On-premises AI for businesses means that language models run entirely within the company’s own IT infrastructure: on its own servers, on-premises or in a private cloud, without any connection to external cloud services. Sensitive data never leaves the company network, and the system continues to operate even without an internet connection.
For organisations with stringent compliance requirements, this is often the only way to make productive use of artificial intelligence at all. We implement local LLMs in an isolated, auditable manner, integrated into your existing systems.
For compliance reasons, we are not allowed to use cloud services for sensitive data.
Our offline AI runs entirely locally. Your data stays where it is generated.
We want to use AI, but our company works with confidential information.
We implement local LLMs on-premises or in your private cloud: secure, isolated and auditable.
We need AI that works without internet access, for example for internal systems or critical infrastructure.
Our offline models run independently and are fully functional even without an external connection.
Why you can trust us
Data sovereignty in own infrastructure
Data leakage during local operation
Availability - even without Internet access
System for secure, local AI processing
Target group
Large organisations, public authorities and regulated sectors
Markets
Germany, Austria, Switzerland
Technological basis
local LLM models, container deployment, API frameworks, GPU servers, in-house AI stack
Increasing data protection requirements, sensitive data, restricted use of the cloud: this is precisely why our offline AI was developed. The solution enables high-performance large language models to be run on local or isolated infrastructure, with absolutely no reliance on the cloud. Organisations retain full control over their data whilst still benefiting from the performance of modern AI models.
The difference becomes apparent in day-to-day operations: when a department feeds a document into a cloud-based language model, the content leaves your network – along with all the contractual data, personal data or design knowledge it contains. With a locally operated LLM, this step is completely eliminated. There is no external interface through which company data could leak, and no dependence on an external provider’s terms of use.
If you want to use artificial intelligence without compromising on security and compliance, please get in touch with us to discuss your local AI architecture.
Most AI systems run in the cloud: they’re readily available, but come with trade-offs in terms of data privacy and control. With a local LLM, your entire AI infrastructure remains within your own network. Data, models and results never leave your organisation.
Local large language models run entirely within your environment, with no data transfer to third parties and no cloud storage. Cloud AI processes data on the provider’s infrastructure.
The systems operate independently of internet connections and are fully functional even in isolated networks. Cloud AI cannot be used without an internet connection.
Updates, maintenance and monitoring are carried out exclusively by your IT department – no third-party access, no external APIs. With cloud models, the provider determines the update cycles.
The model is tailored to your data and remains within your organisation. In the cloud, customisation is only possible within the scope of the provider’s options.
Full performance through GPU-optimised deployment, directly within your hardware infrastructure. With Cloud AI, performance depends on utilisation and the pricing plan.
On-premises AI is not a substitute for the cloud, but rather an option for certain data categories. We recommend basing the decision not on technological preference, but on data classification.
You are working with data which, in accordance with internal policy or regulatory requirements, must not leave the company network
Your systems are operated in isolated networks – for example, in critical infrastructure or in production environments with no external connection
You need auditable evidence of exactly where data has been processed
You want costs that can be planned for the long term to cover sustained high usage, rather than usage-based billing
the data to be processed is non-critical and speed of implementation is key
You need very large models with highly fluctuating workloads and do not wish to set up your own GPU infrastructure
You are already working extensively in an Azure environment and would like to integrate AI into it
As both approaches have their merits, we implement both: alongside on-premises models, we also deploy generative AI within your Azure infrastructure. In many projects, the end result is a hybrid approach – non-critical use cases in the cloud, and sensitive processing on-premises. What we do not do is recommend an architecture before we are familiar with your data environment.
Implementation
We review existing systems, networks and security policies to determine the optimum environment for your Local LLM.
We select the appropriate LLM – for example, open-source, fine-tuned or proprietary – and design the architecture for your deployment.
The solution is installed locally or in your private cloud, customised to your hardware and put into operation.
Your existing applications and data sources are connected to seamlessly embed AI into your processes.
We support you with ongoing operation, regular updates and the optimisation of your model.
Technology used
Docker / Kubernetes (containerised deployment)
NVIDIA CUDA / GPU optimisation
Linux / Windows Server
Microsoft 365 / Teams / Dynamics optional
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On-premises AI refers to the operation of large language models within a company’s own IT infrastructure, without the use of external cloud services. All data remains entirely within the company and is processed locally. This enables the use of AI even in environments with stringent requirements regarding data protection, security and control.
A local LLM offers complete data sovereignty when operated locally, as no data is transferred to external providers. The AI can be used independently of an internet connection and can be tailored specifically to internal requirements. This makes on-premises AI particularly suitable for organisations that prioritise data protection, compliance and control without having to forego modern AI capabilities.
On-premises AI is suitable for organisations handling sensitive data or data requiring special protection. These include, amongst others, public authorities, financial institutions, and companies in the healthcare sector or regulated industries. Companies with strict internal security policies also benefit from running AI models on-premises.
Modern local LLMs can achieve performance comparable to that of cloud-based models in many use cases. In particular, when operated on GPUs and trained specifically on a company’s own data, results can be achieved that are suitable for productive AI applications. For very large models and highly fluctuating workloads, a cloud environment may offer advantages.
Requirements depend on the model chosen and the number of concurrent users. We utilise GPU-optimised deployment and scale the infrastructure – right up to a GPU cluster with a load balancer – as part of the requirements analysis. The solution can be operated both on your own on-premises servers and in a private cloud.
Yes. In practice, this is often the most sensible approach: non-sensitive use cases in the cloud, and sensitive processing on-premises. We implement both approaches and base our recommendation on your data classification, rather than on a fixed technological decision.
Talk to us about your local AI architecture. Get a no-obligation consultation now.
