Enterprises are increasingly recognizing the limitations of public cloud services for AI deployment, often encountering hidden costs and complexities that impede growth. Many are exploring alternatives, such as private AI infrastructures and hybrid solutions, as public cloud pricing and service delivery models are failing to meet their needs. The current charging strategy is unsustainable for most companies, prompting a pivot to AI private clouds or traditional on-premise systems. If public cloud providers don't adapt, they risk losing their dominance in enterprise computing, as stakeholders prioritize predictable performance and reasonable costs.
We're already seeing the consequences. More enterprises are exploring alternative approaches, including private AI infrastructure and hybrid solutions. They're finding that the promise of simple, scalable AI deployment in the public cloud often comes with hidden complexities and costs that make it difficult to achieve growth.
The current model of charging for general compute resources and adding premium fees for AI-specific services isn't sustainable for most enterprises, and they are moving on to non-cloud alternatives.
The stakes are high. As enterprises continue to invest heavily in AI initiatives, they'll gravitate toward platforms that can provide predictable performance, reasonable costs, and specialized infrastructure.
Public cloud providers risk losing their position as the default choice for enterprise computing if they can't adapt quickly enough.
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