Qdrant Vector Database | Quick and Scalable Similarity Search

Discover Qdrant, an open-source vector database optimized for fast similarity searches. Ideal for AI applications, it…

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⚙️  Tech Specs

❑ Website Registered On:

  27th October, 2020

❑ Is this Mobile Friendly?

  Yes!

❑ Name Servers:

ns1.domain.com, ns2.domain.com

❑ Tech Stack:

Hugo, Chart.js, MathJax, Bootstrap, jsDelivr, Polyfill, MailChimp, Google Font API, Facebook, Clipboard.js, parcel, jQuery, Twitter Ads, Matomo Analytics, Linkedin Insight Tag, HubSpot, Hotjar, Google Tag Manager, Google Optimize, Google Analytics, Google Workspace, Netlify

📡  Connect

❑ Tool Name:

  Qdrant Vector Database

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Contact Info:

info@qdrant.com

❑ Email Service By:

  HubSpot

❑ Safe to Send? :

 Risky

❑ Verification Status:

  Valid

〒 Know More

❑ Use it For:

  Developer Tools

❑ Pricing Options:

  Free, Paid

❑ Suitable Tags:

  API, Open Source, Self Hosted

Check this Tool

Qdrant is an open-source vector database and search engine built for high-performance similarity search at scale. It allows developers to store, manage, and query high-dimensional vector data efficiently. Qdrant shines in applications involving neural network encodings or semantic-based matching. Its Rust-based architecture delivers blazing fast search capabilities while offering flexible deployment options.

Major Highlights

  • Cloud-native scalability with managed cloud service
  • Advanced compression techniques like quantization to reduce memory usage
  • Filtering and rich payload support for refined searches
  • Distributed architecture for horizontal scaling
  • SIMD hardware acceleration for optimized performance
  • Easy-to-use REST API and client libraries
  • Support for sparse vectors and multimodal data
  • Enterprise-grade security features
  • Flexible on-premise and cloud deployment options
  • Integration with popular ML frameworks and embedding models

Use Cases

  • Semantic search engines
  • Recommendation systems
  • Image and audio similarity search
  • Anomaly detection in complex datasets
  • Question-answering systems
  • E-commerce product matching
  • Content-based filtering
  • Facial recognition applications
  • Document clustering and organization
  • Multimodal search combining text, images, audio

Qdrant packs a punch for developers building AI-powered apps needing fast, accurate vector search. Its mix of speed, flexibility and enterprise-readiness makes it a top choice for production deployments. Whether you’re a startup or large enterprise, Qdrant can likely handle your vector search needs with room to grow.

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