Simultaneously, a new breed of AI infrastructure providers is rising, offering bare metal, GPU-as-a-service, or colocation solutions purpose-built for machine learning. These platforms attract business by being more transparent, customizable, and affordable for enterprises tired of chasing discounts and deciphering complexity in hyperscaler pricing. The hyperscalers are responding with hybrid and multicloud offerings—even working to allow easier migration, better reporting, and more granular consumption-based pricing.
Still, there’s an acknowledgment in the boardrooms of Seattle and Silicon Valley: The easy growth is gone. Enterprises now want flexibility, especially when core business transformation depends on AI investment. Cloud providers must be more than arms-length landlords—they must become close partners, prepared to meet client workloads both on-prem and in the cloud, depending on what makes the most sense that quarter.
Navigating the hybrid cloud era
Repatriation doesn’t signal the end of cloud, but rather the evolution toward a more pragmatic, hybrid model. Cloud will remain vital for elastic demand, rapid prototyping, and global scale—no on-premises solution can beat cloud when workloads spike unpredictably. But for the many applications whose requirements never change and whose performance is stable year-round, the lure of lower-cost, self-operated infrastructure is too compelling in a world where AI now absorbs so much of the IT spend.
