Local vs cloud
When does local AI make sense for a business?
Local AI makes sense when your team uses AI regularly, wants control over business data, or needs less dependence on a remote provider. The model still has to do the job well at the speed your team needs.
What changes when AI runs on your own server?
With local inference, the model generates its answers on hardware at your premises. Your staff can reach it through a browser or a terminal over your network. They do not need a powerful GPU in every laptop: the server does the model computation.
That gives you a different set of choices. You decide which model versions are available, who can use them and when to install updates. In return, the machine needs power, maintenance and enough capacity for the people using it. Owning a server changes who operates the service; it does not remove that work.
Start with work your team already does
A useful starting point is recurring work: finding information in internal documents, drafting customer replies, preparing product descriptions, explaining code or transforming structured data. These jobs can build up substantial usage across a team, even when each individual request looks small.
Count completed work, not just tokens. If one model needs several attempts and extensive corrections, a lower inference cost may not produce a cheaper result. Compare representative documents and tasks, the time to a usable answer, and the amount of review each answer needs. Local models can make mistakes, just as hosted models can.
Control matters as much as the monthly bill
Keeping an internal model close to your data can simplify decisions about where prompts and files are processed. It also lets you keep a tested model available while you evaluate its replacement. A provider changing its model lineup does not automatically change the model installed on your server.
The configuration matters. A locally hosted chat page can still send requests to a cloud model. Document processing, search and other integrations can have their own external connections. Map those connections before describing the complete service as local.
Plan for an internet outage and a hardware outage
A fully local setup can keep serving people on the local network during an internet outage, provided the model, required files, sign-in service and other dependencies are available locally. External search, hosted applications and remote staff access may still need an internet connection.
The server and network still need power. One machine is also one point of failure. Decide how long the business can wait for a repair, what should be backed up and whether spare capacity is justified. Local AI offers a different continuity plan, not automatic uptime.
Choose around the workload
Local AI is particularly worth examining for steady use, private internal knowledge and teams that want direct control over their infrastructure. A cloud service can remain a sensible choice for occasional requests, sudden demand spikes or a particular model capability you cannot reproduce locally.
You can use both deliberately. Route appropriate internal work to your server and keep an approved cloud option for other tasks. Establish which data may use which service, then size the local machine against actual demand. A clear boundary is more useful than choosing every future tool at once.
Sources & further reading
Official documentation behind the technical details in this guide.