On-premise or cloud AI? A practical decision guide
Where your AI runs decides who sees your data, what it costs and how fast you can ship. Five questions settle most cases.
By the Maple Crew team · · 5 min read
Every AI project reaches the same fork early on: do we call a hosted model, or do we run one ourselves? The question is often framed as a matter of principle. In practice it is an engineering trade-off, and five questions settle most cases.
1. Can the data leave your premises?
Start here, because the answer can end the discussion. Patient records, banking data and some personnel files are covered by rules that restrict where they are processed and by whom. If your regulator, your contracts or your own policy say the data stays inside, the model comes to the data. If hosted processing is allowed under conditions, write those conditions down before choosing a provider: region, retention, and whether prompts are used for training.
2. How hard is the task?
The largest hosted models are still ahead on long, open-ended reasoning. Open-weight models that fit on a single server are now strong at bounded tasks: classifying documents, extracting fields, summarising a file, answering questions over a known corpus. Most business workloads are bounded tasks. Test your real cases on both before assuming you need the biggest model.
3. What does the volume look like?
Hosted models are billed per token, so cost follows usage. A server is a fixed cost that is wasted when idle and cheap when busy. Low or unpredictable volume favours the cloud. A steady, high volume of similar requests favours your own hardware, sometimes within the first year.
4. Who will run it?
An on-premise model is a production system: drivers, updates, monitoring, capacity. If nobody owns that work, the system degrades quietly. Either budget for the operations or pick a managed service. This is the question teams skip most often.
5. How fast do you need to move?
A hosted model is available this afternoon. Hardware has lead times. A common and sensible path is to prototype in the cloud with synthetic or anonymised data, prove the value, then move the production workload on premise.
Keep the door open
Whatever you choose, do not let the choice leak into your whole codebase. Put the model behind one interface, keep prompts and evaluation cases in version control, and measure quality with your own test set. Then switching provider, or moving from cloud to on premise, is a configuration change followed by a test run. That portability is worth more than any single model decision, because the models will change again next year.