AI comparison
Local model vs API
A local model keeps data and versions under your control at the cost of hardware and operations; a hosted API gives larger models with no servers, charged per token.
| Aspect | Local model | Hosted API |
|---|---|---|
| Where data goes | Stays on your machines | Sent to the provider |
| Cost shape | Hardware and power, whether used or not | Per token, only when used |
| Model size | Limited by your memory | Up to the largest available |
| Version changes | Only when you change it | When the provider updates or retires a model |
Choose Local model when
- Data must not leave your network
- Usage is steady and high enough to keep hardware busy
- You need a fixed version for a long time
Choose Hosted API when
- You need the strongest models
- Usage is low or uneven
- You do not want to run inference servers
Learn it hands-on
These free lessons teach it with commands to run in a lab and checked tasks. Read them whole with no account.
Related terms
- Local model
- A local model is a language model that runs on hardware you control, a laptop, a server or your own cloud machine, rather than behind someone else's API.
- Open weights
- An open-weights model is one whose trained parameters are published for download, so you can run, inspect and fine-tune it on your own machines.
- Quantisation
- Quantisation stores a model's weights with fewer bits, such as 4 instead of 16, so it needs less memory and runs faster with a small loss of quality.