Qdrant Has a Free Vector Database Optimized for AI and Semantic Search

Qdrant is a vector similarity search engine built in Rust. It stores and searches high-dimensional vectors for AI applications like RAG, recommendations, and semantic search. What You Get for Free HNSW indexing — fast approximate nearest neighbor search Filtering — combine vector search with payload filters Quantization — reduce memory usage by 4x Distributed mode — horizontal scaling REST + gRPC APIs — multiple client libraries Snapshots — backup and restore Multi-tenancy — tenant isolation via payload Quick Start docker run -p 6333:6333 qdrant/qdrant Store and Search Vectors (Python) from qdrant_client import QdrantClient from qdrant_client.models import VectorParams , Distance , PointStruct client = QdrantClient ( ' localhost ' , port = 6333 ) client . create_collection ( ' docs ' , vectors_config = VectorParams ( size = 384 , distance = Distance . COSINE )) client . upsert ( ' docs ' , points = [ PointStruct ( id = 1 , vector = [ 0.1 ] * 384 , payload = { ' text ' : ' Hello world '