Amazon OpenSearch's k-NN plugin enables scalable, efficient vector search using ANN algorithms (IVF, HNSW) directly within a managed OpenSearch cluster. It is directly relevant for building, querying, and scaling vector databases on AWS.

Neural and hybrid search capability in OpenSearch that combines lexical queries with vector-based neural search using a pipeline of normalization and score combination techniques. It enables semantic (vector) search and hybrid search over indices such as `neural_search_pqa`, suitable for AI and vector database-style retrieval use cases.

MariaDB Vector is an extension or feature of MariaDB, providing capabilities for handling and querying vector data within the MariaDB ecosystem.

An enhancement to the Neo4j graph database providing vector search capabilities through dedicated indexes.

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.

Vector search extension for Meilisearch engine, supporting hybrid lexical+vector search with BM25 fusion, k-NN similarity. Ideal for enterprise semantic search, RAG, and recommendations. Integrated vs standalone like Weaviate: developer-friendly with typo-tolerant full-text but lighter scale for massive vectors.