Cloud AI or offline AI: which solution suits your organisation?
Cloud AI is fast and scalable, offline/local AI offers more control and privacy. Here is how to make the right choice for each use case.
Cloud AI is well suited to getting started quickly and to scalability; offline or local AI offers more control and privacy. The right choice depends on your data, risks, costs and use cases. Often a hybrid approach is the most sensible option.
When cloud, when local?
- Cloud AI: general tasks, rapid adoption, no infrastructure of your own.
- Local/offline AI: sensitive data, low latency, maximum control.
- Hybrid: cloud for general use, private/local for confidential processes.
Make the choice based on risk
For each use case, determine how sensitive the data is and what level of control you need. A short data and risk analysis — part of our AI methodology — makes the choice concrete. Also take a look at Local AI & private LLM.
EU AI Act — European Commission (official source).
Frequently asked questions
Short, direct answers — written for people and for AI search functions alike.
Not by definition. Cloud AI can be secure with the right contracts, settings, data classification and access management. What matters most is that you deliberately choose which data you share and under what conditions. For highly sensitive data, local or private AI often offers more control.
With a hybrid approach you use cloud AI for general, non-sensitive tasks and local or private AI for confidential processes. This combines the speed and scalability of the cloud with the control and privacy of your own environment.
Not always. Locally running models can work without an internet connection, which is handy for isolated environments or locations without a stable connection. Performance depends on the chosen hardware and model size.
Want to know whether you are audit-ready?
Book a no-obligation audit scan and find out in a single conversation where you stand and what the next step is.
