Which is more popular, pgvector or Weaviate?

pgvector has more GitHub stars (23,226 vs 16,861).

Which is more actively developed, pgvector or Weaviate?

Weaviate had more commits in the last 90 days (3,786 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

pgvector is growing faster: +435 GitHub stars in the last 30 days vs +152 for Weaviate.
Pick pgvector for: open-source vector similarity search for Postgres. Pick Weaviate for: open-source cloud-native vector database for semantic search, filtering, RAG, and reranking.

From GitHub data refreshed daily.

Open-source vector similarity search for Postgres

Weaviateopen-source

Open-source cloud-native vector database for semantic search, filtering, RAG, and reranking

+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

+Unified query interface that combines vector similarity search with structured filtering and RAG capabilities
+Multiple deployment options including Docker, Kubernetes, cloud services, and major cloud marketplaces (AWS, GCP)
+Enterprise-ready with built-in multi-tenancy, replication, RBAC authorization, and integration with popular ML model providers

-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

-Requires understanding of vector embeddings and semantic search concepts for optimal implementation
-May involve complexity overhead for simple use cases that don't require vector search capabilities

•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

•Building RAG (Retrieval-Augmented Generation) systems for AI chatbots and knowledge bases
•Implementing semantic and image search functionality for content discovery applications
•Creating recommendation engines that understand content similarity beyond keyword matching

Which is more popular, pgvector or Weaviate?: pgvector has more GitHub stars (23,226 vs 16,861).
Which is more actively developed, pgvector or Weaviate?: Weaviate had more commits in the last 90 days (3,786 vs 128).
Should I use pgvector or Weaviate?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.