<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vector-Search on René Zander | AI Automation Consultant</title><link>https://renezander.com/tags/vector-search/</link><description>Recent content in Vector-Search on René Zander | AI Automation Consultant</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 09 May 2026 09:00:00 +0200</lastBuildDate><atom:link href="https://renezander.com/tags/vector-search/index.xml" rel="self" type="application/rss+xml"/><item><title>Pinecone vs RunPod for Vector Search: Managed vs Self-Hosted (2026)</title><link>https://renezander.com/guides/pinecone-vs-runpod-vector-search/</link><pubDate>Sat, 09 May 2026 09:00:00 +0200</pubDate><guid>https://renezander.com/guides/pinecone-vs-runpod-vector-search/</guid><description>&lt;p>Every couple of months a client asks whether they should swap Pinecone for self-hosted vector search on a rented GPU. The answer depends on three numbers: vectors stored, queries per second, and how much your team wants to babysit a Qdrant cluster. This guide walks the math with real RunPod and Pinecone pricing.&lt;/p>
&lt;p>If you&amp;rsquo;re already comfortable with the self-hosted-vs-API tradeoff for LLMs, the vector-search version is the same shape with different constants. I covered the LLM side in &lt;a href="https://renezander.com/guides/self-hosted-llm-vs-api/">Self-Hosted LLM vs API Cost: Break-Even Analysis&lt;/a>. This guide is the parallel piece for the retrieval layer.&lt;/p></description></item></channel></rss>