{"id":"qdrant-api","name":"Qdrant","af_score":84.7,"security_score":75.0,"reliability_score":null,"what_it_does":"Qdrant is a high-performance open-source vector database and search engine written in Rust, designed specifically for AI and machine learning workloads. It stores high-dimensional embedding vectors alongside arbitrary JSON payloads and enables fast approximate nearest neighbor (ANN) search with rich metadata filtering. Qdrant supports dense vectors, sparse vectors (BM42), and multi-vector configurations — enabling hybrid search that combines semantic similarity with keyword relevance in a single query. Available as self-hosted (Docker/Kubernetes) or fully managed via Qdrant Cloud, with an official MCP server for direct AI agent memory integration.","best_when":"You need a high-performance, resource-efficient vector database with first-class MCP server support, the ability to self-host for free, or a competitively priced managed cloud option — especially for RAG, semantic search, or agent memory.","avoid_when":"Your workload is purely keyword-based full-text search, or you need relational database guarantees like foreign keys and complex joins.","last_evaluated":"2026-03-01T09:50:06.108003+00:00"}