On-Premises LLMs in the Enterprise: When Is On-Premises Deployment Worth It?

For a long time, the conventional wisdom was that anyone wanting to use top-tier language models had to go to the cloud. That’s no longer the case. Over the past two years, openly available models have drastically narrowed the performance gap with proprietary top-tier models—from years to just a few months. At the same time, operating costs per token have fallen, quantization techniques enable high-performance models to run on off-the-shelf hardware, and regulatory pressure (EU AI Act, GDPR, NIS2, DORA) is increasing the value of data sovereignty.

For companies, this means the question is no longer necessarily “on-premises or cloud,” but rather “which workload belongs where.” This article describes the problem, contextualizes technical developments and the regulatory framework, compares operating models, and provides a decision-making matrix for workload allocation. Four practical scenarios and specific recommendations for action round out the discussion.

Generative AI has long since found its way into many areas of business—from software development to document processing and knowledge management. These applications are often based on cloud models that are integrated via application programming interfaces (APIs). This approach allows for a quick start but also creates dependencies.

Among other things, companies must clarify how sensitive data is processed, how ongoing AI costs will evolve, and how stable the technical foundation will remain in the long term. Changes to models, prices, or terms of use can affect existing applications. In regulated industries, additional requirements regarding data protection, information security, and traceability come into play.

On-premises language models offer an alternative here: they can be run within a company’s own infrastructure or in controlled operating environments. This allows for more targeted control over data flows and access rights. However, local operation does not automatically mean greater security or GDPR compliance—architecture, permissions, and specific implementation remain critical factors.

There is no one-size-fits-all answer when deciding between on-premises language models and cloud-based AI. The choice depends on the tasks a model is intended to perform and the requirements regarding performance, cost, and data privacy.

For clearly defined tasks such as summarizing, classifying, or extracting information, on-premises models can be a suitable solution. Processing internal documents using Retrieval-Augmented Generation (RAG) can also be a relevant use case when data needs to be processed in as controlled a manner as possible.

For complex inferences, sophisticated tool usage, or specialized knowledge requirements, powerful cloud models can still offer advantages. Furthermore, requirements vary depending on query volume, response time, and available infrastructure.

Therefore, the key question is no longer simply “local or cloud?” but rather: Which AI workloads should be processed locally, which in the cloud, and which in a hybrid architecture?

An on-premises language model is not automatically less expensive than a cloud service. While cloud solutions often incur usage-based costs, on-premises operation requires investments in hardware, infrastructure, maintenance, and personnel. Whether these costs pay off depends, among other things, on utilization rates and actual usage volume.

Model quality should also be evaluated based on specific tasks. General benchmark results are not sufficient to determine whether a model meets a company’s requirements. Representative tests using realistic data and clearly defined quality criteria provide a better basis for decision-making.

In addition, there are security and compliance requirements. Companies should examine what data is processed, how access is regulated, what logging is required, and how model changes can be controlled. Especially for business-critical applications, a structured evaluation is more important than simply choosing a specific technology.

Instead of completely replacing the existing AI infrastructure, a phased approach is recommended. Companies can start by identifying suitable use cases, testing local models with representative tasks, and comparing the results with existing cloud solutions.

In doing so, technical performance, total cost of ownership, data protection, and operational overhead should be considered together. Based on this analysis, a decision can be made as to whether a local, cloud-based, or hybrid operating model best meets the respective requirements.

What operating models are available? How can total costs be realistically compared? And what criteria can companies use to decide which workloads should be run on-premises or in the cloud?

Our white paper examines the technical and regulatory developments surrounding on-premises language models, compares different operating models, and uses four real-world scenarios to illustrate the factors that are critical to the selection process. It also provides concrete recommendations for evaluating and implementing on-premises LLMs within your organization.

Want to know if local language models fit your AI strategy? This white paper helps you systematically evaluate opportunities, limitations, and prerequisites so you can make informed decisions about your AI architecture.

Download the full Local LLMs white paper here.

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