Guide · Private AI in Switzerland
Keeping control over data, models and access.
Private AI means AI deployed on infrastructure you control, rather than a direct call to a public service. Here is what the term covers, when it is justified and when it is not.
- Options
- Public API, private or hybrid cloud, on-premise.
- Criteria
- Data sensitivity, volume, operations, budget.
Private AI is deployed locally, on your servers or in a dedicated private cloud. If the project requires it, your data does not pass through any uncontrolled third-party service, and access is defined explicitly.
- Models installed on controlled infrastructure
- Internal document search (RAG)
- Role-based permissions and audit log
- Workflows run within the same perimeter
Cloud, private or local?
The right level depends on your constraints, not on a principle.
| Mode | When it makes sense | Trade-offs |
|---|---|---|
| Public API | Non-sensitive data, quick need, limited budget | Data sent to an external provider |
| Private or hybrid cloud | Sensitive data, limited internal infrastructure | Dependence on a provider, contract to be checked |
| On-premise or local | Strict confidentiality, data residency requirements | Hardware and operations to plan for |
Use cases
Where private AI makes a real difference.
Confidential documents
Contracts, HR files or customer data queried without leaving your perimeter.
Regulated sectors
Accounting firms, healthcare, legal: confidentiality constraints point towards a private deployment.
Internal assistants
A document assistant for your procedures and documentation.
Sensitive automations
A workflow that handles critical data, run in a controlled environment.
Frequently asked questions
Private AI, without promises of automatic compliance.
Is a local AI automatically compliant with the Swiss FADP?
No. No architecture is compliant by default: compliance depends on the data processed, the measures in place and the use. The FDPIC has confirmed that the FADP, which is worded in a technology-neutral way, applies directly to AI-based data processing.
Do we need a dedicated GPU server?
Not necessarily. Sizing depends on the model, the volume and the expected response time. We scope it before recommending any hardware.
Does an SME really need a local model?
Not always. If the data is not sensitive, a well-governed external provider may be enough. Going local is justified by a real constraint of confidentiality, data residency or cost at high volume.
Can we move from cloud to local later?
Yes. A well-designed hybrid architecture allows this gradual transition, without rebuilding everything.
Private AI
A specific confidentiality constraint?
Describe it. We will propose the simplest architecture that meets it.