Kendall Clark, CEO of enterprise knowledge graph company Stardog, told Fierce there are three primary things driving private cloud adoption: a desire for data privacy, a need for data integration into AI and a need to close the distance between where the data resides and where the AI runs. There’s also the realization that the best economics come from owning your own compute infrastructure, especially when it comes to AI.
Stardog itself came to this realization, he said, and built its own GPU private cloud infrastructure to run workloads for its Stardog Voicebox AI offering.
All of those things, he added, are putting pressure on the idea that the public cloud is the end goal for enterprises.
“I think private GenAI will flatten that adoption curve unless the hyperscalers can do something else,” Clark said, referencing Garman’s comments about the public cloud’s growth opportunity.
While hyperscalers’ current strategy is to “buy all the GPUs,” Clark speculated that Nvidia is going to “realize at some point that having that customer concentration is bad for their stock and bad for their business and start thinking about how they can sell more GPUs to [companies] other than those four big whales.”
“Without GenAI the cloud migration story, the cloud adoption story would look much, much better for the shareholders of the hyperscalers than it’s going to look for the next few years,” he concluded. “Public cloud growth and private cloud growth, I would suspect, will look more like 50-50 than we would have said three years ago. Nobody would have said that three years ago.”
According to Chkaiban, New Street has predicted that in 2027 enterprise private AI infrastructure will account for 25-30% of the total market, with hyperscalers’ internal AI infrastructure accounting for another 25-30%. Public cloud will still make up around 45% of the market, he said.
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