Your AI.Your borders.
Open-weight models served on your GPUs, in your VPC or your data centre: the capability of frontier AI with nothing, prompt or weight or log, leaving your perimeter.
Every requestpicks a side.
Trace where a prompt actually travels. On the sovereign path it never meets infrastructure you do not control. On the public-API path, it crosses the border on every single call.
The sovereign path. Application to inference server to weights and back, a complete round trip inside the boundary. The model answers; the data never travels.
The public-API path. Every prompt and completion crosses the wall into infrastructure you do not control, under terms you do not set. For some workloads that is fine. For yours, perhaps not.
Sovereignty is not a policy document. It is a routing decision, made once, enforced by architecture.
Four rungs.All of them yours.
Most AI stacks rent at least one of these layers and call it ownership. We hand over all four, read from the metal up, with no layer you cannot inspect, move, or replace.
The application
YoursDefined in code, portable across AWS, GCP, and Azure, and yours to keep. No per-token meter sits between your product and its intelligence.
The model weights
YoursOpen weights on your own disks. Air-gapped sites hold them offline; updates arrive by signed artifact transfer, typically monthly.
The inference runtime
YoursA containerised serving stack on raw cloud primitives. No proprietary layer between you and the model, nothing you cannot take with you.
The GPUs
YoursHardware you choose: A100, H100, H200, or L40S on-premise; p4d and p5, A3, or NDv5 in cloud. Sized to concurrency and latency, not over-provisioned spend.
Read it bottom to top: metal, runtime, weights, application. There is no fifth rung where a platform takes a percentage.
What neverleaves.
Run the audit before the auditor does. Five classes of material move through an AI system; here is where each one lives when the system is sovereign.
Cross-region copies outside the boundary are refused at the policy layer, and the refusal is itself logged. Residency you can prove, not residency you assert.
Nine frameworks across healthcare, payments, government, and four data-residency regimes. Every deployment ships with a documented compliance mapping and an audit-ready evidence trail.
Models thatmove in with you.
Open families we deploy and serve inside the perimeter. The roster shifts as the field does; the ownership does not.
The open-weight default for general assistants and tool use.
Lean dense models when latency budgets lead the decision.
Strong multilingual coverage and long-context work.
Small enough for edge boxes and single-GPU serving.
A compact family that fine-tunes well on narrow domains.
Reasoning and code-heavy workloads at open weights.
The strongest closed frontier models still lead on some hard reasoning and coding tasks. When that gap matters more than residency for your workload, we will say so. Enterprise-licensed private deployments of closed models can also sit inside a compliant boundary, depending on your agreement, and in-region managed options (AWS Bedrock, Azure OpenAI, GCP Vertex AI) cover the cases where the perimeter is a region rather than a rack.
Your infrastructure at cost.One fixed number for the build.
Two parts, no meter. You pay for your own GPUs and cloud directly, at cost, with nothing marked up in between. We charge a fixed-scope fee for the initial 8 to 12 week deployment, quoted after discovery, with ongoing ops and model refresh as an option rather than a dependency.
below managed API providers, in typical total run cost at scale. Below that scale, we will tell you the API is cheaper, because it is.
Common questions about sovereign cloud.
What teams ask before they move a workload inside their own boundary.
Open models: Llama 4, Qwen 3, Gemma 3, Phi-4, DeepSeek, Mistral. Managed sovereign options: AWS Bedrock (in-region), Azure OpenAI (in-region), GCP Vertex AI (in-region). Enterprise-licensed closed models through private deployments (Anthropic Enterprise, OpenAI Enterprise) are also supported depending on your agreement.
For air-gapped environments, we ship a fully-containerized stack (LLM runtime, vector DB, observability) with offline model weights. All dependencies are pre-vendored. Updates happen via signed artifact transfer, typically monthly. We've deployed air-gapped systems for defense, intelligence, and financial institutions.
On-premise: NVIDIA A100, H100, H200, L40S clusters. Cloud: AWS p4d/p5, GCP A3, Azure NDv5. Edge: Jetson Orin for embedded, Mac Studio (M3 Ultra) for small-scale LLM inference. We size based on concurrency, latency SLA, and model size: right-sized clusters, not over-provisioned spend.
HIPAA (healthcare), SOC 2 Type II (SaaS), PCI-DSS (payments), ISO 27001 (general enterprise), FedRAMP (US government), GDPR (EU data residency), DPDP (India), APPI (Japan), LGPD (Brazil). Every sovereign deployment includes a documented compliance mapping and audit-ready evidence trail.
Two parts: your infrastructure (GPU choice and scale, on-prem or cloud, paid by you at cost) and our engagement fee for the initial 8-12 week deployment, with optional ongoing ops and model refresh. Both are fixed-scope quotes after discovery. At scale, total cost typically lands 30-60% below managed API providers.
Intelligence,inside the walls.
If a regulator, a board, or your own architecture says the data cannot leave, this is how serious AI still ships. Tell us where your perimeter is; we will put the intelligence inside it.