https://renezander.com/tags/comparison/Recent content in Comparison on René Zander | AI Automation ConsultantHugoenWed, 15 Apr 2026 08:00:00 +0200https://renezander.com/guides/llm-api-comparison/Wed, 15 Apr 2026 08:00:00 +0200https://renezander.com/guides/llm-api-comparison/<p>I have five LLM providers wired into production code. Not in side projects. Real things I get paid to maintain. After two years of swapping between them, retrying failed calls at 3am, and debugging tool-use schemas, I have opinions.</p> <p>This is an LLM API comparison focused on what actually matters when you ship. Not benchmark leaderboards. Not marketing spec sheets. Features, SDK quality, failure modes, tool-use reliability, and whether the docs will waste your afternoon.</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>