Irrespective of industry, the rise of gen AI has reignited boardroom discussions about cloud strategies. While the potential is big, so are the complexities — especially for CIOs suddenly facing budget overruns, security risks, and tangled hybrid environments.
“Companies are trying everything, which leads to enormous costs,” says Juan Orlandini, CTO North America at Insight. “If you’re running gen AI in the public cloud, the costs add up quickly. You get charged for everything — compute, storage, and all the network traffic.”
Juan Orlandini, CTO North America, Insight
Insight
Those surging expenses reflect a broader reality. Gen AI workloads are unpredictable, data-intensive, and often experimental, according to Bastien Aerni, VP at GTT, a networking and security as a service provider. “CIOs often don’t know how successful a given initiative will be,” he says. “Overinvesting is risky, and underinvesting limits scalability and user experience.”
This cost is often amplified by data gravity in that once a large amount of data accumulates in one place, it becomes inefficient — both technically and financially — to move it elsewhere for processing. At the same time, cloud environments can unintentionally create data swamps through redundancy and uncontrolled sprawl. Orlandini warns of hidden costs when separating compute and data. “If your data is on prem and you use a cloud-based AI service, you either need high-speed connections or you need to duplicate the data in the cloud. Both are costly.”
