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High-Performance Vector Search at Scale

Powering the next generation of AI applications with advanced, open-source vector similarity search technology.

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Qdrant Powers Thousands of Top AI Solutions.

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.

All features

Cloud-Native Scalability & High-Availability

Enterprise-grade Managed Cloud. Vertical and horizontal scaling and zero-downtime upgrades.

Qdrant Cloud
Ease of Use & Simple Deployment

Quick deployment in any environment with Docker and a lean API for easy integration, ideal for local testing.

Quick Start Guide
Cost Efficiency with Storage Options

Dramatically reduce memory usage with built-in compression options and offload data to disk.

Quantization
Rust-Powered Reliability & Performance

Purpose built in Rust for unmatched speed and reliability even when processing billions of vectors.

Benchmarks

Our Customers Words

Bayer

“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.“

Hooman Sedghamiz

Hooman Sedghamiz

Director Al /ML, Bayer

CB Insights

“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

Alex Webb

Director of Engineering, CB Insights

Bosch

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

Jeremy T. & Daly Singh

Jeremy T. & Daly Singh

Generative AI Expert & Product Owner, Bosch

Cognizant

“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

Kyle Tobin

Principal, Cognizant

Integrations

Qdrant integrates with all leading embeddings and frameworks.

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Deploy Qdrant locally with Docker

Get started with our Quick Start Guide, or our main GitHub repository.

1 docker pull qdrant/qdrant
2 docker run -p 6333:6333 qdrant/qdrant

Vectors in Action

Turn embeddings or neural network encoders into full-fledged applications for matching, searching, recommending, and more.

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.

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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.

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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.

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Data Analysis and Anomaly Detection

Transform your approach to Data Analysis and Anomaly Detection. Leverage vectors to quickly identify patterns and outliers in complex datasets. This ensures robust and real-time anomaly detection for critical applications.

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Turn embeddings or neural network encoders into full-fledged applications for matching, searching, recommending, and more.

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