On-premise AI software development: control over code, models and delivery

“We need this on-premise” is rarely a technical requirement. Usually a worry sits behind it: where does our data go, and who can turn off the tap? Both questions can be answered – but only by breaking the development chain into layers that are decided independently.

1. The wrongly posed question

“On-premise or cloud” suggests one decision. In fact there are five – and in most organisations the answer differs per layer. Bringing everything in-house at once means paying for control you do not need in some places.

2. Five layers of control

The layers are independent. A common, well-justified split: source code and runtime in-house, the build on your own infrastructure, model use through a service with contractual assurances – or the other way round, depending on the data.

3. Operating models

• Public service. Least effort, greatest dependency. Usually unproblematic for public and non-critical content.
• Dedicated environment at the vendor. Contractual assurances on processing, storage location and use for training; no operations of your own required.
• Private cloud. Operated in your own cloud environment, control over network boundaries and access, but with operational effort.
• On-premise. Full control, full effort – hardware, operations, updates, availability.

4. Running models in-house

Running your own models is technically possible and right in certain constellations. What to plan for realistically:

• hardware with enough memory for the chosen model, including redundancy,
• operations and updates – models, runtime, drivers,
• performance differences compared with large hosted models, which can be substantial depending on the task,
• capacity planning for concurrent use,
• selection and assessment of the models, including licence terms.

A pragmatic middle path is splitting by task: simple, high-frequency tasks locally, demanding tasks through a service with contractual assurances – steered by the data classes from the governance policy.

5. Network boundaries and data flows

Whatever the operating model, it is worth mapping the actual data flows once. Experience says three points tend to surprise:

• Tool chains are longer than expected – editor, extension, telemetry, an assistant feature inside a third-party system.
• Agents read more than the task requires when their scope is not limited.
• Outbound connections are rarely restricted; an allow-list is more effective than any after-the-fact analysis.

6. Build and delivery

The delivery layer is often the easiest decision and still has a big effect: when build, registry and deployment run on your infrastructure, the path from source state to running application stays entirely under your control – regardless of where a model did its computing.

In practice: your own pipeline, your own registry, deployment into your own GitLab, Jenkins, on-premise, private cloud or Kubernetes. Details in the deep dive CI/CD for AI-assisted software development.

7. The cost side

Control costs, and the arithmetic should be honest. Account for hardware and depreciation, operational effort, on-call and availability, updates, and the effort of maintaining a performance level. Against that stand avoided risks, client requirements you can meet and the ability to take on projects that would otherwise be out of reach.

What matters is naming both sides. A decision to run things yourself that ignores the effort gets quietly unwound after two years.

8. A decision aid

Checklist: control per layer

Conclusion

Sovereignty in AI-assisted development does not come from running everything yourself but from deciding deliberately per layer – and being able to justify the decision. That is the more defensible position towards clients, auditors and your own management.

The layer with the best ratio of effort to control gained is usually delivery: when build and deployment run on your infrastructure, the path from source state to production is entirely in your hands.