Local and private
Cloud AI vs local AI: how to choose
Cloud models offer capability and no hardware; local models offer control and flat cost. The trade-offs that actually decide it, and why most teams use both.
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Capability
The largest and strongest models are hosted, and on genuinely hard reasoning the difference is real. If your work is dominated by the hardest kind of question, that matters more than anything else on this page.
For summarising, drafting, extraction, classification and routine code, the gap has narrowed enough that it is rarely the deciding factor.
Data
This is usually what actually decides it. A prompt sent to a hosted model is processed by that provider under their terms. For most content that is fine. For some — regulated records, unreleased work, third-party confidential material — it is not, and no contractual assurance is as strong as the data not leaving.
This is why the split is rarely all-or-nothing. It is usually decided per kind of data rather than per organisation.
Cost
Cloud is variable: no capital outlay, and a bill proportional to use that grows with success. Local is fixed: hardware up front, then near-zero marginal cost.
The crossover depends on volume. Occasional use is cheaper hosted. Heavy, steady, predictable use is usually cheaper local, and the break-even arrives sooner than people expect once usage is continuous.
Most teams end up with both
Local for sensitive and high-volume work, hosted for the hardest questions, and a routing policy deciding per request. That requires a system where the decision is explicit and auditable — otherwise "we keep sensitive things local" is an intention rather than a control.
Questions people ask
- Is local AI cheaper?
- At sustained volume, usually. At low or bursty volume, usually not — idle hardware costs money whether or not you use it.
- Can I start hosted and move later?
- Yes, and it is a sensible order: prove the workflow with hosted models, then move the parts whose volume or sensitivity justifies hardware. That is much easier on a platform that already supports both.
- Is hybrid complicated?
- It is if you build it yourself, because you are maintaining two paths. It is straightforward if the routing layer already treats local and hosted models as interchangeable destinations.
Try it rather than read about it
ClawAI treats local and cloud models as the same kind of destination, and its privacy-first and local-only modes make "sensitive work stays local" a setting rather than a habit.