On-premise AI · sovereign deployment
On-premise AI, without the marketing.
The model runs on your hardware, the data stays where it is produced. That solves real problems and creates others. Here is both sides, for an industrial deployment.
What on-premise AI actually means
The model runs on hardware you control — a server in your plant, an edge device next to the line, or your own datacenter — instead of a vendor's cloud. Data is processed where it is produced and never crosses your network boundary. It is not the same as a private cloud: in a private cloud the infrastructure is still someone else's, just isolated. On-premise means the machine is yours and the inference happens inside your facility.
Cloud versus on-premise, line by line
| What you are deciding | Cloud | On-premise |
|---|---|---|
| Where the data goes | Leaves the site. If servers sit in another country, that is an international transfer you have to justify under most data protection regimes. | Stays inside the facility. There is no transfer to document, and no third-party processor in the chain. |
| Response time | Round trip to a datacenter. Fine for a report, too slow for a reject decision at line speed. | Milliseconds, on the same network segment as the machine. |
| Cost shape | Low to start, recurring forever. Streaming continuous video is often more expensive than the inference itself. | Hardware up front, then flat. The cost does not grow with how much you look at. |
| Model control | The vendor can deprecate, reprice or change the model behind the same endpoint. | The version you validated is the version that keeps running, until you decide otherwise. |
| When the link drops | Inference stops. The plant keeps producing, but blind. | Keeps running. Connectivity affects reporting, not operation. |
When the cloud is the right answer
Three cases where on-premise is the wrong choice, and pretending otherwise would waste your time.
Bursty or unpredictable workloads
If demand swings by an order of magnitude, paying for peak hardware that idles most of the month is worse than renting it.
No operations team
On-premise means someone patches, monitors and replaces hardware. Without that capability the cloud is genuinely simpler and safer.
Frontier-model capability
For open-ended reasoning and language, the largest cloud models remain ahead of anything you can run locally. For narrow industrial tasks the gap closes or reverses — a small model trained on your line beats a general one.
Where on-premise clearly wins
Not a philosophy. Four situations where the arithmetic decides it.
- The decision must happen in milliseconds, next to the machine.
- Cameras capture people, which makes the footage personal data under most regimes.
- Bandwidth to send raw signal off-site costs more than the processing.
- The plant must keep operating when the connection does not.
Run it on one line before deciding anything
We deploy on an edge device connected to sources you already have and you see the output from a real shift. No migration, no commitment.