Deep Dive · Architecture

On-Premise AI

Many companies hear the same line when they want to introduce AI: "It only runs in the cloud anyway." Usually that isn't true. And when it is true, it's a statement about the provider's convenience, not about the reality of your data sovereignty requirements.

On-premise and EU-sovereign hybrid architectures are more practical for companies in the DACH region today than ever before: open-source models run on manageable hardware, dedicated EU hosting environments are contractually cleaner than corporate clouds, and the question "where does our data actually live" can be answered honestly — if you ask it honestly.

This page is deliberately not a technology catalog. It shows when on-premise is the right approach, when it would be overkill, and what a sound decision between cloud, hybrid and on-premise looks like in practice — with realistic cost logic, without hype and without ideology.

When on-premise genuinely makes sense

Makes sense

Sensitive data must not leave the building

Production data, formulas and recipes, patient data, client data, customer data with specific confidentiality obligations. As soon as a contract, an industry regulation or an internal policy mandates this unambiguously, on-premise is not an option — it's the requirement.

Makes sense

Data volume is high and stable

When large volumes of documents, images or sensor data are processed every day, a dedicated environment often pays off within two to three years against the variable costs of a per-request-priced cloud.

Makes sense

Latency is a hard constraint

In production, quality control or machine-adjacent applications, the delay of a cloud request often can't happen at all. An on-site edge setup is then not just faster — it's the only thing that's practical in the first place.

Less sensible

Doing everything on-premise "because cloud feels off"

An uneasy gut feeling is not a selection criterion. We advise against buying on-premise just to have something you can physically touch. For many SMEs, an EU-sovereign managed setup is significantly cheaper, easier to be accountable for, and closer to day-to-day operations.

Three architecture patterns we use in practice

01

EU-sovereign managed

Dedicated environment at an EU hosting provider (e.g. IONOS Cloud, Hetzner, OVHcloud, Stackit) with contractual commitments on data handling, access control and operations. Legally clean EU hosting, without you having to buy your own hardware or build an in-house operations team.

Typical stack: EU VM with dedicated GPU (NVIDIA L4 or A10), Mistral or Llama models in the 8B to 12B range, inference via vLLM or Ollama, storage in EU S3-compatible object storage.

02

On-premise edge

Open-source models run on manageable hardware directly on site. Sensitive data never leaves the building, latency is minimal. Particularly suited to machine-adjacent, latency-critical applications.

Typical hardware range: Workstation with RTX 4090 / RTX 6000 Ada for models up to roughly 14B parameters; server-class with NVIDIA A100 or H100 for 70B models and parallel load. Model families: Llama 3.1 / 3.3, Mistral Small / Large, Qwen 2.5, Phi 4. Inference stack: vLLM, Ollama or Llama.cpp depending on load and quantization requirements.

03

Hybrid

Sensitive steps (e.g. extraction from contracts, personnel data, patient records) run locally, generic steps (e.g. language polishing, translation) use vetted EU managed services. Every data flow is documented, every boundary is drawn deliberately and traceably.

Typical split: Data extraction and classification locally (Llama 3.1 8B or Mistral 7B), rephrasing and normalization via GDPR-compliant providers with a DPA in place. Clear data classification per data flow (e.g. ISO 27001 Annex A.5.12) and documented routing rules.

The components listed are typical building blocks, not a guarantee: which model class, GPU tier and inference layer fits depends on latency requirements, context length, load profile and existing licenses. We make the selection together with you — without ideology, without vendor lock-in.

What on-premise really costs

Upfront costs are almost always higher with on-premise

Hardware, network, operating environment, monitoring, staff. If you don't see these costs in a cloud invoice, you're comparing unevenly.

Variable costs are almost always lower with on-premise

No per-request fees, no surprise monthly invoices, no provider price increases. With stable load profiles, this becomes clearly noticeable over time.

The tipping point is usually between year two and three

Under three years of operation, genuine on-premise rarely pays off. Over three years with stable volumes, it almost always does. Decide based on an honest three-year calculation, not a one-year snapshot.

The most expensive option is almost always the ideological one

"We want everything on-premise because cloud is bad" is not a cost argument. Neither is "we're going to the cloud because on-premise is complicated." The right answer is a sober assessment per use case and per data flow.

Related pillars

On-premise is not a pillar of its own, but an architectural deep dive. It connects with the following pillars whenever data sovereignty moves to the forefront:

Pillar 1 · Strategy

AI consulting →

When the cloud-vs-on-premise question becomes part of the strategic roadmap.
Pillar 4 · Integration

ERP and CRM integration →

When existing systems stay in the building and still need AI capabilities.
Pillar 5 · Support

Managed AI operations →

When operating an on-premise system exceeds internal capacity.
Deep Dive · Governance

AI governance →

When data sovereignty becomes an operational governance question.
Guide

On-premise or cloud AI? →

An honest decision basis between on-premise, EU-managed and hybrid — with a quick-decision grid and a three-year calculation.

Frequently asked questions about on-premise AI

Is on-premise AI realistic for companies, or only for large corporations?
Realistic — but not sensible in every case. For growing companies, two approaches are practical today above all: genuine on-premise hosting for open-source models (on owned or rented hardware in an EU data center) and EU-sovereign managed clouds that are technically cloud but legally hosted cleanly within the EU. The choice between the two depends on process proximity, data types, load profiles and staff availability — not on a blanket "cloud vs. on-premise" ideology.
Is on-premise cheaper than cloud?
Almost never in the first twelve months, and often only after two to three years of operation once the load profile has stabilized. On-premise has high upfront costs (hardware, network, operations, staff) and lower variable costs. Cloud has low upfront costs and higher variable costs. The honest answer: calculate both scenarios over three years with a realistic staffing and energy assumption, and base the decision on a total-cost-of-ownership calculation, not a headline.
Do we have to buy our own hardware?
Not necessarily. Genuine on-premise hardware pays off when sensitive data truly must not leave the building. For many cases, a dedicated environment at an EU hosting provider with clear contractual commitments on data handling, access and operations is enough. We assess together with you which setup meets your requirements without forcing unnecessary investment.
Is EU hosting enough for GDPR compliance?
For most use cases at DACH companies: yes, provided the provider is a documented EU hosting provider, a data processing agreement (DPA) is in place, no data flows into non-EU third countries, and the typical GDPR obligations are properly addressed. Highly sensitive data (special categories under Art. 9 GDPR, professional secrecy obligations) can still force the move to on-premise or a private EU environment — but that is a special case, not the GDPR standard case. More in the On-Premise vs. Cloud guide.
When is hybrid the better choice over pure on-premise?
More often than many companies assume. When only individual data flows are highly sensitive and the rest of the process is generic in nature, hybrid is often the best answer both economically and legally: sensitive steps run locally, generic steps use vetted EU managed services. The precondition is clean data classification and documented routing per data flow. Hybrid is not "the comfortable middle ground" — it is the most demanding of the three architectures, because every boundary must be drawn deliberately.

Cloud, hybrid or on-premise — what fits your situation?

In a short on-premise analysis, we assess per use case which approach is right legally, technically and economically. Free of charge, no obligation, with an honest three-year calculation.

Request an on-premise analysis