https://renezander.com/tags/cloud/Recent content in Cloud on René Zander | AI Automation ConsultantHugoenMon, 06 Apr 2026 08:00:00 +0200https://renezander.com/guides/hetzner-vs-aws-ai-workloads/Mon, 06 Apr 2026 08:00:00 +0200https://renezander.com/guides/hetzner-vs-aws-ai-workloads/<p>Most &ldquo;hetzner vs aws ai workloads&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.</p> <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.</p>https://renezander.com/guides/gpu-cloud-comparison-ai-inference/Sat, 04 Apr 2026 13:00:00 +0200https://renezander.com/guides/gpu-cloud-comparison-ai-inference/<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.</p> <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.</p>