Qdrant is an open-source vector database and similarity search engine designed to manage high-dimensional vectors essential for AI-driven applications. Developed in Rust, it ensures exceptional speed and reliability, even when handling billions of vectors. Qdrant’s cloud-native architecture supports seamless vertical and horizontal scaling, facilitating zero-downtime upgrades and efficient resource utilization.
The platform offers a user-friendly API for quick deployment across various environments, including Docker, making it ideal for both local testing and large-scale production. Key features include advanced filtering capabilities, hybrid search combining vector and keyword queries, and built-in vector quantization to reduce memory usage. Qdrant integrates seamlessly with popular machine learning frameworks and embedding models, streamlining the development of applications such as semantic search, recommendation systems, and anomaly detection.
