vs

pgvector vs Qdrant

pgvector: The standard Postgres extension for vector similarity search with HNSW and IVFFlat indexes. Qdrant: Rust vector search engine with rich payload filtering, quantization and a managed cloud.

key differences

  • Qdrant is built for larger datasets (≤ 1 PB) than pgvector (≤ 10 TB).
  • Horizontal scaling is addon in pgvector and native in Qdrant.
  • Transactions: pgvector offers full ACID; Qdrant offers none.
  • Only pgvector has transactions, joins, change data capture, triggers, stored procedures.
  • Only Qdrant has geospatial.

choose pgvector for

  • +Adding semantic search to an existing Postgres app
  • +RAG with permission filtering via SQL joins
  • +Recommendations next to product data
  • +Deduplication and similarity lookups

best at · Vector search, RAG, OLTP

choose qdrant for

  • +RAG retrieval with metadata filtering
  • +Recommendation and similarity search
  • +Image and multimodal search
  • +Multi-tenant semantic search

best at · Vector search, RAG

overview
Primary model
Vector
Vector
Also supports
Relational
Document
Type
Extension
Database engine
Status
Active
Active
Developer
Andrew Kane and pgvector contributors
Qdrant Solutions GmbH
First release
2021
2021
Latest version
0.8.7 (Oct 1, 2026)
1.20.0 (Oct 22, 2026)
License
Open source (permissive)PostgreSQL License
Open source (permissive)Apache License 2.0
Free tier
✓
✓
popularity
WhichDB Score
40.0
35.9
Rank
#44
#55
GitHub stars
23.5K
35.3K
Stack Overflow questions
91
60
scale & guarantees
Comfortable data size
Medium (≤ 10 TB)
Large (≤ 1 PB)
Horizontal scaling
Addon
Native
Multi-region
Read replicas
None
ACID
Full
None
Consistency
Strong
Tunable, Eventual
Schema
Strict
Schemaless
Architecture
Single node, Primary replica, Client server
Distributed, Client server
deployment
Self-hosted
✓
✓
Vendor-managed cloud
—
✓
Serverless
—
—
Embedded
—
—
Edge
—
—
Clouds
AWS, Google Cloud, Azure, DigitalOcean, Heroku, Render
AWS, Google Cloud, Azure
capabilities
Transactions
✓
—
Joins
✓
—
Secondary indexes
✓
✓
Full-text search
✓
✓
Vector search
✓
✓
Geospatial
—
✓
JSON
✓
✓
Change data capture
✓
—
Triggers
✓
—
Stored procedures
✓
—
Materialized views
✓
—
TTL / expiry
—
—
Pub/Sub
✓
—
Columnar storage
—
—
In-memory
—
—
Durable persistence
✓
✓
Compression
✓
✓
Encryption at rest
—
—
Role-based access
✓
✓
Time travel
—
—
Branching
—
—
Online schema changes
✓
—
Streaming ingest
—
—
Offline sync
—
—
Runs in WebAssembly
—
—
interfaces
Query languages
SQL
Qdrant Query API (JSON filters)
Client languages
Python, JavaScript, Go, Java, Ruby, Rust, C#, PHP
Python, JavaScript, Rust, Go, Java, .NET
Best for
Vector search, RAG, OLTP
Vector search, RAG

pgvector vs Qdrant: FAQ

What is the difference between pgvector and Qdrant?

pgvector: The standard Postgres extension for vector similarity search with HNSW and IVFFlat indexes. Qdrant: Rust vector search engine with rich payload filtering, quantization and a managed cloud. Qdrant is built for larger datasets (≤ 1 PB) than pgvector (≤ 10 TB). Horizontal scaling is addon in pgvector and native in Qdrant. Transactions: pgvector offers full ACID; Qdrant offers none.

When should I choose pgvector over Qdrant?

Choose pgvector for adding semantic search to an existing Postgres app, RAG with permission filtering via SQL joins and recommendations next to product data. Choose Qdrant for RAG retrieval with metadata filtering; recommendation and similarity search; and image and multimodal search.

Is pgvector or Qdrant more popular?

pgvector ranks #44 on the WhichDB Score (40.0/100) and Qdrant ranks #55 (35.9/100). Popularity is not fit: check the requirements that matter for your project.

Are pgvector and Qdrant open source?

Yes. pgvector is open source. License: PostgreSQL License. Yes. Qdrant is open source. License: Apache License 2.0.