What is on-premise AI?

How on-premise AI works

For an on-premise setup, a company downloads a model with openly available weights – so-called open-weight models – and runs it on its own servers. In addition to the model, an inference server, a user interface, user management, a knowledge base with embeddings and monitoring are needed. Powerful language models require graphics processors (GPUs) with a lot of memory; the larger the model and the more users work in parallel, the more hardware is required.

Advantages

• Full control: data does not leave your own infrastructure, which can be decisive for very sensitive data or contractual requirements.
• Independence: no dependency on the availability or price changes of an external provider.
• Offline operation: possible in isolated networks without internet access.

Disadvantages and effort

• Investment: GPU servers are expensive and are often under-utilised outside peak times.
• Model quality: the most powerful models of large providers are generally not available as open weights; locally operable models are often smaller.
• Operating effort: updates, security patches, new model versions, monitoring and backups require specialist staff.
• Security remains your task: on-premise is only as secure as your own operations – access control, patch management and logging included.
• Speed of innovation: new functions such as agents, connectors or multimodal models have to be integrated by yourself.

On-premise, private cloud or EU cloud?

Between local operation and a global cloud service there are intermediate forms: a private cloud with a hosting partner or a cloud service whose infrastructure and model regions are in the EU. For many medium-sized companies, a hosted service in the EU with a data processing agreement, transparent sub-processors and permissions concept is a practical middle ground. What matters is not the location of the server alone, but the overall picture of contracts, technical measures and data flows – see GDPR-compliant AI.

Questions for the decision

1. Which data must never leave your own infrastructure – and why?
2. Do you have staff who can operate GPU servers and models permanently?
3. Which model quality do your use cases require?
4. How many users will work with the system in parallel?
5. Would an EU-hosted service with a data processing agreement meet your requirements?

On-premise and AIPE

AIPE is not an on-premise product. AIPE is operated as a hosted service, primarily at Hetzner in Germany; published model providers run in EU regions, such as Azure OpenAI (Sweden Central), Amazon Bedrock (Frankfurt), Google Vertex (europe-west1) and Mistral (EU). The data processing agreement lists all sub-processors. What runs locally is the AIPE Worker: your own Mac or Windows computers can carry out tasks with files, terminal, browser and programs. This is local execution, not a local model – the model plans in AIPE. More on the overall picture in the guide GDPR and AI.