The Great GPU Scam: Why Your Cloud AI Budget Is Getting Robbed

Cloud Computing Insider

Aggressively pursuing GPU adoption—whether in the cloud or on-premises—often leads organizations straight into costly traps. A surprising amount of infrastructure is chronically overprovisioned, with organizations buying or renting more GPU power than they'll ever use "just in case." This overkill results in idle or underutilized GPUs that drain budgets, mirroring the same wasteful patterns in both environments. Most enterprise workloads don't even need GPU acceleration, but the current industry hype pushes adoption far beyond what actual business goals require. Making matters worse, soaring GPU prices aren't delivering transformative returns for mainstream use, meaning costs rise faster than the benefits. The common claim that cloud pay-as-you-go solves utilization issues is misleading: many companies simply leave pricey instances running, wasting just as much as they do on underused hardware racks. Only persistent, compute-intensive tasks like AI/ML training truly justify ongoing GPU investment. The bottom line: Regardless of environment, only strong governance, real-time observability, and right-sized, hybrid strategies truly control spend and prevent waste. Without tight oversight, both clouds and datacenters fall victim to the same needless overspending.