Qdrant is a dedicated vector database and similarity search engine supporting advanced filtering and efficient retrieval, suitable for faceted search and retrieval-augmented generation. It offers self-hosted and cloud deployment options, making it highly relevant for vector search applications.
Trieve provides an all-in-one infrastructure for vector search, recommendations, retrieval-augmented generation (RAG), and analytics, accessible via API for seamless integration.
PostgreSQL supports vector indexing and similarity search via the PGVector extension, allowing relational databases to manage and retrieve vector embeddings efficiently.
RediSearch is a Redis module that provides high-performance vector search and similarity search capabilities on top of Redis, enabling advanced search and retrieval features for AI and data applications.
Arroy is an open-source library for efficient similarity search and management of vector embeddings, useful in vector database systems.
HelixDB is a powerful, open-source graph-vector database built in Rust, designed for intelligent data storage for Retrieval-Augmented Generation (RAG) and AI applications. It combines graph database features with vector search, making it directly relevant to AI and machine learning workflows that require vector data management.
Category: Vector Database Engines
Website: qdrant.tech
Qdrant is an open-source vector database and similarity search engine, purpose-built for handling high-dimensional vectors and powering large-scale AI applications. It supports advanced filtering, efficient retrieval, faceted search, and retrieval-augmented generation (RAG). Qdrant is available for both self-hosted and managed cloud deployments.
open-source, vector-search, similarity-search, rag
Note: For full pricing details, visit the official Qdrant website.