Which is more popular, pgvector or vLLM?

vLLM has more GitHub stars (93,097 vs 23,226).

Which is more actively developed, pgvector or vLLM?

vLLM had more commits in the last 90 days (4,023 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

vLLM is growing faster: +2,933 GitHub stars in the last 30 days vs +435 for pgvector.
Pick pgvector for: open-source vector similarity search for Postgres. Pick vLLM for: a high-throughput and memory-efficient inference and serving engine for LLMs.

From GitHub data refreshed daily.

Open-source vector similarity search for Postgres

vLLMopen-source

A high-throughput and memory-efficient inference and serving engine for LLMs

+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

+Exceptional serving throughput with PagedAttention memory optimization and continuous batching for production-scale LLM deployment
+Comprehensive hardware support across NVIDIA, AMD, Intel platforms and specialized accelerators with flexible parallelism options
+Seamless Hugging Face integration with OpenAI-compatible API server for easy model deployment and switching

-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 significant GPU memory for optimal performance, limiting accessibility for resource-constrained environments
-Complex setup and configuration for distributed inference across multiple GPUs or nodes
-Primary focus on inference means limited support for training or fine-tuning workflows

•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

•Production API serving for applications requiring high-throughput LLM inference with multiple concurrent users
•Research and experimentation with open-source LLMs requiring efficient model switching and testing
•Enterprise deployment of private LLM services with OpenAI-compatible interfaces for existing applications

Which is more popular, pgvector or vLLM?: vLLM has more GitHub stars (93,097 vs 23,226).
Which is more actively developed, pgvector or vLLM?: vLLM had more commits in the last 90 days (4,023 vs 128).
Should I use pgvector or vLLM?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.