbor search without a post-filter scan step, maintaining sub-50ms latency on complex metadata filters.
pgvector's HNSW index achieves sub-50ms similarity search for collections under 10M vectors, covering most product recommendation and semantic search use cases without a specialized vector database.
Qdrant supports sparse vectors natively alongside dense vectors, enabling BM25 and embedding search in the same collection for hybrid retrieval without maintaining two separate indexes.
pgvector writes and deletes embeddings within standard Postgres transactions, ensuring vector index and application data never diverge in multi-step operations that require rollback.
Qdrant's on-disk HNSW stores vectors on SSD while keeping only graph navigation data in RAM, serving collections larger than server memory at acceptable latency for cost-sensitive deployments.
Pricing Intelligence
pgvector Plans
Free tier available
- • Free PostgreSQL extension
- • MIT license
- • Self-hosted
Qdrant Plans
Free tier available
- • Full features
- • Apache 2.0
- • Docker deployment
- • From $0.014/hr
- • Managed clusters
- • Free tier available
- • Private cloud
- • SSO
- • Dedicated support
Feature Matrix
6 differences found across 14 standardized features
Pros & Cons Face-Off
Evaluative strengths and weaknesses — not feature lists
- +No new infrastructure — runs inside existing PostgreSQL
- +SQL interface familiar to every developer
- +ACID transactions across vectors and relational data
- +Works on Supabase, RDS, Neon — all managed PG providers
- −Performance trails purpose-built vector DBs at 10M+ vectors
- −No distributed vector search without manual sharding
- +Top benchmark performance via Rust and quantization
- +Named vectors enable multimodal and complex search patterns
- +Binary quantization reduces memory 32x
- +Excellent documentation and developer experience
- −Smaller managed cloud ecosystem than Pinecone
- −Newer company — fewer enterprise customer references