ASDSHosted AI services are the easiest way to start. But for some businesses, running a model on a server you control is the better fit.
Where a local model makes sense
- Private data. Clinic notes, internal documents and customer records never leave your network.
- Steady, high volume. Summarizing or classifying thousands of items a day can cost less on your own hardware than per-request pricing.
- Offline or unreliable internet. The assistant keeps working when the connection does not.
Where the cloud still wins
- The hardest reasoning and writing tasks, where the largest hosted models are still ahead.
- Low volume. If you ask a few questions a day, pay per use.
- No one to maintain a server.
A hybrid is usually right
Route sensitive and repetitive work to a local model, and send the occasional hard task to a hosted one. Put a thin layer in front so your apps do not care which one answers.
Whatever you choose, measure it on your own real examples. A model that scores well on a public benchmark may still fail on your invoices, your language mix or your forms.