Comparison · AI Infrastructure
On-premise or cloud AI: where to run it
Running AI agents on local servers or on cloud infrastructure: what actually changes for cost, data control and GDPR compliance.
In brief
For most Italian and European SMEs the right call is Cloud AI (models via API, no hardware to manage): faster to start, lower fixed costs, and GDPR-compliant if you use EU regions and a proper DPA. Choose On-premise AI (local, on your own servers) only in two cases: when a contractual or regulatory obligation forbids data from leaving your perimeter, or when inference volumes are so high and steady that cloud becomes more expensive than hardware over time. In practice many companies go hybrid: sensitive data local, generic processing in the cloud.
Option A
On-premise AI (local)
Models and AI agents run on servers inside your perimeter (corporate data center or dedicated hardware). Data never leaves the infrastructure you control.
Pros
- +Full data control: nothing leaves your perimeter
- +Useful when contracts or regulation forbid data leaving the company
- +Predictable cost at very high, steady volumes
- +No dependency on third-party connectivity for inference
Cons
- −High upfront investment in hardware (GPUs) and setup
- −Requires internal IT skills for management, updates and security
- −Open models you can host locally are often less capable than frontier cloud models
- −Scaling means buying more hardware, not raising a limit
Best for
- Companies with data under strict contractual or sector rules (healthcare, defense, public sector)
- Those with an existing structured IT department and data center
- Very high, continuous inference volumes
Option B
Cloud AI
Agents run on a cloud provider's infrastructure, with models accessed via API. You pay per use or a subscription, with no hardware to manage.
Pros
- +Fast start: no hardware to buy, first agent live in weeks
- +Access to the most capable current models, kept updated by the provider
- +Scale up and down with load, pay for what you use
- +GDPR-compliant with EU regions, an art. 28 DPA and no training on your data
Cons
- −Data passes through a third-party provider: you need a solid DPA and the right region
- −Usage-based cost that can grow if not monitored
- −Provider dependency and possible lock-in if you don't design for portability
- −Some clients or regulations may forbid processing outside your perimeter
Best for
- SMEs that want to start fast without investing in hardware
- Those needing the most capable, up-to-date models
- Variable or growing workloads
| Criterion | On-premise AI (local) | Cloud AI |
|---|---|---|
| Time to start | Weeks/months (hardware + setup) | A few weeks (first agent live in 4) |
| Upfront cost | High (GPUs, data center) | Low (no hardware) |
| Running cost | Predictable at high volumes | Usage-based, grows with use |
| Data control | Total, inside the perimeter | At the provider, with DPA and EU region |
| Model capability | Open models hostable locally | Frontier models, always updated |
| Scalability | Buy hardware | Elastic, on demand |
The verdict
This isn't an ideological choice but a matter of concrete constraints. If you have no contractual or regulatory obligation preventing data from leaving your perimeter, cloud with EU regions, an art. 28 DPA and no training on your data is almost always faster, cheaper and more capable: you start in weeks instead of months. On-premise makes sense when data is genuinely locked down (regulated sectors, clauses that forbid the outside) or when volumes are so high and steady that they amortize the hardware. In many cases the best answer is hybrid: keep sensitive data local and let the cloud handle generic processing.
FAQ
What people usually ask us.
Is cloud GDPR-compatible for an Italian SME?
When is on-premise actually worth it?
Can I start in cloud and move on-premise later?
Is a local model as capable as ChatGPT or Claude?
Not sure which one fits your case?
20 minutes with the CEO to work out the right choice for your processes. No pitch, no obligation.
