I've spent close to a decade building distributed systems and cloud-native platforms. I helped teams containerize monoliths, adopt service meshes, fight with Helm charts at 2 AM. I love this stuff.
And I'm telling you: the infrastructure assumptions we built on are breaking.
Cloud-native was designed for a world of CPU-bound, stateless, request-response workloads. Horizontal scaling. Twelve-factor apps. Elegantly boring.
AI-native is a different animal entirely.
You're now scaling memory-aware GPU pipelines. Your "requests" consume tokens that cost real money per invocation. Your services have state — model weights, context windows, embedding caches — that doesn't fit the stateless paradigm.
What this means practically:
Capacity planning is GPU planning now. Most platform teams I talk to are still thinking in CPU cores and memory limits. Meanwhile their AI features are bottlenecked on GPU scheduling that their orchestrator doesn't natively understand.
Inference cost is COGS. Every AI feature you ship carries a per-call cost that directly eats your margin. This isn't like serving a webpage from a CDN. A single complex agent workflow can cost $2-5 in inference. Multiply that by your user base. Your CFO needs to know this number.
The hybrid question has flipped. It's not "cloud vs. on-prem" anymore. It's inference routing — which workloads hit cloud APIs, which hit your own GPU cluster, and which get quantized models at the edge.
The CTOs treating this as an infrastructure upgrade will be explaining cost overruns to the board within 18 months.
How are you handling GPU capacity right now — cloud APIs, own hardware, or some messy middle ground? Curious what's actually working.