Unlike traditional public clouds, these approaches are often built from the ground up to handle the unique demands of modern AI infrastructure. This means high-density GPU configurations, liquid cooling systems, and energy-efficient designs. More importantly, they allow enterprises to shift to ownership models or shared resources that cut costs over the long term.
Betting on the wrong business model
Public cloud providers are positioning themselves as the natural home for building and deploying AI workloads. Naturally, the focus at AWS re:Invent 2024 was again on generative AI and how the AWS cloud supports generative AI solutions. Early-stage AI experimentation and pilots have driven a short-term spike in cloud revenue as organizations flock to hyperscalers to train complex models and rapidly test new use cases.
Training AI models on public cloud infrastructure is one thing; deploying those systems at scale is another. By betting on AI, public cloud vendors are relying heavily on consumption-based pricing models. Yes, it’s easy to spin up resources in the cloud, but the cracks in this model are becoming harder to ignore. As companies shift from experimentation to production, long-term, GPU-heavy AI workloads don’t translate into cost efficiencies.