Datastax offers a vector search solution integrated with its database platform, enabling approximate similarity search and hybrid queries for enterprise use cases.

Oracle's core database now includes vector search capabilities, enabling enterprises to perform scalable vector queries natively as part of their data management workflows. Oracle includes vector search capabilities in its database platform, supporting approximate KNN and hybrid search for enterprise-scale use cases.

Orama is a lightweight search engine that supports vector and hybrid search functionalities, suitable for browser, server, or edge environments.

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.

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.

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.