On-prem AI model: when it makes sense and when it doesn’t

Three ways to deploy AI

The AI infrastructure decision isn’t binary. In practice there are three options, chosen according to data sensitivity and compliance requirements:

1. Cloud AI services - calls to the provider’s AI infrastructure, typically with an EU region, a data processing agreement and clear rules for data retention and use.
2. Private cloud in the Czech Republic or in the client’s infrastructure - the AI service runs in an isolated instance with a Czech provider or in the client’s existing cloud account.
3. On-prem / local model - an open model, or one we fine-tune, deployed directly at the client, possibly in a hybrid setup (part of the processing local, part in the cloud).

Each option has its place. The myth that “local = more secure = better” doesn’t hold - local has its drawbacks too, and they are rarely discussed.

When cloud AI makes sense

For most business processes, the cloud is the right choice:

• Fast deployment. No hardware investment; you start on existing infrastructure.
• Low operating costs. You pay for actual usage, with no hardware investment.
• Top model quality. The most powerful frontier models are usually available as a cloud service before they are available to run locally.
• Scalability. Volume grows tenfold and the infrastructure copes without any action from you.

If the data you send to AI is commercially sensitive but not secret - typically supplier invoices, certificates, standards, routine business correspondence - and you have a data processing agreement in place, the cloud is the most rational choice. Not only technically, but economically too.

When a private cloud makes sense

A private cloud (an isolated instance with a Czech provider or in the client’s infrastructure) is a good middle ground for companies where:

• Internal IT policy doesn’t allow data processing outside the client’s environment.
• Compliance requires Czech jurisdiction, or at least an EU region with strict controls.
• The company wants integration with its corporate IAM, logging and auditing according to its own rules.
• Volume is high enough to justify dedicated capacity.

This option keeps most of the cloud’s advantages (model quality, scaling), but gives more control over data flow. For regulated sectors (banks, insurers, large manufacturing groups) it is often the compromise that gets through internal IT approval.

When on-prem makes sense

A local model in your infrastructure makes sense under specific conditions. All of them should apply at the same time:

1. Data must not leave the company

Medical records, legal files, state secrets, highly confidential industrial know-how. With this data, the question isn’t whether the cloud provider “offers solid assurances” - internal or regulatory rules simply won’t let the data out.

2. You want to train the model on your own data

This is one of the strongest reasons to go on-prem. A generic model doesn’t know your abbreviations, your code lists, your habits. A local or dedicated model can be tuned on your data - turning it into a specialist for a specific job. On the task it handles every day, it can then be more practical than a general model without domain context.

Both the data and the model’s operation stay under your control. Operator corrections can be used as an evaluation set or a training signal, provided this is consistent with data rights and internal rules. Quality can then grow in a controlled way, not at random.

3. Volume justifies the investment

On-prem requires hardware infrastructure, integration, operation and maintenance. Below a certain volume (say, hundreds of documents a day) it is more expensive and more complicated than the cloud. Above that volume the picture can reverse - a smaller specialised local model can be cheaper to run than repeated calls to a large cloud model.

4. You have the expertise, or a partner who will take care of it

An on-prem model can’t be deployed and forgotten. It needs monitoring, periodic refreshes (data drift), versioning and an evaluation pipeline. If no internal team or external partner is responsible for this, the project falls apart over time.

The compliance paradox

Here’s an interesting thing that surprises companies: in some regulated sectors, a local model can be not just acceptable, but operationally better suited than the cloud.

The reason: healthcare, finance or law often have internal, contractual or regulatory restrictions on passing data to third parties. They may therefore choose a local model, tune it on internal data and deploy it on-prem. The result:

• Data doesn’t leave the company.
• Knowledge from corrections and evaluation data stays under the company’s control.
• Accuracy on a specific task can exceed a general model without domain context - in our certificate test, the fine-tuned model beat cloud AI.
• Operational know-how is a company asset, not just a setting in someone else’s platform.

In these sectors, on-prem doesn’t have to be a compromise - it can be a competitive advantage.

Common myths

“On-prem is expensive.” It depends on the time horizon. Hardware and deployment cost more up front. But at sufficient volume the infrastructure typically pays for itself within months - and can then run at lower unit costs than cloud calls.

“Cloud models are always better.” On generic tasks, often yes. On a narrowly defined task with a good evaluation set, a specialised solution can win. The key question isn’t just “is the model smarter”, but also “does it know my context”.

“You have to pick one path.” No. Hybrid deployment is common - sensitive documents stay local, general tasks (translation, summarisation) run in the cloud. The workflow simply routes each request where it belongs.

How to decide

Instead of “cloud vs. on-prem”, ask:

1. What are the most sensitive documents in this process? If they can’t leave the company, you have half the decision answered.
2. Do we want to turn our own data into a competitive advantage? If so, on-prem with fine-tuning makes strategic sense.
3. What is the annual volume of processed documents? Below a few thousand a year → cloud. Above tens of thousands a month → dedicated capacity starts to make sense.
4. Do we have internal capacity or a partner for maintenance? Without this, on-prem gradually degrades.

For the companies we work with, we most often end up with a hybrid deployment: the workflow runs in a private cloud in the Czech Republic, sensitive steps use a local model, and general tasks call a cloud service. We design the architecture only after analysing the data and requirements, not as a default product. Details are on the Local models page.

Key takeaways

On-prem isn’t automatically better or worse than the cloud. It’s a tool for specific scenarios - and where it fits, it can be cheaper, more accurate and strategically stronger than a generic cloud solution.

You don’t make the key decision when you choose a provider. You make it when you describe what data, in what volume, with what sensitivity and for what purpose you want to process. The option follows from that. Not the other way round.