Private vs Public LLM: A Decision Framework
The choice between a private (self-hosted) and a public (API) LLM comes down to one question: how sensitive is the data you will feed it? Public LLMs are convenient and broad, but they process your data on someone else’s infrastructure. Private LLMs keep data inside your perimeter, at the cost of running it yourself. This framework helps you decide, dimension by dimension.
The core difference
A public LLM is reached over an API: your prompt and any attached data travel to the provider’s cloud, get processed, and a response comes back. A private LLM runs on infrastructure you control — on-premise or in a sovereign cloud — so the data stays inside your environment. Almost everything else (jurisdiction, auditability, cost profile) follows from that one architectural fact.
Private vs public LLM, at a glance
| Dimension | Public / API LLM | Private / self-hosted LLM |
|---|---|---|
| Where data is processed | Provider’s cloud | Your infrastructure |
| Data egress | Yes | None, if isolated |
| Training on your data | Possible unless excluded | No |
| Jurisdiction | Provider’s, incl. foreign law | Yours (e.g. EU) |
| Auditability | Limited to what the provider exposes | Full — you hold the logs |
| Setup effort | Low — call an API | Higher — you run it, or use a turnkey workspace |
| Best for | Non-sensitive, general tasks | Regulated, personal or confidential data |
A five-question decision framework
- 1. Is the data regulated, personal, or confidential? If yes, lean private. Client financials, patient records, case files and personal data rarely belong in a public endpoint.
- 2. Which regimes apply? GDPR, the EU AI Act, DORA or NIS2 all reward the control and auditability a private deployment makes easier.
- 3. Is cross-border transfer a risk? Sending personal data to a non-EU provider can trigger GDPR Chapter V transfer obligations. Private processing avoids the question.
- 4. Do you need to evidence what happened? If an auditor or regulator may ask, you want the logs and citations a private, controlled system can produce.
- 5. What are the volume, latency and cost profiles? High, steady volumes can favour self-hosting; spiky, low volumes can favour an API. This is the one dimension where public often wins.
When public is fine — and when it isn’t
Public LLMs are a reasonable default for non-sensitive, general-purpose work: drafting public-facing copy, brainstorming, summarising already-public material. They become hard to defend the moment regulated or confidential data is involved — which, for teams in finance, insurance, healthcare, law and the public sector, is most of the real work.
The middle path: a turnkey sovereign workspace
The usual objection to private LLMs is effort — someone has to run the models. A turnkey sovereign workspace removes that: Diana runs open models inside your own perimeter and gives your team a finished product — chat, projects, agents and cited documents — with the data control of a private deployment and little of the build. It runs on-premise, in a sovereign EU cloud, or air-gapped, never trains on your data, and keeps a tamper-evident audit trail. For most regulated teams, that is the practical way to get private-LLM control without a private-LLM project. See the architecture.
Frequently asked questions
Diana is the sovereign AI workspace for regulated European teams — specialist agents produce finished, cited documents inside your own perimeter.