What is Qdrant?
Qdrant is an open-source vector database designed for storing, searching, and managing high-dimensional embeddings with a focus on performance, rich filtering capabilities, and ease of use. Written in Rust for high performance and memory safety, Qdrant provides efficient similarity search over millions or billions of vectors while supporting complex metadata filtering, making it well-suited for production AI applications requiring both semantic search and structured constraints.
The database offers several distinctive features: advanced payload filtering that combines vector similarity with structured queries, built-in support for hybrid search combining dense and sparse vectors, collections with multiple vectors per point (useful for multi-modal or multi-representation scenarios), and clustering for distributed deployment. Qdrant provides both self-hosted and cloud-managed options, with comprehensive APIs, SDKs for multiple languages, and integration with popular AI frameworks.
Qdrant has gained significant adoption in the AI community due to its performance characteristics, feature richness, and developer-friendly design. It excels at scenarios requiring filtered vector search, such as multi-tenant applications, document search with metadata constraints, or recommendations with business logic. The open-source nature allows self-hosting with full control, while Qdrant Cloud offers managed infrastructure for production deployments. Its architecture emphasizes both accuracy and speed, with support for quantization and other optimizations to handle large-scale vector collections efficiently.