AI sovereignty starts at the infrastructure layer. Learn why governments choose OpenStack and Kubernetes to control compute, data, and AI workloads.
§ Sovereignty Is No Longer Abstract
AI is no longer just a technology investment. It is rapidly becoming strategic infrastructure comparable to energy grids, telecommunications networks, and defense systems.
Training data, model weights, and inference systems are increasingly treated as national assets. Yet much of the infrastructure powering these systems sits inside hyperscaler ecosystems, where Amazon, Microsoft, and Google collectively control roughly 70% of the European cloud market. Ecosystems governed by foreign jurisdictions, subject to external pricing decisions, and tightly coupled to proprietary platforms that were never designed with sovereignty in mind.
In the AI era, digital sovereignty is no longer a policy discussion. It is an infrastructure design decision determined beneath the application layer: how compute is provisioned, where data persists, and whether the underlying platform is open or opaque.
Platforms built on OpenStack and Kubernetes are becoming strategically important in this context. They enable governments and regulated industries to run sovereign AI on infrastructure they can fully control, operate, and govern without proprietary dependencies or external jurisdictional risk.
Sovereignty is not a feature that can be added later. It is a foundation that must be designed from the start.
§ 1 AI Changes the Sovereignty Equation
Cloud dependency is not new, but AI changes the equation. Traditional workloads like web apps, databases, or SaaS platforms created dependencies that were real but manageable. You could migrate a VM, move a database, or shift traffic between regions.
AI workloads are heavier and harder to move. They depend on specialized hardware, massive datasets, and tightly integrated infrastructure. As a result, they concentrate dependency in ways that can directly affect sovereign control.
GPU availability is one of the biggest constraints. AI training and inference rely on specialized hardware that remains scarce, and the largest pools of GPUs sit inside hyperscaler environments. Access is often controlled through quotas, credits, or regional capacity limits.
Data also creates its own form of gravity. Training datasets, model checkpoints, and artifacts quickly grow to enormous sizes, making relocation expensive and operationally difficult. Once these assets live inside a provider’s storage environment, moving them becomes increasingly complex.
Capacity concentration adds another layer of dependency. Training runs that take weeks and cost millions require guaranteed compute availability. Organizations often commit to long term capacity agreements simply to secure the resources they need.
The result is a new form of lock in driven by cost volatility, jurisdictional exposure, and deep platform coupling. GPU pricing can fluctuate, data may fall under foreign legal frameworks, and AI pipelines often integrate with proprietary services that are difficult to replace.
Kubernetes helps by orchestrating workloads and enabling portability across clusters. But orchestration alone does not solve sovereignty. The real dependency sits below it in who controls the GPUs, where the data resides, and how the infrastructure itself is operated.
If you want to learn more about running Kubernetes in 2026, we encourage you to read this blog post.
In the AI era, sovereignty is not about where containers run. It is about who owns the infrastructure beneath them.
§ 2 Why Open Infrastructure Is the Strategic Choice
Sovereignty requires more than policy. It requires architectural leverage, the ability to inspect, modify, and move infrastructure without relying on a single provider. Organizations need to understand how their systems operate and retain the freedom to change where workloads run.
Open infrastructure supports this by design. Transparent APIs, auditable code, and open control planes make it possible to see how compute, networking, and storage actually function. When the platform itself is open, organizations retain the ability to migrate or adapt their infrastructure as requirements change.
This is why governments and regulated industries increasingly favor open systems. Proprietary platforms often hide critical infrastructure layers, making it difficult to guarantee data residency, enforce network boundaries, or fully audit compliance.
A practical model separates infrastructure control from workload orchestration. OpenStack governs the infrastructure layer, managing compute, storage, networking, and identity through open and auditable APIs. Kubernetes orchestrates applications, handling scheduling, scaling, and workload portability across environments.
Together, they create a stack where the infrastructure remains sovereign and the workloads remain portable. This combination allows organizations to maintain full control of their systems without being locked into a single provider.
