Start with the measurement, because it decides who this is for. Eurostat reported on 11 December 2025 that “In 2025, 20.0% of EU enterprises with 10 or more employees used artificial intelligence (AI) technologies”. The same release lists the bottom of the table: “At the other end were Romania (5.2%), Poland (8.4%) and Bulgaria (8.5%).”
Romania is last in the European Union. That is not only a gap. It means that most Romanian organisations are choosing their first AI architecture right now, with no legacy deployment to defend. The choice is open in a way it is not in Denmark, where Eurostat put the figure at 42.0 %.
What private AI actually means
Private AI is artificial intelligence that an organisation runs in an environment it controls: the infrastructure, the access and the path the data takes. Several architectures satisfy that. The model can run on servers the organisation owns. It can run on dedicated infrastructure in a private environment. It can run in a managed private environment with contractual and technical controls around the data.
The label on the product is not the test. The test is one question. Who controls the environment in which your data is processed?
A private deployment is not a different kind of artificial intelligence. It can run the same class of language model used everywhere else. What changes is where inference happens, and who owns the system around it. With a cloud service, the application sends the information out and receives a result. With a local deployment, the request can be answered inside the network it started in.
Head count does not decide sensitivity
Private AI is often described as enterprise technology. The description does not survive contact with the work. A law firm of fifteen people holds client matter files. A small accounting practice processes payroll and financial records. A software company holds its own source code. A retailer of five thousand people may hold less that matters.
The number of employees does not determine how sensitive the information is. The work does.
What private AI does not solve
A local model is not a secure model. OWASP's 2025 Top 10 for LLM applications lists the risks that belong to the application rather than to the hosting: prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption.
Every one of those ten applies to a model running in your own rack. Moving the inference inside the building removes a set of external dependencies. It does not remove authentication, access control, backups, monitoring, signed updates, or the need to treat an uploaded document as untrusted input.
NIST takes the same position at the level of the whole system. Its AI Risk Management Framework, released on 26 January 2023, is described as “intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems”. Design, development, use and evaluation. Not location.
Moving the model inside the building changes which risks the organisation owns. It does not reduce how many there are.
The five questions that settle it
- What information will employees actually put into the system?
- Who controls the infrastructure that processes it?
- Where may that information be stored, and for how long?
- What happens if the external provider changes its terms, its prices or its availability?
- Does the service have to keep working when the internet connection does not?
If the answers are comfortable, a cloud service is the cheaper and faster route, and it should be taken. Cloud infrastructure is genuinely better at low and variable usage, it needs no capital, and the largest models are there first.
If the answers are uncomfortable, the question is no longer which model is strongest. It is which architecture fits the data.
With Bastion
What Bastion changes
Bastion is a private AI system delivered as one sealed appliance that runs inside your building. One monthly fee covers the hardware, the model, the hardened operating system and support, and nothing your team types leaves the building.
Bastion is the private-AI architecture delivered as one system rather than as a project. Enterprise hardware, an open-weight model with a commercial licence, hardened software and a local API arrive configured. The appliance answers inside the customer's network and needs no outbound internet connection to do it.
That removes the assembly work, which for an organisation of twenty to a hundred people is most of the cost. It does not remove the security engineering above it, and this article says so on purpose.
Questions this article answers
- What is on-premise AI?
- AI that an organisation runs in an environment it controls: the infrastructure, the access and the path the data takes. The test is not the label on the product but one question, who controls the environment in which your data is processed.
- Is a local model more secure than a cloud model?
- Not on its own. OWASP's 2025 Top 10 for LLM applications lists ten risks that belong to the application rather than the hosting, from prompt injection to unbounded consumption, and every one of them applies to a model running in your own rack.
- How many EU companies actually use AI?
- Eurostat reported on 11 December 2025 that in 2025, 20.0% of EU enterprises with ten or more employees used AI technologies. Romania was last at 5.2%, which means most Romanian organisations are choosing a first AI architecture rather than replacing one.
On the record
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Read next
- What an on-premise AI appliance is, and what it saves you from buildingAn AI appliance is the decision not to become an AI infrastructure company. The eleven components a local deployment needs, the one measured example of how much a wrong choice costs, and what an appliance gives up in exchange for removing the assembly.Read the article
- Seven questions to answer before you decide where AI runsStart with the workload, not the hardware. What the system will read, whether the data has to leave, how steady the usage is, whether the internet is needed, how strong the model has to be, who operates it, and what happens when it is unavailable.Read the article