Which is more popular, pgvector or Qdrant?

Qdrant has more GitHub stars (34,904 vs 23,223).

Which is more actively developed, pgvector or Qdrant?

Qdrant had more commits in the last 90 days (754 vs 128).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Qdrant is growing faster: +796 GitHub stars in the last 30 days vs +437 for pgvector.
Pick pgvector for: open-source vector similarity search for Postgres. Pick Qdrant for: vector similarity search engine and database written in Rust, with payload filtering and managed cloud service.

From GitHub data refreshed daily.

Open-source vector similarity search for Postgres

Qdrantopen-source

Vector similarity search engine and database written in Rust, with payload filtering and managed cloud service

+Native PostgreSQL integration preserves ACID compliance, transactions, and allows complex JOINs between vector and relational data
+Supports multiple vector types (single/half-precision, binary, sparse) and distance metrics (L2, cosine, inner product, Hamming, Jaccard)
+Wide ecosystem compatibility with any language that has a Postgres client and available through multiple installation methods

+High-performance Rust implementation delivers fast vector operations and reliable performance under heavy loads with proven benchmarks
+Advanced filtering capabilities allow complex queries combining vector similarity with metadata filtering for sophisticated search scenarios
+Production-ready with both self-hosted and managed cloud options, including comprehensive APIs and client libraries for easy integration

-Requires PostgreSQL expertise and may have steeper learning curve compared to dedicated vector databases
-Installation complexity varies by platform, especially on Windows systems
-Performance may not match specialized vector databases for very large-scale vector workloads

-Specialized focus on vector operations means additional tools needed for traditional database operations and non-vector data storage
-Requires understanding of vector embeddings and similarity search concepts, creating a learning curve for teams new to vector databases

•RAG (Retrieval Augmented Generation) applications where embeddings need to be stored alongside document metadata and user data
•E-commerce recommendation systems that combine vector similarity with product catalog data and user preferences
•Semantic search applications where vector queries need to be combined with traditional filters and business logic

•Semantic search applications that need to find similar documents, images, or content based on meaning rather than exact keywords
•Recommendation systems that match user preferences with product catalogs or content libraries using neural network embeddings
•Neural network-based matching for applications like duplicate detection, content classification, or similarity-based grouping

Which is more popular, pgvector or Qdrant?: Qdrant has more GitHub stars (34,904 vs 23,223).
Which is more actively developed, pgvector or Qdrant?: Qdrant had more commits in the last 90 days (754 vs 128).
Should I use pgvector or Qdrant?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.