<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vectordatabase on René Zander | AI Automation Consultant</title><link>https://renezander.com/tags/vectordatabase/</link><description>Recent content in Vectordatabase on René Zander | AI Automation Consultant</description><generator>Hugo</generator><language>en</language><lastBuildDate>Tue, 17 Mar 2026 07:41:59 +0000</lastBuildDate><atom:link href="https://renezander.com/tags/vectordatabase/index.xml" rel="self" type="application/rss+xml"/><item><title>Your Vector Database Decision Is Simpler Than You Think</title><link>https://renezander.com/blog/your-vector-database-decision-is-simpler-than-you-think/</link><pubDate>Tue, 17 Mar 2026 07:41:59 +0000</pubDate><guid>https://renezander.com/blog/your-vector-database-decision-is-simpler-than-you-think/</guid><description>&lt;p>Every week someone asks which vector database they should use. The answer is almost always &amp;ldquo;it depends on three things,&amp;rdquo; and none of them are throughput benchmarks.&lt;/p>
&lt;p>I run semantic search in production on a single VPS. Over a thousand items indexed, embeddings generated on the same machine, queries return in under a second. But that setup only works because of the constraints I&amp;rsquo;m operating in. Change the constraints and the answer changes completely.&lt;/p></description></item></channel></rss>