Which is more popular, Milvus or pgvector?

Milvus has more GitHub stars (46,310 vs 23,226).

Which is more actively developed, Milvus or pgvector?

Milvus had more commits in the last 90 days (680 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

Pick Milvus for: milvus is a high-performance, cloud-native vector database built for scalable vector ANN search. Pick pgvector for: open-source vector similarity search for Postgres.

From GitHub data refreshed daily.

Milvusopen-source

Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search

Open-source vector similarity search for Postgres

+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

-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

•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

Which is more popular, Milvus or pgvector?: Milvus has more GitHub stars (46,310 vs 23,226).
Which is more actively developed, Milvus or pgvector?: Milvus had more commits in the last 90 days (680 vs 128).
Should I use Milvus or pgvector?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.