What is on-premise AI?
On-premise AI is an AI system operated entirely on an organisation's own hardware or in its own data centre – rather than at a cloud provider. Requests, documents and the language model never leave your own network. It is the most far-reaching form of data sovereignty: no third-country transfer, no provider access, and no dependence on whether an external service stays available.
How on-premise differs from cloud
With a classic cloud AI service you send your question to the provider's servers. The model runs there, the answer is produced there – so your content is processed outside your infrastructure. On-premise reverses this: the model runs at your place, and the data stays where it is.
A third variant is often offered: operation in a European data centre. That is considerably better than a US cloud, because the data location is in the EU. But the difference from on-premise remains: processing still takes place at a service provider, not in your own house. Which variant is right depends on protection needs, IT resources and compliance requirements.
Technically, on-premise AI needs computing power – for language models usually GPUs – plus operation, updates and monitoring. In return you get full control: you decide which model runs, when it is changed and who has access.
What on-premise AI is used for
Neither prompts nor documents are transmitted to third parties – relevant for confidentiality-bound professions and special categories of data.
Without external API calls, the CLOUD Act and third-country transfer question disappears entirely.
You determine which language model is used and when it is updated or replaced.
Price, licence or API changes at an external service do not affect your operation.
The assistant can work directly with internal data sources inside a closed environment.
Instead of usage-based billing, there are capital and operating costs for your own hardware.
On-premise is no guarantee of data protection or security – it only moves the responsibility into your own house. Access rights, network security, backups and updates are yours to run properly. And on-premise requires hardware and IT resources. For many organisations a dedicated German data centre is the more pragmatic middle ground between data sovereignty and operational effort.
Frequently asked questions
Does on-premise AI need expensive hardware?
It needs computing power, usually GPUs. The specific requirement depends on the model used and the number of users – smaller open models run considerably more economically than very large ones. What makes sense in your case is best clarified in a conversation.
Is on-premise safer than cloud?
It offers more control, not automatically more security. Protection depends on how well your own environment is secured and operated. The decisive advantage is data sovereignty: no third party can reach the processing.
What is the difference from a private cloud?
With a private cloud, a service provider operates a dedicated environment reserved for you alone. The data sits separately from other customers, but the processing still happens at the provider. On-premise means: operated in your own house.
Can you switch between on-premise and cloud later?
With KOSMO, yes – both operating models are available. What matters for that flexibility are open, interchangeable models and the absence of proprietary formats, so that no vendor lock-in arises.
What this looks like with KOSMO
Theory is one thing – in 30 minutes we show you live how KOSMO does this in your organisation. With your own content.
