Every enterprise AI strategy eventually comes down to the same question: Should proprietary data travel to the model, or should the model come to the data?

As privacy rules tighten and geopolitical risk rises, more organizations are choosing the second option. That is changing how data platform providers present themselves, and Cloudera Inc. has built its AI strategy around deployment flexibility. The company doesn’t ask customers to hand their data to an external AI service. Instead, it aims to run AI inside the environments where that data already lives, according to Wim Stoop (pictured), product director at Cloudera.

“Rather than saying, ‘Just give us all your data and we’ll in return provide you access to a model that’s then contextualized based on that data,’ we turn things around,” Stoop said. “We say, ‘We’ll bring AI to where the data is and where the data lives.'”

Stoop spoke with theCUBE’s Rebecca Knight and Paul Gillin at Cloudera EVOLVE26, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how enterprises can bring AI to their data while maintaining control and managing costs. (* Disclosure below.)

Sovereignty moves to the center of enterprise AI strategy

Cloudera’s latest move in that direction is a partnership with Mistral AI SAS, which brings Mistral’s models into Cloudera’s data platform. For European customers in particular, the model maker’s local roots matter as much as its technology, Stoop noted.

“Certainly for us in Europe, having a French organization, a local company, that provides these models and provides the complete stack is very attractive for a lot of our customers,” Stoop said. “Mistral was a very easy choice for us to say, ‘Those are the ones that we’re going with.'”

AI is at the top of a maturity curve that starts with sovereign data, sovereign cloud and local operations, according to Stoop. Rules such as the European Union’s Digital Operational Resilience Act and the U.S. CLOUD Act are pushing organizations to prove they have control rather than simply claim it. But control alone won’t move AI out of the pilot phase. For many companies, the bigger obstacle in an enterprise AI strategy is the cost of running models at scale, because generating tokens takes a lot of hardware.

“If you use a public provider for that, the more you use your model, the more you get hit on cost,” Stoop said. “How do I get that high utilization of a good model inside my organization without being exposed to horrific costs? That, I think, is the biggest challenge that organizations are struggling with right now.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Cloudera EVOLVE26:

(* Disclosure: TheCUBE is a paid media partner for the EVOLVE26 2026 event. Neither Cloudera, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE