When a company decides to adopt generative artificial intelligence, the first technical question that arises is not which model to choose, but where that model is going to run. The decision between deploying AI on your own infrastructure (on-premise or private cloud) or consuming it as a service from the public cloud has direct implications for cost, data privacy, regulatory compliance and time to deployment. There is no universal answer: it depends on your sector, the type of data the AI processes and your usage volume. This article breaks down the key factors so you can make that decision with confidence.
What "on-premise AI" really means
The term on-premise in the AI context refers to running the language model — or another AI model — on servers that your organisation physically controls, or in a private cloud (private cloud in your own datacentre or in an exclusive colocation facility). Input data and responses never leave the perimeter you manage.
The most common options in 2025–2026 are:
- Open models deployed locally: Llama 3 (Meta), Mistral, Gemma 2 (Google DeepMind) or Qwen 2.5, run with Ollama, vLLM or LM Studio on your own GPUs.
- Dedicated appliances: Pre-configured servers from NVIDIA (DGX, HGX) or solutions from Dell, HPE and Lenovo with A100/H100 GPUs optimised for inference.
- Contracted private cloud: Dedicated (non-shared) GPU instances at providers such as Hetzner, OVHcloud or AWS Outposts where the infrastructure is logically exclusive.
In all cases, the common denominator is that data never crosses a public API or enters the provider's training systems.
What deploying AI in the public cloud means
The alternative is to consume the model through a provider's API: OpenAI, Google Vertex AI, AWS Bedrock, Azure OpenAI Service or Mistral AI Platform are the most common in Europe. The model runs on the provider's servers; you send the prompt and receive the response. The advantage is obvious: no own infrastructure, no GPUs, no maintenance. But data travels outside your environment.
Between the two extremes there is a range of intermediate options: deployments on European sovereign clouds (GAIA-X, Scaleway, T-Systems), contractual data-processing agreements with hyperscale providers guaranteeing that data is not used for retraining (such as the OpenAI Enterprise Data Processing Addendum or Azure OpenAI with a "no training on customer data" policy), and hybrid architectures where part of the processing happens locally and part in the cloud.
Head-to-head comparison: on-premise vs cloud
| Dimension | On-premise / Sovereign AI | Public cloud (external API) |
|---|---|---|
| Data control | Total. Data never leaves the perimeter. | Partial. Depends on the DPA contract and the provider's policy. |
| Initial cost | High (GPU hardware, licences, installation). | Low or zero (pay-per-use from the first token). |
| Cost at scale | Low per inference once hardware is amortised. | Grows linearly with the volume of tokens processed. |
| Latency | Very low if the GPU is on the local network. | Variable depending on region and provider saturation. |
| Model quality | Limited to open models (Llama 3, Mistral…); somewhat below the leading proprietary models. | Access to the most powerful models (GPT-4o, Gemini 1.5 Pro, Claude 3.5…). |
| Time to production | Weeks or months (procurement, installation, integration). | Days or even hours. |
| Maintenance | Internal responsibility or that of the consultant. | Managed by the provider (updates, scaling). |
| GDPR compliance | Simpler; data under your direct control. | Requires Standard Contractual Clauses (SCCs) if the provider is outside the EEA. |
| AI Act (Regulation EU 2024/1689) | Greater autonomy to manage the model lifecycle. | Shared responsibility; the provider assumes part of the compliance as a "general-purpose AI model provider". |
| Customisation / fine-tuning | Full freedom over the base model. | Limited to the options the provider enables (OpenAI fine-tuning, Azure…). |
The regulatory framework that should guide the decision
GDPR and international data transfers
The General Data Protection Regulation (GDPR) — EU Regulation 2016/679 — requires that any processing of personal data comply with the principles of data minimisation, purpose limitation and lawfulness. When you send a prompt containing personal data (a customer's name, medical history, email address) to a US-based provider's API, you are carrying out an international data transfer that requires an adequate legal basis: the EU–US Privacy Framework (Data Privacy Framework, approved in July 2023 by the European Commission via Adequacy Decision 2023/1795), the Standard Contractual Clauses (SCCs) updated in 2021, or binding corporate rules.
If the data is particularly sensitive — clinical records, employment files, financial data subject to banking secrecy — an on-premise solution eliminates the transfer risk at the root and greatly simplifies the GDPR audit.
AI Act: the European AI Regulation
The EU Regulation 2024/1689 on Artificial Intelligence (AI Act), in force since August 2024, classifies AI systems by risk level. High-risk systems — applications in HR, credit, access to essential services, critical infrastructure — must meet requirements for traceability, human oversight and technical documentation. If you run the model in your own infrastructure, you have full control over inference logs, audit trails and the data chain of custody, making it easier to demonstrate compliance in an audit. In the cloud, that traceability depends on what the provider exposes in its consoles.
For regulated sectors (healthcare, finance, defence), an on-premise or European sovereign cloud architecture is often the only one that withstands scrutiny from the Data Protection Officer or sectoral regulator. If you need guidance on how your system fits within the AI Act, our colleagues at Summum Consultoría — AI Act handle the legal compliance and governance side.
Additional sector-specific regulations
Beyond GDPR and the AI Act, sector-specific regulations often settle the debate without room for interpretation:
- Financial sector: The DORA Regulation (EU Regulation 2022/2554), applicable since January 2025, requires financial entities to document and manage the risk of their ICT providers, including cloud AI providers.
- Healthcare sector: The European Health Data Space Regulation (EHDS, currently being finalised) will introduce additional restrictions on the processing of clinical data outside the EEA.
- Public administrations and their suppliers: The Spanish National Security Framework (ENS, Royal Decree 311/2022) requires that ICT systems supporting public services be hosted within infrastructures offering equivalent guarantees; in practice, this pushes towards sovereign cloud or certified private cloud.
