For many people, using AI automatically means opening a website and sending a request somewhere else. That is only one way to run AI.

Modern AI models can also run on hardware located inside a business. A Local Private AI Server can process supported prompts, documents and workflows directly on the organization's own network.

This does not mean cloud AI is bad. Cloud platforms are convenient, require little hardware management and can provide access to very large models. But local AI has a different set of advantages.

• Business data can remain local for supported workloads.
• Local processing can avoid internet round trips.
• Supported AI can continue operating without an internet connection.
• There are no Thinkputer per-token charges for supported local processing.
• The organization controls its own hardware, models and storage.

There is also no reason a company must choose only one model. A business may run private or repetitive workloads locally and connect to cloud AI when a particular external model or service is useful.

Our comparison of Cloud vs VPS vs Local Private AI explains where each approach makes sense.

AI infrastructure is becoming a choice rather than an assumption. For many organizations, the best answer may eventually be a combination of local and cloud AI rather than relying completely on either one.