<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Qdrant on Manfred Dreese</title>
    <link>https://dreese.de/tags/qdrant/</link>
    <description>Recent content in Qdrant on Manfred Dreese</description>
    <generator>Hugo -- gohugo.io</generator>
    <language>en</language>
    <copyright>© 2026 Manfred Dreese</copyright>
    <lastBuildDate>Thu, 23 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://dreese.de/tags/qdrant/index.xml" rel="self" type="application/rss+xml" />
    
    <item>
      <title>Lessons Learned: 3 Years of Qdrant on Kubernetes for Product Recommendations</title>
      <link>https://dreese.de/posts/2026-three-years-with-qdrant/</link>
      <pubDate>Thu, 23 Apr 2026 00:00:00 +0000</pubDate>
      
      <guid>https://dreese.de/posts/2026-three-years-with-qdrant/</guid>
      <description>&lt;p&gt;Since 2023, I have served as a Tech Lead and consultant for a product providing large-scale recommendation solutions. A substantial part of our stack relies on Qdrant and Kubernetes. Over the years, we’ve handled millions of vectors and survived more than a few unexpected challenges. If there is one thing I’ve learned, it’s that in the world of vector databases, data science needs a rock-solid operational foundation to prevent lost opportunities. Understanding database internals and Kubernetes plumbing is what transforms nighttime pager alerts into fiction.&lt;/p&gt;</description>
      <media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://dreese.de/posts/2026-three-years-with-qdrant/featured.jpg" />
    </item>
    
  </channel>
</rss>
