Governments around the world are looking at implementing AI for a range of use cases. But infrastructure challenges are holding them back. We look at some common concerns they have and how OpenStack offers a way to tackle these issues.
Public agencies are racing to deploy AI. Be that language models for citizen services, computer vision for transport safety, or predictive analytics for healthcare capacity.
And while tons of tools and platforms have popped up, the hard part is standing up infrastructure that meets national security, residency, and procurement requirements while still giving data scientists the elasticity they need.
OpenStack sits in a rare sweet spot here. It’s battle-tested at hyperscale, fully open source, and deployable on government-controlled infrastructure with Kubernetes and modern storage built in.
OpenStack powers more than 45 million CPU cores in production, with multiple “mega-users” running more than a million cores.
Why this matters right now
- There's policy momentum. Governments have moved from AI “pilots” to national strategies. OECD’s policy tracker counted more than 1,000 AI policy initiatives across 69 countries as of 2024, with many calling out data sovereignty and public-sector capability building.
- The option to scale without the massive fees charged by hyperscalers. OpenStack’s footprint continues to grow; the OpenInfra community reported tens of millions of cores under management globally, underscoring its viability for large research and government estates.
- Sovereignty-by-design. Projects like Sovereign Cloud Stack (SCS), an OpenStack/Kubernetes-based stack supported by the German government and European industry, show how to operationalize sovereignty goals using open software and documented processes.
Use cases
There are plenty of use cases that could be handled by AI. For instance:
Citizen-service language models
Contact-center copilots for tax, benefits, or licensing thrive on elasticity but must respect data residency and access rules.
An OpenStack region pinned to national jurisdiction keeps embeddings and chat histories in country; Cinder+Barbican provides encrypted storage for transcripts; and Octavia fronts the inference endpoints with per-project quotas enforced in Keystone and Kubernetes RBAC.
The outcome is a modern user experience that still passes compliance review.
Public-health modelling at surge times
During outbreaks, epidemiology teams need GPUs quickly while keeping patient data governed.
Nova delivers GPU flavors close to the data; Magnum orchestrates batch jobs; object storage holds de-identified datasets and model artifacts with lifecycle rules. Because the entire stack runs in sovereign regions, sharing happens through policy-controlled buckets and signed URLs, so no bulk data export to an external vendor. SCS’s standardized approach is designed for exactly these provider-to-agency alignments. Including Cyborg as a service for managing GPU accelerators could further optimize performance and simplify orchestration of AI workloads.
Geospatial analysis for civil protection
Pipelines ingest satellite/drone imagery to object storage, tile and index on Kubernetes, and deploy inference behind micro-segmented networks in Neutron/OVN. For defense-adjacent analytics where extra assurance is required, selected inference nodes run as SEV-enabled VMs to reduce hypervisor trust.
Sovereign model hosting and regulated inference
When ministries deploy vision models for border or transport safety, inference services sit behind Octavia with strict egress controls; secrets and certificates live in Barbican; sensitive volumes are marked encrypted by default. If you must demonstrate reversibility in procurement, the stack’s openness, plus SCS’s shared conformance, lets you export images, manifests, and data without entanglement.
Are governmental bodies really choosing OpenStack?
The proof is always in the pudding. We have list a couple public-sector examples to show you what's possible:
- NUBO (France): A French interministerial project operated by the Ministry of Economy and Finance chose OpenStack to build a sovereign cloud for state workloads. Also, to keep control of data, costs, and roadmap, while retaining the ability to scale and integrate upstream tools.
- Sovereign Cloud Stack (Germany): A government-funded initiative that packages a consistent, open OpenStack/Kubernetes stack for providers and agencies, focusing on data sovereignty and supply-chain transparency. The program’s aim is a repeatable, auditable cloud baseline that public bodies can deploy and certify.
Beyond the policy context, which is in alignment, OpenStack also offers a whole host of benefits.
You can build it in state-owned facilities, operate it as a hosted sovereign cloud, or shared as a community platform across ministries. In every case, you keep jurisdiction over data and keys, inspect the source, and document the supply chain.
Projects such as NUBO and Sovereign Cloud Stack make this tangible. One is a national deployment narrative, the other a systematic way to reproduce the stack with clear interfaces and lifecycle rules. Together they show how public bodies can modernize without surrendering autonomy.
For cross-border initiatives, OpenStack’s ability to create federated clouds or multi-region deployments can support collaborative efforts between nations, such as joint disaster response or international research projects, while still enforcing data residency and compliance requirements.
There is also a scale argument. AI pilots stall when GPU capacity arrives late or data can’t leave a boundary. OpenStack’s global footprint has repeated shown that it can handle production load in many forms, from research clouds to telco edges. Government programs benefit from that maturity. Any firmware quirks and driver issues have been seen (and solved) somewhere before.
OpenStack also fits procurement needs. The stack itself is vendor-neutral, widely deployed, and supported by multiple providers, supporting data sovereignty, source access, and reversibility. That reduces lock-in and helps public agencies insist on exportable data, exportable images, and reproducible automation.
So what does a sovereign AI platform look like?
