Chroma is an open-source embedding database optimized for LLM apps, with in-memory/persistent storage and simple Python API. Features: HNSW indexing, automatic batching, metadata filtering, integrations with LangChain/LlamaIndex. Ideal for local dev, prototyping RAG; vs pgvector, easier for Python users; vs full DBs like Milvus, lighter but less scalable.

Solr is a mature open-source search engine that has incorporated vector search capabilities, making it relevant for enterprises looking to implement vector-based search alongside traditional keyword search.

ClickHouse is a columnar OLAP database with vector indexes (ANN via AMM, brute-force), supporting SQL queries over vectors + structured data at petabyte scale. Excels in aggregations with vectors. For analytics workloads with embeddings; faster ingestion than Postgres pgvector for big data.

Open-source search engine with support for vector and hybrid search for fast semantic retrieval.

Lightweight Node.js library for low-latency on-device vector similarity search using HNSW and BM25 hybrid, with CRUD, metadata filtering, and persistence for edge RAG pipelines. Enables real-time semantic search without servers; more lightweight than cloud Qdrant.

A distributed vector database designed for scalable and efficient vector similarity search. It is purpose-built for handling large-scale vector data and search workloads.