Initially, the promise of the cloud is compelling, offering immediate scalability, rapid provisioning, and managed services. However, as organizations transition from pilots and proofs of concept to production-grade, steady-state AI, cloud costs can escalate rapidly, sometimes far exceeding initial forecasts.
Resource-intensive AI training or inference jobs in the cloud can trigger unexpected, fluctuating bills, often leaving finance teams scrambling for answers. Moreover, AI workloads tend to be “sticky,” consuming large volumes of compute that require specialized GPUs or accelerators, which come at premium prices in the cloud. Today, those same components are much cheaper to buy directly than they were 10 years ago, essentially reversing the previous equation.
The economics of hardware costs
A decade ago, acquiring advanced hardware for AI was costly, complex, and risky. Organizations faced long procurement cycles, supply chain volatility, and the daunting challenge of maintaining bleeding-edge gear. Public cloud was the solution, offering pay-as-you-go access to the latest GPUs and accelerators, with none of the upfront costs.
