Customers

Name: Qdrant
Brand: Qdrant Vector Database

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.

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VISUA improves quality control process for computer vision with anomaly detection by 10x.

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A man in a jeans shirt is holding a smartphone, only his hands are visible. In the foreground, there is an image of a robot surrounded by chat and sound waves.

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.

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“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

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Jonathan Eisenzopf

Chief Strategy and Research Officer at Talkmap

“With Qdrant, we found the missing piece to develop our own provider independent multimodal generative AI platform on enterprise scale.”

Angel Luis Almaraz Sánchez

Full Stack | DevOps

Thank you, great work, Qdrant is my favorite option for similarity search.

Shubham Krishna

ML Engineer @ ML6

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.

Kwok Hing LEON

Data Science

Check out qdrant for improving searches. Bye to non-semantic KM engines.

Ankur S

Building

Quadrant is a great vector database. There is a real sense of thought behind the api!

Yasin Salimibeni View Yasin Salimibeni’s profile

AI Evangelist | Generative AI Product Designer | Entrepreneur | Mentor

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!

Marcel Coetzee

Data and AI Plumber

Using Qdrant as a blazing fact vector store for a stealth project of mine. It offers fantasic functionality for semantic search ✨

Andrew Rove

Principal Software Engineer

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!

Josh Lloyd

ML Engineer

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 ⏩🔥

Leonard Püttmann

data scientist

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.