Infrastructure on René Zander | AI Automation Consultant

Self-Hosted LLM vs API Cost: Break-Even Analysis (2026)

Thu, 16 Apr 2026 09:00:00 +0200

Every few months a client asks me the same question. “We’re burning $8k/mo on Claude. Should we self-host Llama?” The answer is almost always no, and the reason has nothing to do with whether the model is good enough. It has to do with what a GPU costs when it’s idle, and how much engineering time it takes to keep a serving stack healthy at 3am.

This guide breaks down self-hosted LLM vs API cost with real numbers. Hetzner GPU pricing, RunPod and Lambda hourly rates, Claude Sonnet 4.6 and Haiku 4.5 token pricing, and the break-even points that actually matter. The goal is to give you a decision framework, not a marketing pitch for either side.

GPU Cloud Comparison for AI Inference: 2026 Reality Check

Sat, 04 Apr 2026 13:00:00 +0200

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.

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.

Your Vector Database Decision Is Simpler Than You Think

Tue, 17 Mar 2026 07:41:59 +0000

Every week someone asks which vector database they should use. The answer is almost always “it depends on three things,” and none of them are throughput benchmarks.

I run semantic search in production on a single VPS. Over a thousand items indexed, embeddings generated on the same machine, queries return in under a second. But that setup only works because of the constraints I’m operating in. Change the constraints and the answer changes completely.

I Run 10 AI Agents in Production. They're All Bash Scripts.

Thu, 12 Mar 2026 14:29:44 +0000

A week ago I wrote about shipping AI agents the right way. That piece was about the harness: quality gates, token economics, multi-model verification. The stuff that separates demos from production.

A lot of people resonated with it. But I left out the part that actually eats most of my time: keeping the boring stuff running.

So let me walk you through what production AI agents actually look like when the conference talk is over.

Lots Of People Are Demoing AI Agents. Almost Nobody's Shipping Them The Right Way.

Wed, 04 Mar 2026 10:56:24 +0000

Lots of people are demoing AI agents. Almost nobody’s shipping them the right way.

Conference stages are packed with live demos of agents writing Terraform, spinning up Kubernetes clusters, and generating Helm charts on command. The audience claps. The tweet goes viral. And then… nothing ships.

Here’s the uncomfortable truth: the gap between “look what my agent can do” and “this runs in production every day” is enormous. I’ve been on both sides. I spent years as an Enterprise Architect watching organizations spin up AI pilots that never graduated. Now I run my own infrastructure with Claude as the core agent — not as a demo, not as a proof of concept, but as the actual engine that keeps things moving.