Qdrant Blog

Satellite Vector Broadcasting: Near-Zero Latency Retrieval from Space

The future of vector search, featuring a constellation of CubeSats for ultra-low-latency vector retrieval. Complete with benchmark results and field reports from our beta testers.

HubSpot & Qdrant: Scaling an Intelligent AI Assistant

Explore how HubSpot uses Qdrant to scale Breeze AI, enhancing customer engagement with faster, accurate vector search capabilities.

Vibe Coding RAG with our MCP server

Check out how the MCP server can give you more control over the quality of vibe coding with AI agents like Cursor, and Claude Code!

How Deutsche Telekom Built a Multi-Agent Enterprise Platform Leveraging Qdrant

Learn about Deutsche Telekom's requirements for scaling enterprise AI agents, key AI stack considerations, and how the team built a Platform as a Service (PaaS) - LMOS (Language Models Operating System) — a multi-agent PaaS designed for high scalability and modular AI agent deployment.

Introducing Qdrant Cloud’s New Enterprise-Ready Vector Search

Discover Qdrant Cloud's enterprise features: RBAC, SSO, granular API keys, advanced monitoring/observability.

Metadata automation and optimization - Reece Griffiths | Vector Space Talks

Metadata plays a critical role in vector search accuracy, yet it’s often overlooked. In this episode of Vector Space Talks, Reece Griffiths, CEO of Deasy Labs, explains why metadata automation is essential for scalable AI systems. He walks us through how Deasy Labs orchestrates metadata extraction, classification, and enrichment to boost retrieval efficiency.

How to Build Intelligent Agentic RAG with CrewAI and Qdrant

Learn how to build an agentic RAG system to semi-automate email communication with CrewAI, Qdrant, and Obsidian.

Qdrant 1.13 - GPU Indexing, Strict Mode & New Storage Engine

Voiceflow & Qdrant: Powering No-Code AI Agent Creation with Scalable Vector Search

Learn how Voiceflow builds scalable, customizable, no-code AI agent solutions for enterprises.