AI in the Cloud or on Dedicated Infrastructure: How to Weigh the Real Constraints
Compare delivery models based on data requirements, management, and the performance you need.
SqualiOnline editorial team · 2026-09-07
"I want a private AI" is almost always the answer to one specific concern: that company data will end up somewhere it shouldn't. It's a legitimate concern, but it isn't enough on its own to choose between a cloud service, dedicated infrastructure, and an on-premises server. The choice is made on written requirements, weighed against what each option demands in expertise, maintenance, and recurring costs. This guide is about building that list.
What you actually need to decide
"Where does the model live" is the consequence, not the starting question. There are four questions to ask first.
- What data the system will touch, and which of it can't leave your perimeter. Often it's a small part of the total: certain categories of documents, not the entire archive.
- What happens if the service is unavailable for half a day. If work grinds to a halt, the continuity requirement changes everything else.
- Who will maintain it, by name, over the next three years. It's the requirement that gets ignored most often, and it's the one that decides more than any other.
- How predictable the usage is. A steady load and a spiky one are paid for very differently.
The three options, and what they ask for in return
Simplifying things, there are three options. The table compares what each one requires, not what it promises.
| Cloud service | Dedicated infrastructure | On-premises server | |
|---|---|---|---|
| Where the data passes through | On third-party systems, per contract | On resources reserved for you, managed by a vendor | On your own network |
| Setup | Fast | Intermediate | Slow: purchase, installation, configuration |
| Cost | Tied to usage, variable | Mostly fixed | Upfront investment, plus management |
| Expertise required | Minimal, integration-level | Moderate, for managing the vendor | High and ongoing |
| Model updates | The vendor's, even when not requested | Agreed upon | Yours: if you don't do it, it stays frozen |
| Continuity | Depends on the vendor and the network | Agreed by contract | Depends on your own server room |
| Models available | Wide choice, which changes over time | Wide, within the vendor's offering | Limited by the available hardware |
No column is better in absolute terms: each one shifts work and risk to a different place. Cloud shifts it onto the contract, dedicated infrastructure onto the relationship with the vendor, an on-premises server onto you.
The expertise that stays with you
An on-premises server isn't just a computer left switched on. It's a set of activities someone has to carry out continuously, and they don't disappear when that person changes jobs.
- System and security updates, with a window in which they can be applied without stopping anyone.
- Backups of data and configurations, with at least one restore tested.
- Access management: who can use the system, who can change its behavior, who reads the logs.
- Replacing failed hardware, with the lead time that takes to source it.
- Updating the models, which isn't automatic and isn't painless: what used to work can behave differently, and needs to be retested.
If these activities fall to a person who already has twenty other things on their plate, the system will stay stuck at the version it was installed with. This isn't pessimism: it's the ordinary fate of servers forgotten in a closet.
Contracts and data flows
With an external service, the guarantees live in the contract and the technical documentation, not in the marketing pages. There are only a few things to verify in writing, but they're precise.
- Where the data is processed and stored, and whether that can change without notice.
- How long it's retained, and what happens when you close the service.
- Whether your content can be used to train models, and how to opt out.
- Who, on the vendor's side, can access it and under what circumstances.
- What gets logged about your usage, and for how long.
The chain also needs looking at: many services rely on other ones underneath. A vendor who can't tell you what they depend on isn't necessarily hiding anything from you, but they can't guarantee you anything either.
Testing on the same use case
The choice is settled with a test, not a comparison on paper. The condition is that it's the same task, with the same data and the same yardstick.
- Pick a real, well-defined task, with real documents, including the difficult ones.
- Prepare a set of examples for which you already know the right answer. Without this, the comparison stays an impression.
- Test every option on the same set, measuring the quality of the answers, response time, and behavior when the information isn't there.
- Estimate the cost on a realistic one-year volume, not the volume of the test: this is where the options really diverge.
- Add internal hours to the tally, both for the test and for future maintenance.
An honest test takes just a few weeks and can end with "none of the three, the problem isn't the model." That's just as useful a result as any other.
"On-premises" doesn't mean secure
Equating data kept in-house with data that's protected is the shortcut that leads to the wrong purchases.
The choice doesn't even have to be a single one. A common and sensible split keeps the most sensitive documents in-house, searched by a local system, and sends out the tasks that don't require confidential data. The cost of this approach is complexity: two systems to maintain and a rule about what can pass from one to the other. If the rule can't be written in three lines that whoever uses the system can understand, it will be worked around.
What this guide doesn't cover
This guide covers where to run a model-based system, starting from your constraints. The recurring cost items of an assistant already in operation — usage-based consumption, reviewing answers, updates — are covered separately, as is managing permissions, logs, and backups for a business management system, which is only touched on here.
Frequently asked questions
Does an open model installed in-house cost less?
It shifts the cost, it doesn't eliminate it: instead of usage fees, you pay for hardware, power, and above all the time of whoever maintains it. It becomes worthwhile with high, steady volumes and existing in-house expertise. With low, uneven volumes it's almost always more expensive.
Can we start in the cloud and move later?
Yes, if the system is built so the model can be swapped out. The cost of moving is almost never in the model itself: it's in the data, the integrations with your systems, and the checks that need to be redone on the answers.
How do you avoid company data ending up used for training?
On two fronts. The first is contractual: get it stated in writing whether your content is used for training and how to opt out. The second is practical: send the system only what the task needs, instead of entire archives.
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