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'}), ]) results = client.search('docs', query_vector=[0.1]*