<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Aws on René Zander | AI Automation Consultant</title><link>https://renezander.com/tags/aws/</link><description>Recent content in Aws on René Zander | AI Automation Consultant</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 06 Apr 2026 08:00:00 +0200</lastBuildDate><atom:link href="https://renezander.com/tags/aws/index.xml" rel="self" type="application/rss+xml"/><item><title>Hetzner vs AWS for AI Workloads: The Honest Breakdown (2026)</title><link>https://renezander.com/guides/hetzner-vs-aws-ai-workloads/</link><pubDate>Mon, 06 Apr 2026 08:00:00 +0200</pubDate><guid>https://renezander.com/guides/hetzner-vs-aws-ai-workloads/</guid><description>&lt;p>Most &amp;ldquo;hetzner vs aws ai workloads&amp;rdquo; comparisons I read online are either breathless Hetzner fanboying or AWS enterprise sales brochures. Neither is useful when you actually have to pick where your AI inference pipeline, your n8n instance, or your fine-tuned Llama deployment should live.&lt;/p>
&lt;p>I run production AI systems on both clouds. Steady-state stuff sits on Hetzner. Burst GPU jobs and anything that has to integrate with an AWS-native enterprise backend goes on AWS. The decision is not ideological. It comes down to workload shape, team size, and what you actually need from your cloud.&lt;/p></description></item></channel></rss>