High-Performance Vector Search at Scale
Powering the next generation of AI applications with advanced, open-source vector similarity search technology.
AI Meets Advanced Vector Search
The leading open source vector database and similarity search engine designed to handle high-dimensional vectors for performance and massive-scale AI applications.
“Qdrant powers our demanding recommendation and RAG applications. We chose it for its ease of deployment and high performance at scale, and have been consistently impressed with its results.”
“VectorStores are definitely here to stay, the objects in the world around us from image, sound, video and text become easily universal and searchable thanks to the embedding models. I personally recommend Qdrant. We have been using it for a while and couldn’t be happier.“
“We looked at all the big options out there right now for vector databases, with our focus on ease of use, performance, pricing, and communication. Qdrant came out on top in each category... ultimately, it wasn’t much of a contest.”
“With Qdrant, we found the missing piece to develop our own provider independent multimodal generative AI platform on enterprise scale.”
“We LOVE Qdrant! The exceptional engineering, strong business value, and outstanding team behind the product drove our choice. Thank you for your great contribution to the technology community!”
See what our community is saying on our Vector Space Wall
1 docker pull qdrant/qdrant 2 docker run -p 6333:6333 qdrant/qdrant
Advanced Search
Elevate your apps with advanced search capabilities. Qdrant excels in processing high-dimensional data, enabling nuanced similarity searches, and understanding semantics in depth. Qdrant also handles multimodal data with fast and accurate search algorithms.
Recommendation Systems
Create highly responsive and personalized recommendation systems with tailored suggestions. Qdrant’s Recommendation API offers great flexibility, featuring options such as best score recommendation strategy. This enables new scenarios of using multiple vectors in a single query to impact result relevancy.
Retrieval Augmented Generation (RAG)
Enhance the quality of AI-generated content. Leverage Qdrant’s efficient nearest neighbor search and payload filtering features for retrieval-augmented generation. You can then quickly access relevant vectors and integrate a vast array of data points.