Pay-per-token API
Your data leaves the building on every call, and the bills compound with use. You rent a capability you never own.
CONTROL PLANE / PRIVATE AI
Run modern AI on your hardware, keep your data
Your GPUs, your network, no per-token bills. Serve open models behind your own walls.
- No tokensFlat cost on hardware you already own.
- No egressInference stays inside your network.
- Open modelsRun current open weights. Swap them freely.
The status quo
Three ways to run AI, each one costs you
Today you pick what to give up: your data, your weekends, or your wallet.
Build it yourself
Months of platform work before it's production-ready — HA, multi-tenancy, metering, a UI. And it all lives in one engineer's head.
Closed GPU SaaS
Locked into one vendor's closed stack, on someone else's hardware. Still off-prem, still not yours.
There is a fourth option. It runs on your servers and gives you the control plane the other three leave out.
The resolution
OpenModelControl resolves the trade-off, on your terms
A control plane that runs on your servers. Deploy open models to your GPUs, route inference, and keep everything inside your network.
Private & sovereign
Runs on your servers and deploys to your own GPUs. Prompts and weights never leave your network. No egress, no per-token bills.
Production-ready
High availability, autoscaling, load balancing, and failover. Built to serve real traffic, not a demo.
Operator-friendly
A dashboard, one-click deploy, RBAC, and an audit trail. Run it without standing up a platform team.
How it works
One control plane, from client to GPU
Clients hit one URL; the load balancers route straight to a worker. The control plane stays out of the request path — it schedules models, programs the balancer, and handles failover — across workers in any region or datacenter. You own the apps and the hardware; OpenModelControl manages everything that serves them.
Clients
Call one endpoint
Apps, SDKs, and existing OpenAI clients hit one URL. Change a single line.
Load balancers
Route to workers
An HA pair sends each request straight to a worker replica, with health checks. No single point of failure.
Control plane
Out of band
Not in the request path. It schedules models onto workers, programs the balancer, and watches health.
Workers
Run the engines
One agent per worker runs the engines and reports health. Put workers in any region or datacenter.
GPUs
Where it runs
Models execute on your GPUs, quota'd per tenant. Nothing leaves the network.
Access patterns
Direct, balanced, gated, or regional
Connect straight to workers
Skip the balancer tier. Trusted clients reach workers directly, for the fewest hops and the lowest latency.
Pool behind load balancers
Put an HA pair in front of one shared pool. Requests spread across every worker, with health checks and failover.
Gatekeep access per tenant
Let the balancers enforce access — route each tenant or key to only the workers it is allowed to reach.
Control GPUs across regions
Run workers in any region, area, or datacenter from one control plane. Keep regulated data in-region and serve from the nearest site.
What it does
Operate the whole stack
Deploy models, schedule GPUs, balance traffic, and meter every tenant. The work that turns raw hardware into served inference.
Control dashboard
One console for the whole cluster: models, GPUs, tenants, and traffic.
One-click deploy
Push a model to your GPUs in a single action. No YAML archaeology.
Load balancing
Spread inference across replicas. Slow or failing workers drop out of rotation.
Live GPU monitoring
Per-GPU VRAM, utilization, temperature, and throughput in real time.
Auto-failover
A worker drops and requests reroute on their own. No pager goes off.
Load testing
Drive synthetic traffic to size capacity before you commit to it.
Audit trail + RBAC
Every action logged. Roles, keys, and quotas scoped per tenant.
Chat playground
Test any deployed model in the browser. No client to wire up first.
Who it's for
Built for teams that must keep data in-house
Three kinds of operator: enterprises that cannot let data leave the building, data centers reselling AI access, and homelabs running open models on their own gear.
Banking
Keep models and customer data inside the bank's network. Meet residency rules without a cloud exception.
Healthcare
PHI never leaves your premises. Serve clinical and back-office AI on-site.
Government
Sovereign by default. Air-gap capable, with full control over every weight.
Legal
Privileged documents stay privileged. No third-party logging, no training on your matters.
Manufacturing
Run inference next to the line. Low latency, no dependence on a public API.
Universities
Share one GPU cluster across labs, with per-group quotas and metering.
DATA CENTERS · RESELLERS
Resell AI access on your own hardware
Multi-tenant by design. Meter inference, bill usage per client, and offer modern open models to your customers — without building a control plane yourself.
HOMELABS · SELF-HOSTERS
Run it at home on the open core
One consumer GPU under the desk is enough to start. The open core is free to run — the same deploy, routing, and monitoring the big clusters use.
Honest comparison
Where OpenModelControl fits
Three approaches to the same job. The difference is how much you build and run yourself.
| Capability | OpenModelControl | Public API | DIY on Kubernetes |
|---|---|---|---|
| Data stays on-prem | |||
| No per-token bills | |||
| Run open models | |||
| HA & failover, out of the box | |||
| Dashboard & one-click deploy | |||
| Live GPU monitoring | |||
| Multi-tenant metering | |||
| RBAC & audit trail | |||
| No vendor lock-in |
You bring the GPUs and run the hardware. We run the scheduler, registry, tenants, and metering above them.
Your hardware
Deploys to the GPUs you already own
From a homelab on a single GPU to a data-center rack. Inference stays on your silicon, not someone else's.
Data center
H100 / H200 / A100. Full VRAM for large models and high concurrency.
Workstation
RTX 6000 Ada / 4090. Local inference under your desk.
Consumer
RTX 3090 / 4070 and similar. Budget cards serve quantized models.
Start small
One GPU, from 8 GB VRAM for quantized models. Larger models want 24 GB+; add workers as load grows.
Mix NVIDIA generations in one cluster.
Get started
Run modern AI on your hardware
Talk to our solutions team: a 30-minute scoping call, and a proof-of-concept on your own GPUs, typically within a week of access.
Open core. No lock-in, no per-token bills.
Want to resell, distribute, or integrate OpenModelControl? Partner with us — partner@openmodelcontrol.com