When to choose on-premise (sovereign AI)
The on-premise solution is the right choice when one or more of the following factors apply:
- Particularly sensitive data: medical records, professional secrecy (lawyers, auditors), criminal records, data relating to minors.
- High and stable inference volume: if you consistently process tens of thousands of requests per day, the per-token cost of cloud APIs can exceed the amortisation cost of dedicated hardware within months.
- Very low latency requirement: real-time applications (voice analysis on calls, computer vision on production lines) where network latency to an external datacentre is unacceptable.
- Deep model customisation: if you need continuous fine-tuning with proprietary data or want to adapt the model architecture, on-premise offers total freedom.
- Air-gapped environments: industrial facilities, defence or critical infrastructure with no internet connection.
- Client or contract requirement: public procurement specifications, contracts with large corporations or audit requirements that explicitly prohibit external processing.
If your organisation fits this profile, at Summum IA we design and deploy sovereign on-premise AI architectures: from model selection to integration with your internal systems, including security hardening and AI Act documentation.
When to choose the public cloud
The cloud has decisive advantages in different scenarios:
- Pilot projects or proof of concept: starting with a cloud API allows you to validate the use case in days, without any hardware investment.
- Variable or seasonal demand: if your request volume fluctuates significantly (campaigns, seasonal peaks), the cloud scales automatically and the cost adjusts to actual usage.
- Access to frontier models: if model quality is critical — for example, complex legal drafting, contract analysis, advanced code generation — the frontier models offered by public APIs still outperform the best open models on cognitively demanding tasks.
- Non-sensitive data: if the prompt contains no personal data or confidential information (queries about public catalogues, news summaries, translation of generic corporate texts), the cloud is perfectly valid under GDPR with the appropriate contractual safeguards.
- Small technical team: managing on-premise GPU infrastructure requires specialist MLOps engineering; if you do not have it, the cloud dramatically reduces the operational burden.
The middle ground: RAG with local data and a cloud model
A very common architecture for SMEs that want the best of both worlds is Retrieval-Augmented Generation (RAG) with data segmentation. The scheme works as follows: proprietary documents (manuals, contracts, knowledge base) remain indexed in a vector store inside your infrastructure; at query time, the system retrieves the relevant chunks locally and injects them into the prompt, which then travels to the cloud provider's API. The external model never accesses your document base directly; it only processes the context you select.
This architecture does not entirely eliminate the transfer risk — the selected chunks do leave for the provider — but it minimises it and allows you to use high-quality cloud models for reasoning without exposing the full corpus. Combined with a robust DPA agreement, it is the solution that most European companies are adopting in 2025–2026.
Real cost: the calculation you must do before deciding
One of the most common mistakes is comparing cloud costs with on-premise hardware costs without considering all the factors. The correct calculation includes:
- On-premise: GPU hardware (an NVIDIA H100 80 GB was priced at around €25,000–€35,000 in 2025), compute server, storage, networking, UPS, rack, electricity, cooling, maintenance staff, inference software licences, model updates, security.
- Cloud: cost per token (for example, GPT-4o charges approximately $2.50/M input tokens and $10/M output tokens in 2025; Mistral Large around $2/M input; Llama 3 70B on Bedrock around $0.27/M input), plus network costs, context storage and any orchestration licences.
The practical rule we apply in consultancy projects: above 500 million tokens processed monthly on a stable basis, on-premise typically pays for itself in under 18 months. Below that figure, the cloud is more economically efficient unless data restrictions dictate otherwise.
Frequently asked questions
Can I use ChatGPT with customer data without violating GDPR?
It depends on the plan and the contract. The free and standard paid versions of ChatGPT use conversations to improve OpenAI's models, which may constitute a data transfer. ChatGPT Enterprise and the OpenAI API with a signed Data Processing Addendum guarantee that customer data is not used for retraining and allow you to choose the processing region (including Europe). Even so, since OpenAI is a US company, the international data transfer rules apply, requiring the DPF framework or SCCs. If the data is particularly sensitive, consult your DPO before activating any integration.
Which open models are best suited for on-premise deployment in 2026?
As of June 2026, the most widely used open-source models in enterprise on-premise deployments are Llama 3.1 (70B and 405B) by Meta, Mistral Large 2 and Qwen 2.5 72B by Alibaba Cloud (Apache 2.0 licence for commercial use). For coding tasks, DeepSeek Coder V2 is highly competitive. The choice depends on the primary language (Qwen 2.5 has better multilingual support for Spanish), the size of the available GPU and the specific task. At Summum IA we evaluate and test models before recommending one for each use case.
Can an SME afford on-premise AI?
With mid-range hardware it is already possible. An NVIDIA RTX 4090 GPU (around €2,000) or an A10G (around €4,000 second-hand) is sufficient to run models of 7B to 13B parameters at 4-bit quantisation with reasonable quality. Larger models (70B+) require greater investment, but the ecosystem of quantisation tools (GGUF, GPTQ, AWQ) has greatly democratised access. The key is to size the solution to the use case: not every SME needs a 70B model. For many document automation or internal support use cases, a well-tuned 8B model is sufficient.
How does the AI Act affect on-premise deployment?
The AI Act classifies systems by risk, not by their deployment architecture. This means that a high-risk on-premise system has the same documentation, traceability and human oversight obligations as a cloud-based one. What changes is operational responsibility: if you use a provider's API, the provider assumes part of the obligations as a "general-purpose AI model provider" (GPAI) under Article 51 of the AI Act; if you deploy your own model, you are the provider and the responsible party. This adds compliance burden, but also gives you full control over the required technical documentation. Our AI Act technical compliance team can help you structure that documentation regardless of the deployment model you choose.