In brief
On-premises AI runs on hardware belonging to the organisation, branch or in-house management environment. Rather than sending all processing to an external AI service, you keep the most important components closer to your own infrastructure.
When should you choose this option?
On-premises AI makes sense when data is sensitive, systems need to continue operating locally, or when external platforms do not align with policy, costs or availability. It is particularly relevant for organisations with clear requirements regarding management and access.
What is the difference between this and cloud AI?
With cloud AI, processing largely runs on infrastructure provided by an external provider. With on-premises AI, more responsibility lies with the organisation itself or with a partner managing the local environment. You gain control, but must organise management effectively.
What components are included?
In addition to the model, youāll need hardware, software, access management, logging, updates, monitoring and support arrangements. On-premises AI is therefore not just about installing a model, but about building a workable environment.
Practical nuance
āOn-premiseā does not mean that nothing can ever be outsourced. Some organisations combine on-premise processing with selected external integrations. It is important to make a conscious decision about which parts remain on-premise and where external services are or are not appropriate.
From concept to practice
How AITJE applies this
For a dedicated server, we configure the model software, access and connection to your workplace as a single environment. We discuss who will manage the installation, how many people will be working on it simultaneously and what information will be processed. In this context, āon-premiseā refers to a choice of location, not an automatic guarantee of security. Back-ups, updates, network access and any external integrations are therefore also part of the setup.
Installation and setupFrom explanation to application
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