Y ou did it. You battled the excellent fight, convinced the board to go all-in on AI, and also browsed the GPU scarcity from hell.
Your brand-new Kubernetes collection is humming with effective GPUs, and the information scientific research team is lastly educating designs at the rate of light. You’re living the desire.
After that the cloud expense lands.
It’s a digestive tract punch. A huge number that makes you question everything. You take a deep breath and inform on your own, “This is the rate of development. We have to invest cash to earn money.”
Suppose I informed you that’s a calming lie? Suppose I informed you that you’re likely burning 60 % of that budget plan on … absolutely nothing?
- After bookkeeping over 50 “AI-ready” Kubernetes clusters, my team and I discovered a pattern so constant it was sickening.
- A quiet 60 % tax, levied on one of the most expensive resources, all many thanks to an essential misunderstanding of exactly how Kubernetes deals with GPUs.
This isn’t nearly money evaporating right into slim air. It has to do with your information scientists staring at a “Pending” husk standing, embeded a line up for resources they believe are complimentary. It has to do with your multi-million buck AI initiative being strangled by a trouble you don’t even understand you have.