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Learn how Qdrant powers thousands of top AI solutions that require vector search with unparalleled efficiency, performance and massive-scale data processing.
“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.”
Alex Webb
Director of Engineering, CB Insights
Recommendation Engine with Qdrant Vector Database
Dailymotion leverages Qdrant to optimize its video recommendation engine, managing over 420 million videos and processing 13 million recommendations daily. With this, Dailymotion was able to reduced content processing times from hours to minutes and increased user interactions and click-through rates by more than 3x.
Dust uses Qdrant for RAG, achieving millisecond retrieval, reducing costs by 50%, and boosting scalability.
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IrisAgent uses Qdrant for RAG to automate support, and improve resolution times, transforming customer service.
Read Story“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!”
Kyle Tobin
Principal, Cognizant
Vector Space Wall
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“With Qdrant, we found the missing piece to develop our own provider independent multimodal generative AI platform on enterprise scale.”
Thank you, great work, Qdrant is my favorite option for similarity search.
Go ahead and checkout Qdrant. I plan to build a movie retrieval search where you can ask anything regarding a movie based on the vector embeddings generated by a LLM. It can also be used for getting recommendations.
Check out qdrant for improving searches. Bye to non-semantic KM engines.
Quadrant is a great vector database. There is a real sense of thought behind the api!
Great work. I just started testing Qdrant Azure and I was impressed by the efficiency and speed. Being deploy-ready on large cloud providers is a great plus. Way to go!
Using Qdrant as a blazing fact vector store for a stealth project of mine. It offers fantasic functionality for semantic search ✨
We have been using Qdrant in production now for over 6 months to store vectors for cosine similarity search and it is way more stable and faster than our old ElasticSearch vector index.
No merging segments, no red indexes at random times. It just works and was super easy to deploy via docker to our cluster.
It’s faster, cheaper to host, and more stable, and open source to boot!
I'm using Qdrant to search through thousands of documents to find similar text phrases for question answering. Qdrant's awesome filtering allows me to slice along metadata while I'm at it! 🚀 and it's fast ⏩🔥
Amidst the hype around vector databases, Qdrant is by far my favorite one. It's super fast (written in Rust) and open-source! At Kern AI we use Qdrant for fast document retrieval and to do quick similarity search for text data.
Qdrant's the best. By. Far.
We're using Qdrant to help segment and source Europe's next wave of extraordinary companies!
Looking forward to using Qdrant vector similarity search in the clinical trial space! OpenAI Embeddings + Qdrant = Match made in heaven!
awesome stuff 🔥
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