// AI / SOVEREIGN AI
When compliance blocks public cloud AI, you bring the answer.
We cannot send patient records to a US hyperscaler. Legal won't sign off until we know where the data goes. We architect sovereign AI across air-gapped, on-premises and jurisdictional deployments, host open-weight models on the client's own hardware, and answer GDPR, DORA, NIS2 and the AI Act clause by clause. All under your brand.
Start a sovereignty conversationCompliance teams are blocking public cloud AI on the first call.
Partners selling into regulated industries lose AI deals not for lack of ambition, but because the client's legal and compliance teams say no before the project starts. GDPR, DORA, NIS2 and the EU AI Act are converging on one demand: know exactly where your AI workloads run, who controls the infrastructure, and prove it in an audit. Around 44 percent of organisations are actively weighing sovereign cloud for AI, yet fewer than one in five have a detailed strategy. The partner who arrives with a credible architecture wins before the competitor gets a meeting.
What we build into the deployment.
Air-Gapped Deployment
Google Distributed Cloud Air-Gapped for fully network-isolated AI: zero internet connectivity, zero vendor telemetry. The workload runs where nothing can reach it from outside, which is what some regulators and clients flatly require.
On-Premises Cloud Consistency
AWS Outposts brings SageMaker and Bedrock inference onto the client's own premises with the same control plane they already know, so a sovereign deployment does not mean relearning the stack.
Sovereign Control Plane
Azure Sovereign Cloud with the Local control plane keeps the full operational surface inside the client's jurisdiction for Microsoft-stack estates. Platform choice follows the client's regulatory position, not a vendor's preference.
Open-Weight LLM Hosting
Llama 3, Mistral Large 3 and DeepSeek R2 served on the client's own hardware with vLLM: PagedAttention memory management, continuous batching and an OpenAI-compatible API. Up to 18 times lower cost per million tokens than commercial APIs, with zero data egress.
Regulatory Mapping
Specific obligations under GDPR, DORA, NIS2 and the EU AI Act mapped to the right deployment model, including DORA's requirement to be able to exit a cloud provider. The architecture answers the regulation clause by clause.
Ongoing Sovereign Governance
Compliance monitoring aligned to the evolving regulations, with quarterly reports and architecture updates when guidance changes. Sovereignty is a posture to maintain, not a box ticked once at handover.
The numbers behind sovereign AI.
How the options compare.
What your client gets from public cloud AI, a single-platform specialist, or our team under your brand.
Sovereign AI, answered straight.
01 What is the difference between sovereign cloud and private cloud AI?
Private cloud puts AI workloads on infrastructure you control. Sovereign cloud goes further: it guarantees data residency inside a specific jurisdiction, operational control by personnel within that jurisdiction, and compliance with local regulation such as GDPR, DORA and NIS2. Sovereign AI means no data crosses a border and no foreign entity can compel access. Private is a starting point; sovereign is the full requirement.
02 Won't on-premises AI cost far more?
For high-volume inference at scale, on-premises hosting with vLLM cuts cost per million tokens by up to 18 times against commercial APIs. For a client running 100,000-plus queries a day, the total cost of ownership usually favours on-premises within four to six months. We model that explicitly in the business case, so the number is grounded, not assumed.
03 We already have an AWS or Azure relationship. Why a special deployment?
Public cloud AI is not sovereign AI. AWS European Sovereign Cloud and the Azure EU Data Boundary address residency at the infrastructure layer, but they do not cover on-premises inference, DORA concentration risk (which demands the ability to exit a provider), or clients who need physical network isolation. We map the specific regulatory requirement to the right deployment model rather than assuming the existing contract is enough.
04 How do we know this stays compliant as regulation evolves?
Sovereign AI governance is delivered as an ongoing service, not a one-time assessment. We provide compliance monitoring aligned to the EU AI Act, GDPR, NIS2 and DORA, quarterly reports, and architecture updates when guidance shifts, all under your brand. Compliance is maintained, not declared once and forgotten.
05 Can we run open models like Llama or Mistral in production?
Yes. Llama 3 (70B and 405B) and Mistral Large 3 (675B MoE, Apache 2.0) are production-ready open-weight models. We serve them with vLLM, which brings PagedAttention memory management, an OpenAI-compatible API and continuous batching. Stripe cut inference costs by 73 percent after moving to vLLM, so the savings are real at scale.
06 How long does a sovereign AI deployment take?
Initial production deployment of targeted workloads (specific classification or inference services) can be done in eight to twelve weeks. Full migration of a complex environment takes longer. On-premises LLM hosting is usually the fastest path: vLLM on existing GPU hardware can be production-ready in four to six weeks.
Tell us what your clients need.
A tri-cloud migration. A 200-site SD-WAN rollout. A security architecture before NIS2 hits. An AI system your client is asking about next quarter. We scope it, staff it, and deliver it under your brand. One conversation tells us if we are the right team.