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Qdrant vs pgvector(2026)

Qdrant is better for teams that need best raw performance. pgvector is the stronger choice if no new infrastructure. Qdrant is open-source (from $0) and pgvector is open-source (from $0).

Full feature breakdown, pricing details, and pros & cons below.

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Qdrant logo

Qdrant

open-source

Qdrant is a high-performance vector similarity search engine written in Rust. It offers rich filtering, payload indexing, and a managed cloud — built for production AI applications.

Starting at $0

Visit Qdrant
pgvector logo

pgvector

open-source

pgvector adds vector similarity search directly to PostgreSQL — store embeddings alongside your relational data and run nearest-neighbor searches with standard SQL.

Starting at $0

Visit pgvector

How Do Qdrant and pgvector Compare on Features?

FeatureQdrantpgvector
Pricing modelopen-sourceopen-source
Starting price$0$0
Rust-based (fast)
Rich payload filtering
Named vectors
Managed cloud
gRPC + REST
Multi-tenancy
Quantization support
PostgreSQL extension
L2/inner product/cosine distance
IVFFLAT indexing
HNSW indexing
Standard SQL
Works with Supabase/RDS
Exact + ANN search

Qdrant Pros and Cons vs pgvector

Q

Qdrant

+Best raw performance
+Rich filtering options
+Low memory footprint
+Production-ready
Smaller community than Weaviate
Cloud tier less polished
Less built-in ML model support
p

pgvector

+No new infrastructure
+Works with existing Postgres
+SQL interface
+Supabase has it built-in
Not as fast as dedicated vector DBs at scale
Limited to Postgres ecosystem
Less filtering flexibility

Should You Use Qdrant or pgvector?

Choose Qdrant if…

  • Best raw performance
  • Rich filtering options
  • Low memory footprint

Choose pgvector if…

  • No new infrastructure
  • Works with existing Postgres
  • SQL interface

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