§3 OpenStack + Kubernetes: The Strategic Stack
Sovereignty is not achieved with a single tool. It comes from a layered architecture where control of infrastructure and control of applications remain separate and portable.
OpenStack governs the foundation. It manages compute provisioning, GPU allocation, networking, storage placement, and identity through open APIs across regions and availability zones. In practical terms, OpenStack determines where resources live and who controls them.
Kubernetes governs the workloads. It handles orchestration, scheduling, scaling, and service discovery using widely adopted CNCF standards. Kubernetes determines how applications run and how easily they can move between environments.
The strength of this combination lies in the separation of responsibilities. Infrastructure choices do not dictate how applications are built, and application choices do not lock organizations into a specific provider’s hardware. Each layer evolves independently while remaining transparent and auditable.
For governments and regulated industries, this architecture offers something hyperscalers struggle to provide: infrastructure that is sovereign at the base, portable at the workload layer, and open throughout the stack.
If you want to learn more about how to reclaim data control, we encourage you to read this blog post.
3.1 Avoiding the Managed Dependency Trap
Not every Kubernetes or OpenStack deployment delivers real openness.
Managed platforms often begin as convenient solutions but gradually introduce hidden dependencies. Proprietary control plane extensions, vendor specific CRDs, and tightly integrated storage or GPU services slowly bind workloads to a single environment.
Each integration feels helpful in the short term. Over time those conveniences become constraints. Migration grows more complex, portability erodes, and organizations find themselves managing around a platform rather than managing it.
The alternative is maintaining alignment with upstream projects. Using open standards instead of proprietary extensions, relying on upstream Kubernetes and OpenStack distributions, and maintaining transparent infrastructure supply chains ensures that every component remains auditable and replaceable.
Long term architectural independence is not about rejecting managed services entirely. It is about ensuring every layer can be replaced, migrated, or operated independently when circumstances require it.
3.2 How VEXXHOST Delivers This Model
This architecture is not theoretical. VEXXHOST delivers it today through Atmosphere.
Atmosphere is built directly on upstream OpenStack and CNCF certified Kubernetes, without proprietary forks or vendor specific extensions. Workloads running on Atmosphere operate on standard open infrastructure that remains portable and transparent.
The platform is designed for demanding modern workloads, including GPU accelerated AI training and inference, high performance networking, and flexible deployment across on premise, colocation, or hybrid environments.
What ultimately differentiates VEXXHOST is not only the technology but the operating philosophy. By maintaining strict alignment with upstream projects and avoiding proprietary modifications, VEXXHOST operates as an open infrastructure provider rather than a closed platform vendor. Organizations retain control of their infrastructure, maintain a clear exit path, and preserve long term sovereignty over their systems.
If you want to explore this evolution further, we recently examined the broader shift in cloud architecture in “OpenStack, Kubernetes, and AI: What 2025 Taught Us About the Future of Cloud.”
§4 Sovereignty as Strategic Leverage
Digital sovereignty is not about rejecting the cloud. It is about keeping the freedom to choose.
Organizations need the ability to move workloads when policy changes, audit infrastructure when compliance requires it, and avoid terms that create long-term dependency. Sovereignty simply means maintaining architectural independence.
AI makes this more urgent. GPUs are concentrated in a few providers, training datasets grow quickly, and pricing models are often dictated by whoever owns the infrastructure.
Open infrastructure changes that balance. It allows organizations to decide where critical workloads run, how resources are governed, and how their AI platforms evolve over time.
Sovereignty is not defensive. It is leverage.
Conclusion
In the AI era, sovereignty is defined at the infrastructure layer. It comes down to who controls the compute, networking, storage, and orchestration behind every model.
Organizations that control this layer keep their flexibility. They choose where their data lives, who provides their compute, and how their systems evolve.
Platforms built on OpenStack and Kubernetes offer a practical foundation for this model. By relying on open standards and upstream technologies, organizations can run modern workloads while keeping control of their infrastructure.
The future of AI will belong to those who control the platforms it runs on.
Explore Atmosphere and discover how open infrastructure powers AI without lock-in.
