Palantir Technologies has named Nebius its preferred sovereign AI infrastructure partner under a strategic partnership that will bring Nebius compute and inference services into Palantir’s enterprise environment, the companies announced Tuesday.

Following an integration period, eligible Palantir commercial customers will be able to access Nebius cloud and inference infrastructure while retaining control over their compute, data and models.

Sovereign AI stack for commercial customers

The companies said the integration will place Nebius compute and inference endpoints inside the Palantir enterprise perimeter. Customers will be able to deploy open models on Nebius infrastructure and adapt them using their own proprietary data.

Palantir positions the arrangement as part of its Sovereign AI Operating System, which combines its AIP, Ontology, Foundry and Apollo products. Nebius provides the underlying AI-native compute and cloud infrastructure.

The preferred-partner designation does not by itself make the arrangement exclusive. The announcement focuses on giving eligible customers another way to run models on infrastructure integrated with Palantir’s authorization and isolation layers.

Companies plan new compute capacity

Nebius and Palantir also plan to work together on bringing additional AI capacity online. The companies said this could include modular data center deployments at sites where power is already available, an approach intended to shorten the time required to add compute.

Power availability has become a major constraint on new AI infrastructure, especially where grid connections and large-scale data center construction can take years. Modular deployments can shift part of the build toward locations with secured power rather than waiting for entirely new campuses.

Nebius has also been expanding its sector-specific AI relationships. TNGlobal recently interviewed the company’s healthcare lead on governed healthcare AI and local validation.

The Palantir partnership extends that infrastructure strategy into enterprise sovereign AI, where control over data and models is increasingly tied to where workloads run and how the surrounding platform enforces access.

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