Think of three layers working together under your control:
Compute and isolation
Agencies carve out CPU- and GPU-backed flavors in Nova for training and inference. Where risk models demand it, they turn on confidential-computing options (e.g., AMD SEV) so model weights and sensitive features are encrypted in VM memory, reducing reliance on the host’s trust boundary. That setting is governed centrally and applied only to workloads that warrant the overhead. However, enabling SEV may require additional hardware validation and consequently, introduce performance trade-offs. This would need to be carefully evaluated during deployment planning.
Kubernetes as the workbench
Data scientists expect notebooks, batch jobs, and microservices to land on Kubernetes. With Magnum, operators expose a clean API for requesting clusters and node pools; underneath, those clusters are provisioned as first-class OpenStack resources, so lifecycle, quotas, and networks remain aligned with agency policy. Magnum’s current direction also includes a Cluster API driver, giving you a well-understood, declarative lifecycle while retaining Magnum’s governance surface.
Storage and networks suited to AI
Ceph underpins object buckets for datasets and artifacts (cheap/elastic) and block volumes for low-latency training runs (predictable IOPS). Overlay networking with Neutron/OVN provides micro-segmentation: projects map cleanly to ministries or programs, and east-west traffic can be limited to mission need. When Kubernetes needs L4/L7 ingress, the upstream cloud-provider-openstack targets Octavia as the standard load balancer, and Cinder CSI/Manila CSI present volumes to pods in a first-class way, avoiding one-off drivers. For advanced GPU workloads, Cyborg can orchestrate accelerators like GPUs and FPGAs, ensuring seamless integration with OpenStack resources.
Identity and encryption are baked in
Agency IdPs (SAML/OIDC) federate into Keystone; RBAC and quotas are enforced centrally and inherited by child projects. For protected classes of data, Cinder volume types are marked encrypted-by-default with keys managed in Barbican, so auditors can see a clear chain of custody for at-rest encryption and key rotations. (This is a common pattern in NUBO/SCS-style deployments.)
Enforce encryption as a default
Mark specific Cinder types “encrypted” and store keys in Barbican, with rotation events logged. Tie projects to IdP attributes so joiner/mover/leaver processes don’t fork in shadow directories. Where confidentiality is paramount, enable SEV for targeted VM flavors and document the trade-off in performance and operational complexity. Keep ingress narrow and observable by standardizing on Octavia, and treat every egress as a policy exception with logging.
Reduce operational risk with visibility
Prometheus and Grafana can cover API latencies (Keystone token issuance, Nova boot), scheduler throughput (placement-aware), OVN raft health, and Ceph I/O. Add ELK/Loki for logs. The important step is agreeing on numbers before a change window: normal token p95, acceptable error rates, expected queue depth under load. Then stick those panels on a single runbook page for cutovers. You don’t need a perfect “single pane of glass”; you need reproducible graphs that everyone trusts.
What about capacity planning during change?
Upgrades, blue-green rehearsals, and live migrations burn headroom. For general fleets, a compute buffer is sensible. GPU islands or SR-IOV/DPDK hosts often need more because migration options are constrained and drain times are longer.
Storage and network also feel the stress because live-migrating memory across hosts creates a spike in east-west bandwidth, and image pulls hammer object buckets.
Plan the window to avoid mission peaks, cap the “dual-run” duration, and document the triggers that end a rehearsal rather than letting it drift. For particularly sensitive workloads, consider a phased migration strategy for GPUs or SR-IOV devices, ensuring minimal disruption to ongoing AI pipelines and maintaining compliance with data residency policies.
How do you know if your implementation was successful?
- Residency & control: Percentage of AI workloads running on agency-controlled infrastructure in national jurisdictions (target >90% for sensitive data).
- Provisioning lead time: Median time to get a production Kubernetes namespace or GPU VM (target hours, not weeks, once the platform is live).
- Security posture: Share of volumes created with encryption enabled (target 100% for protected classes); share of AI workloads with memory-encryption where mandated.
- Cost observability: Percentage of GPU/CPU hours and storage TBs attributed to a program/grant code.
Additionally, tracking compliance KPIs, such as the percentage of projects using federated identity or the frequency of encryption key rotations, provides measurable proof of adherence to policies.
How can you start
Pick a small region or tenant and wire the essentials: identity federation, encrypted storage classes, and one AI pipeline end-to-end (ingest, train, register, serve). Prove the cutover mechanics in staging—database expand/contract, OVN raft health, and a time-boxed fail-forward/fail-back plan—then repeat the pattern. As policy evolves (and it will), keep the evidence bundle tidy: change tickets and approvals, before/after screenshots of key dashboards, logs from a backup restore drill, and API/provisioning test results. Those artifacts reduce meetings and speed audits.
It is straightforward. AI platforms that pass scrutiny, scale when teams need them and remain under national jurisdiction. With OpenStack, and with public examples like NUBO and SCS to copy from, you can get there using technology the government can own, inspect, and improve over time.
If you’re exploring how to build a secure and sovereign AI platform or navigate specific challenges in deploying OpenStack, schedule a consultation with a VEXXHOST expert. We’re happy to help you design a tailored deployment strategy, from infrastructure design to lifecycle management and policy enforcement.
