<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Ai-Architecture on René Zander | AI Automation Consultant</title><link>https://renezander.com/tags/ai-architecture/</link><description>Recent content in Ai-Architecture on René Zander | AI Automation Consultant</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 17 Apr 2026 07:00:00 +0200</lastBuildDate><atom:link href="https://renezander.com/tags/ai-architecture/index.xml" rel="self" type="application/rss+xml"/><item><title>How to Choose an LLM for Production: 7 Criteria That Matter</title><link>https://renezander.com/guides/how-to-choose-llm-for-production/</link><pubDate>Fri, 17 Apr 2026 07:00:00 +0200</pubDate><guid>https://renezander.com/guides/how-to-choose-llm-for-production/</guid><description>&lt;p>Most teams pick an LLM for production the wrong way. They read a leaderboard, pick the top model, and wire it into an endpoint. Six weeks later they hit a rate limit during a traffic spike, or a compliance reviewer asks where EU data is processed, or the p99 latency kills a user-facing flow. Then the real selection work starts, under pressure, in production.&lt;/p>
&lt;p>This guide is how to choose an LLM for production the right way, before any of that happens. I run AI agents and LLM-backed automations for DACH clients, and every production deployment I&amp;rsquo;ve shipped went through the same seven-criteria filter. The order matters. Skip one and you will find out later, usually on a weekend.&lt;/p></description></item></channel></rss>