Cloud Providers, Enterprises Struggle to Keep Up with AI Costs

AI is putting pressure on cloud providers and enterprises alike from all angles.

Major cloud providers are currently grappling with the massive cost of building the proper data center infrastructure to keep up with power-hungry AI development in the hope that adoption of the tech marches on. Enterprises, meanwhile, are struggling with the sheer cost of using cloud to begin with, said Trevor Morgan, chief operating officer at OpenDrives. 

Data from cloud firm Wasabi shows that 62% of over 1,600 enterprise leaders experienced fees related to cloud storage cost overruns, with the unexpected charges often causing delays in business operations. AI is only heightening the expenses, said Morgan. 

The squeeze is prompting many enterprise leaders to reconsider the cloud-first approach, using it as an opportunity to find their so-called “perfect hybrid” strategy, said Morgan. This could mean repatriating data onto on-premise servers or using a private cloud service, he added. 

“They’re going to be looking at this from a cloud service perspective and weighing, ‘Does that add value?” said Morgan. “As the first big ‘go to the cloud’ craze kind of winds itself down, I think it’s actually going to be a good thing for them,” he added.

For cloud providers, however, the potential decline in enterprise business is “one of the major issues they’re struggling with,” he said. At the same time, they’re plunging billions into creating AI-ready infrastructure. 

• Amazon, which is the largest cloud services provider on the market, said in its earnings call in early February that it’s planning to spend more than $100 billion in 2025, primarily on infrastructure to support AI and cloud. 
• Microsoft said in January that it would spend $80 billion on AI-enabled data centers this year, and Google similarly plans to spend $75 billion on AI and cloud capacity. 
• These investments coincide with each of the so-called Big Three reporting a slowdown in growth of their cloud services units. 

Building data centers is an expensive endeavor, not only because of the components and infrastructure needed, but also the talent required to build them well. The rapidly evolving demands of AI make building these facilities even more tricky, he said. 

Generating a return on investment, meanwhile, hinges on adoption — even for hyperscalers. While major tech companies can continue building powerful models and relying on increasingly sophisticated data centers, recouping those costs isn’t going to be easy if enterprises and users don’t take up the tech, Morgan said. 

“There’s going to be some serious capital expenditures in the hopes that the AI movement itself gets fully established,” said Morgan. “The opportunities here are endless. We just don’t have endless resources.”