Rent it
Public AI APIs
- Upside: Fast to start
- Downside: Your data leaves your network on every request
- Downside: Bills grow with every user and use case
Sovereign AI for the enterprise
Your data. Your models. Your costs.
OneInfer Private Cloud is designed to run AI inside your own cloud account or data center: one OpenAI-compatible endpoint for open and commercial models, with guardrails, routing and cost control built in. It is for enterprises that must keep data, model weights and keys in their network, plan and charge back what AI costs, and scale without vendor lock-in.
Our engineers are available today. They take your first use case from idea to production on your own infrastructure, and stay accountable for the results. The platform is in development and released in stages; you move onto each part as it ships.
How an engagement goes
Step 1 of 4
With your business unit, we pick one workflow that pays back and agree targets for accuracy, latency and unit cost.
Both usual ways of running AI break at scale
Build it
Your own open-source stack
- Upside: Your data stays with you
- Downside: Needs a platform team to run and secure it
- Downside: Months before the first workload goes live
Sovereign AI gives you the control of building it, without having to build it.
Sovereign AI means you own three things
What the platform is designed to enforce; our engineers apply the same principles today.
Nothing leaves your network
- Where it runs
- Your VPC or air-gapped data center, on EKS, AKS, GKE, OpenShift or VMware.
- Identity and keys
- Your SSO (Okta, Entra ID), zero-trust controls and your own KMS or HSM keys.
- Logging and audit
- Prompt logging off by default; a tamper-evident audit trail to your SIEM.
Your cloud account or data center
- Your appsCopilots, agents, search, documents
- OneInferGuardrails, routing, cost and tracing
- Your modelsLlama, DeepSeek, Qwen on your GPUs
Hosted APIs, only if you chooseOpenAI, Anthropic, Bedrock
Every AI dollar accounted for
- Cost per resolved task
- What each answer costs, set against the work it completes.
- Chargeback and caps
- Spend by business unit, project and cost center, with hard budget caps.
- Capacity, not consumption
- Predictable capacity on your own or reserved GPUs, not per-token bills.
Chargeback, caps and automatic cost control are in development; today our engineers scope each use case against a unit-cost target.
- Model calls
- plus Retrieval
- plus Agent loops
- plus GPU time
- equals Cost per resolved task (not per token)
Scale across the business without lock-in
- One endpoint
- OpenAI-compatible, for open and commercial models. Apps switch by changing one URL.
- Checked answers
- Guardrails on prompts and answers; accuracy checks cite sources or escalate to a person.
- Efficient by design
- The lowest-cost model that meets your bar, reused context and idle GPUs released.
One request through the platform
- Your app
- Guardrails
- Retrieval
- Routing
- Model: your GPUs, or a hosted API if allowed
- Accuracy check
- Answer
One trace ID links every step, for audit and cost.
Our team takes AI from idea to production
Available today, on your infrastructure. Led by systems engineers with more than 20 years in distributed systems and regulated enterprise infrastructure.
Scope one use case
You get: Agreed accuracy, latency and cost targets
Prepare and adapt
You get: Grounded answers
Integrate in your environment
You get: AI inside your systems
Run and measure
You get: One accountable partner
Where we are today, and where we're heading
Available now
Our engineering team
Delivering AI use cases on your infrastructure today.
In development
OneInfer Private Cloud
Released in stages; you move onto each part as it ships.
The goal
One month to results
From install to measured results, once the platform is complete.
Week 1
Set up and baseline
Latency, cost and GPU use measured
Weeks 2–3
Test on real work
Quality and cost compared with your current APIs
Week 4
Report and roadmap
Report, blueprint, compliance pack, rollout plan
Frequently asked questions
Does any of our data leave our network?
No. Models, prompts and records are designed to stay in your VPC or data center.
Can we keep using OpenAI, Anthropic or Bedrock?
Yes, behind the same endpoint as your open models, only for the workloads you allow.
What do we need to run it?
Your Kubernetes (EKS, AKS, GKE, OpenShift or VMware), GPUs for open models, and access to the systems involved.
How is success measured, and who is accountable?
Against accuracy, latency and cost-per-task targets set at the start; we are accountable for all three.
Start with one use case
Our engineers take one priority use case to production on your infrastructure, adding each part of the platform as it ships.
You bring the use case, an executive sponsor, a technical lead and an environment.