“vector search and databases”

Elasticsearch as a vector database, combining vector search with hybrid lexical search, filters and reranking.

Vector and hybrid search built into MongoDB, running on the same operational data as the database.

Build vector databases on Redis, with vector search combined with exact-match and full-text search.

Open-source vector similarity search engine and database written in Rust, with a managed cloud.

Apache Cassandra-based NoSQL database with vector search for generative AI workloads.

Open-source, cloud-native vector database for semantic search, RAG and hybrid queries.

Search database for vector and full-text queries, built on object storage and available as a Python client.

Open-source vector database inside the OpenSearch search suite, for semantic search and RAG.

Serverless vector database with dense, sparse and hybrid indexes for semantic and keyword search.

Vector Search engine built on Google Research's ScaNN algorithm for semantic search and recommendations.

Open source vector database for AI apps, built on Postgres with pgvector and available self-hosted or managed.

Open-source, high-performance vector database for large-scale search, with a managed option on Zilliz Cloud.

Database for AI with a deep-learning storage format for embeddings, media and datasets, plus vector search.

Globally distributed vector database for building AI applications on Cloudflare Workers.

Managed search database for AI agents and apps, covering semantic, sparse, full-text and hybrid queries.

Fully managed Milvus vector database service with serverless and dedicated deployment options.