Cost Optimization for ML

ML infrastructure costs are often driven more by inefficiency than by model complexity.

It’s common to see idle GPU capacity, models running on expensive hardware without clear impact, and workloads that could run on CPUs with no noticeable quality loss.

A cost audit typically reveals that a large portion of spend comes from misallocation rather than necessity.

The takeaway is simple. Reducing cost isn’t always about changing models. It’s about using the right resources for the right workloads.