Choosing between cloud AI and on-premise
When to use AI models through a cloud provider, when running them yourself makes sense, and the middle options worth considering.
The short answer
Using AI models through a cloud provider gives a business the most capable models, no infrastructure to run, and cost that scales with use. Running models on your own hardware gives control over where data goes, at the price of buying and maintaining that hardware, finding the expertise to run it, and accepting models that are usually weaker than the best cloud options. The real question for a small business is not cloud versus on-premise but which cloud arrangement: a provider that processes in the EU, contractually does not train on your data, limits retention, and signs a data processing agreement, configured so that sensitive data is minimised before it is sent. Self-hosted open models earn their place in specific cases: data that genuinely cannot leave your environment, residency requirements that exclude every acceptable cloud option, very high volumes of simple tasks where a small local model is cheaper, or systems that must work offline. Whichever you choose, design so the model can be swapped: the business logic, the prompts, the data flows and the evaluation are yours; the model behind them is a decision you can revisit.
Comparing the options
| Option | Model quality | Data control | Cost shape | Expertise needed | Fits when |
|---|---|---|---|---|---|
| Cloud provider, consumer terms | Best | Poor: training and retention possible | Cheap or free | None | Never for business data |
| Cloud provider, business terms, EU processing | Best | Good: no training, limited retention, processing agreement | Usage-based | Low | Most business use |
| Open model hosted by a European provider | Good | Good, with a clear contract | Usage or dedicated | Low | Residency emphasis; open-model preference |
| Private deployment in your own cloud account | Good | Very good: your account, your region | Reserved capacity | Medium | Sensitive data; higher volumes |
| Self-hosted on your own hardware | Adequate to good, improving | Complete | Hardware plus running costs | High | Data cannot leave; offline; very high simple volume |
| Hybrid: local for sensitive steps, cloud for the rest | Best of both, by step | Tailored | Mixed | Medium | Mixed sensitivity |
Deciding
- Classify the data each AI use touches: public, internal, personal, sensitive, contractually restricted.
- Minimise before anything else: send only the fields a task needs; pseudonymise where possible.
- For most data, choose a cloud provider with business terms, EU processing, no training, limited retention and a signed processing agreement.
- For restricted data, evaluate a private deployment or a self-hosted model, and cost the expertise honestly.
- Design for swapping: keep prompts, logic, evaluation and data flows in your own code; wrap the model behind an interface.
- Evaluate on your own tasks, not on benchmarks, before committing.
- Document the decision, the data classes and the terms, for your own governance and for client questionnaires.
Where this is heading
Open models keep improving and the cost of running them keeps falling, so the self-hosted option becomes more capable every year. Cloud providers keep adding regional processing and stronger contractual terms in response to European demand. The sensible position for a small business is to use the best acceptable cloud arrangement now, keep the architecture model-agnostic, and revisit yearly, moving specific workloads to private or local deployments when the case is clear.
What this means for you
Use cloud AI under business terms with EU processing, no training on your data, limited retention and a signed processing agreement for most work, after minimising what you send. Reserve private deployments and self-hosted models for data that genuinely cannot leave, residency cases, very high simple volumes or offline needs, and cost the expertise honestly. Keep the model swappable and revisit yearly, because the options are improving in both directions.
Frequently asked questions
Is it safe to send our data to a cloud AI provider?
It depends on the arrangement, not on the word cloud. A business-grade agreement with a provider that processes in the EU, does not train on your data, limits retention, and signs a data processing agreement is a defensible setup for most business data, comparable to the email and accounting services you already use. Pasting customer records into a consumer chat tool is not. The contract and the configuration decide.
When does running our own model make sense?
When data genuinely cannot leave your environment, for legal, contractual or risk reasons; when residency requirements exclude every acceptable cloud option; when you run very high volumes of simple tasks where a small model on your own hardware is cheaper; or when the system must work offline. Those cases are real and less common than the anxiety about cloud suggests, and each brings hardware, expertise and maintenance that a small business must be ready to carry.
What is the middle ground?
Several options: cloud providers with EU-region processing and strong contractual terms; open models hosted by a European provider in a dedicated environment; a private deployment of a model within your own cloud account; or a mix, where sensitive steps run on a self-hosted small model and general steps use a cloud model. A small business lands on the first, with the architecture designed so a move to the others is possible.
Sources
- EUR-Lex: Regulation (EU) 2016/679 (GDPR), Article 28 (processor) (accessed 2026-09-16)