Questions & answersCloud AI or local AI: what fits your company?
Both paths lead to AI. The difference is in data, costs and dependence, and usually the answer is: both, cleanly separated.
The data class decides. Confidential data, contracts, HR data and engineering documents speak for local AI on your own hardware. Non-critical tasks with few users can be handled faster and cheaper in the cloud, with a processing agreement and preferably in the EU. Most companies do best with a combination: local for the confidential, rented for the rest, in one shared interface.
When does local AI make sense?
When data is involved that must not or should not leave the building. When many users work regularly and running costs should become predictable. When customers or group policies dictate the location of processing. And when independence from a provider is a strategic goal.
When does cloud AI make sense?
For non-critical tasks with few users. For a quick start without your own infrastructure. For applications that need the very strongest models. And for companies without an IT team that could look after a server. With a processing agreement and a provider in the EU, much can be handled defensibly.
Prerequisites for the decision
A data classification. Without it, every discussion about cloud or local is a gut decision. Four classes from public to strictly confidential, worked out in a few hours, give every use case its operating path. Plus an honest estimate of the number of users and a look at your own IT capacity.
Options: three operating paths
- Local. Your own server or appliance, open models, everything in-house. Full control, one-off costs, own operations.
- European cloud. Rented compute with an EU location and a processing agreement, often with open models. Less operations, data with a third party in the EU.
- Provider cloud. ChatGPT, Claude, Gemini and others. Strongest models, running costs, data with the provider, often outside the EU.
Benefits of the combination
Most companies do not need the one answer but a rule. Confidential runs locally, non-critical may go to the cloud, and a shared interface decides by data class which model answers. That keeps access to strong cloud models without confidential data leaving the building.
Limits and risks
Local means own operations: updates, backups, hardware. Cloud means dependence: prices, models and interfaces change without your input. A combination needs discipline so the rule is followed, and an interface that enforces it technically.
Example
A machine builder classified its data into four classes. Engineering data and contracts stayed local on a server with a GPU. Marketing texts and general research could run through a connected cloud model. Both ran in the same interface, employees chose the model by a simple rule stored in the permissions concept. The data protection officer supported the solution because the rule was documented.
Frequently asked
Can cloud AI be used in a GDPR-compliant way?
With a data processing agreement, a provider with an EU location or suitable guarantees and a clear rule on which data may go in, yes. For strictly confidential data the hurdle remains high, and many companies choose local there.
Is local AI worse than cloud AI?
On par for everyday tasks, slightly behind the largest cloud models for complex reasoning. For confidential data the difference is secondary because the cloud is not an option there anyway.
What about a European cloud?
A good middle way: rented compute with an EU location and a processing agreement, often with open models. Data sovereignty is better than with providers outside the EU, but the data still sits with a third party.
How can both be combined?
Through an interface such as Open WebUI that connects local and external models side by side. The data classification defines which tasks take which route. Employees notice little of the difference.
What does vendor lock-in mean for AI?
Being tied to a provider whose models, prices and interfaces can change. Anyone who builds processes on a single cloud model carries that risk. Open models and open software keep the switch open.
Conclusion
Cloud or local is not a matter of faith but of data class. Confidential locally, non-critical rented, both in one interface with a clear rule. Anyone who starts that way does not have to commit and can expand either path later.
Related questions: How can sensitive data be processed with AI?, What does a local enterprise AI cost?, Secure AI environments in Europe, what matters in 2026
Local AI for business
Language models on your own hardware, GDPR-compliant and without vendor lock-in. Options, comparison with the cloud and the path to a pilot.
To the solution: local AI