Qdrant Has a Free API: A Vector Database Built in Rust for AI Applications

Qdrant is a vector similarity search engine built in Rust. It stores and searches high-dimensional vectors for RAG, recommendation systems, and semantic search. Why Qdrant Matters AI applications need to find similar items — similar documents, images, products. Traditional databases cannot search by similarity. Qdrant is built for exactly this. What you get for free: Vector similarity search (cosine, dot product, Euclidean) Payload filtering (combine vector search with metadata filters) Built in Rust for maximum performance Horizontal scaling with sharding REST and gRPC APIs Python, JS, Rust, Go clients Free cloud tier (1GB, 1M vectors) Docker deployment Quick Start # Docker docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant # Or install locally cargo install qdrant Python Client from qdrant_client import QdrantClient from qdrant_client.models import Distance , VectorParams , PointStruct client = QdrantClient ( " localhost " , port = 6333 ) # Create collection client . create_collectio