<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai-Inference on René Zander | AI Automation Consultant</title><link>https://renezander.com/tags/ai-inference/</link><description>Recent content in Ai-Inference on René Zander | AI Automation Consultant</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 04 Apr 2026 13:00:00 +0200</lastBuildDate><atom:link href="https://renezander.com/tags/ai-inference/index.xml" rel="self" type="application/rss+xml"/><item><title>GPU Cloud Comparison for AI Inference: 2026 Reality Check</title><link>https://renezander.com/guides/gpu-cloud-comparison-ai-inference/</link><pubDate>Sat, 04 Apr 2026 13:00:00 +0200</pubDate><guid>https://renezander.com/guides/gpu-cloud-comparison-ai-inference/</guid><description>&lt;p>You want to run LLM inference in 2026 and the GPU cloud market has fragmented into roughly three camps: developer-first hourly clouds (Lambda, RunPod, Vast.ai), enterprise Kubernetes clouds (CoreWeave, AWS, GCP, Azure), and fixed-price European hosts (Hetzner, Nebius). The right pick depends less on the raw dollar-per-hour number and more on your utilization pattern, your compliance story, and your network egress shape.&lt;/p>
&lt;p>This is a gpu cloud comparison ai inference engineers actually use when planning production workloads. I will not pretend there is one winner. The honest answer is that Hetzner dominates for always-on L40S-class inference in the EU, RunPod Secure is the sweet spot for spiky workloads, CoreWeave and the hyperscalers are the only real answer for compliance-heavy H100 SXM, and Vast.ai only earns a spot in the experimentation phase.&lt;/p></description></item></channel></rss>