Generative AI shifts the cloud calculus
Somerset Capital Group is one organization that has opted to go private to run its ERP applications and pave the way for generative AI. The Milford, Conn.-based financial services company migrated data to the public cloud more than a decade ago and will continue to add workloads there, especially for customer-centric applications. But the company’s critical data — and any future generative AI data — will likely run on its new hosted private cloud, says Andrew Cotter, Somerset’s EVP and CIO.
“As we are testing and dipping our toes in the water with AI, we are choosing to keep that as private as possible,” he says, noting that the public cloud has the horsepower needed for many LLMs of today but his company has the option of adding GPUs if needed via its privately owned Dell equipment. “You don’t want a mistake to happen and have it end up ingested or part of someone else’s model. We’re keeping that tight control and keeping it in the private cloud.”
Todd Scott, senior vice president for Kyndryl US, acknowledges that AI and cost are among the key factors driving enterprises toward private clouds.
“Most enterprises are currently exploring AI on the public cloud, but we expect clients will ultimately bring the app to their data and run AI where the data is, in private environments and at the edge,” he says.
“Another factor that’s driving a move back to private cloud is predictability of cost,” Scott says. “Agile enterprises, by definition, make frequent changes to their applications, so they sometimes see big fluctuations in the cost of having their data on public clouds. Private clouds provide more predictability because the infrastructure is dedicated.”
Buying into private cloud
Analysts support that private cloud spending is on the rise.
