You launch your AI platform with clean abstractions, promising eval metrics, and a roadmap that looks reasonable on paper. Six months later, latency creeps up, GPU costs double, hallucinations spike
*]:pointer-events-auto scroll-mt-[calc(var(–header-height)+min(200px,max(70px,20svh)))]” dir=”auto” data-turn-id=”request-WEB:7369a85e-60fa-4167-8cbc-fa5309d45b58-1″ data-testid=”conversation-turn-4″ data-scroll-anchor=”true” data-turn=”assistant”> Real-time analytics sounds simple until you try to run it: ship events from a dozen systems, transform them fast, store them cheaply, and
You shipped the feature in two weeks. A clean abstraction layer, a single HTTPS call to a frontier model, and suddenly your product can summarize, classify, generate, and reason. No
Your deployment pipeline probably feels like the safest part of your system. It is automated, versioned, peer reviewed, and covered in green checkmarks. But if you have ever chased a
You can usually tell within 30 minutes whether AI agents will scale or devolve into chaos. The scalable ones feel boring in the best way: predictable loops, explicit state, sharp
You usually start thinking about cross-region databases right after the first time you get burned. Maybe it was a regional cloud outage. Maybe it was a fiber cut that isolated
You have probably sat through an AI architecture review where everything looked clean on the whiteboard. The data pipeline was “robust.” The model was “state of the art.” The monitoring
You do not notice adaptive concurrency control when it works. You notice it at 2:17 a.m., when your API latency jumps from 80 ms to 8 seconds, CPU is pegged,
Architecture rarely collapses all at once. It drifts. One quarter, you add a service to move faster. Next quarter, you split a database for scale. A year later, onboarding a