Solved: Are we ignoring the main source of AI cost? Not the GPU price, but wasted training & serving minutes.

🚀 Executive Summary TL;DR: The true cost of AI stems from wasted training and serving minutes, not GPU hardware prices, often due to a ‘fire and forget’ mentality. Solutions range from quick ‘Dead Man’s Switch’ scripts to robust MLOps platforms and cost-gating processes, all aimed at regaining control over spiraling cloud expenses. 🎯 Key Takeaways AI’s primary cost driver is wasted GPU-minutes from unmanaged training and serving, stemming from a disconnect between data scientists’ experimentation and DevOps’ budget adherence. Implementing MLOps platforms like Kubeflow or AWS SageMaker Pipelines allows for defining resource constraints in code, enabling repeatable, cost-controlled experimentation and automatic resource cleanup. Process-level ‘cost-gating’ for high-threshold jobs introduces a mandatory approval step, fostering awareness and preventing unchecked spending without necessarily obstructing innovation. The real cost of AI isn’t the GPU price tag; it’s the unchecked, spiraling