On-premise AI for SMEs in Spain: complete guide 2026
Everything a Spanish SME needs to know before installing artificial intelligence on its own servers: requirements, costs, and real use cases.
On-premise AI for SMEs in Spain: complete guide 2026
For years, artificial intelligence was the exclusive domain of large corporations with million-euro budgets and data science teams. That changed in 2023 with the arrival of open-source language models, and in 2026 it no longer makes sense for a Spanish SME to ignore this technology.
But does it make sense for a 20 or 50-person company to install AI on its own servers? Isn't it easier to pay a ChatGPT subscription? In this guide we answer those questions with concrete data.
What "on-premise AI" means
"On-premise" literally means "on the premises": the software runs on hardware you control, whether in your office, on a rented server, or in a private cloud. The opposite is "cloud" or SaaS, where the provider manages the infrastructure.
In the context of artificial intelligence, on-premise AI means the language model (LLM) — the brain that processes text and generates responses — is installed on your servers. Your data never leaves your environment.
Why a Spanish SME should consider on-premise AI
Protection of business data
A typical SME handles sensitive data it wouldn't want on third-party servers: client data, commercial proposals, projects in development, financial information. Using ChatGPT or Copilot with that data implies blind trust in the provider's terms of service.
With on-premise AI, protection is architectural. Data cannot leave because there is no connection to the outside.
GDPR and AI Act: real compliance
The General Data Protection Regulation applies to any company that processes data of European citizens, regardless of size. Sending that data to US providers requires specific guarantees that in practice many SMEs don't have formalised.
The European AI Act, fully in force since 2025, adds transparency and human oversight requirements for AI systems. These requirements are much easier to meet when you control the infrastructure.
Predictable long-term cost
SaaS AI services have per-user/month pricing that scales with growth. With on-premise AI, the main cost is the initial investment (hardware or rented server) and maintenance. Past the break-even point — usually between 6 and 18 months — your own AI is cheaper.
Customisation: the AI learns your business
Generic models like ChatGPT don't know your company, your sector, or your terminology. With on-premise AI you can:
- Fine-tuning: adjust the model with your data so it "speaks" your language
- RAG (Retrieval-Augmented Generation): connect the AI to your internal documentation
- Integrations: connect with your CRM, ERP, or client database
Does my SME have the resources for this?
This is the question we get asked most. The short answer: it depends on the use case, but probably yes.
Hardware requirements
A model like Llama 3.1 with 8 billion parameters runs perfectly on a computer with a mid-range GPU (RTX 4080, 16 GB VRAM) and is more than sufficient for most office tasks: document summarisation, text generation, question assistant.
For companies with more concurrent users or larger models, professional GPUs are needed (A10, A100). Here it makes sense to rent a dedicated server in a Spanish data centre.
Personnel requirements
You don't need a team of data scientists. You need:
- Someone to manage the server (can be the same profile that manages your current web or servers)
- A technology partner for the initial deployment
At Galileo Studio we deliver the configured solution and train the team. Subsequent maintenance is minimal.
Real use cases for Spanish SMEs
Agencies and consultancies
Private AI helps generate proposals, status reports, market analyses, and meeting summaries. Connected to the CRM, it can generate personalised drafts for each client.
Accounting firms and tax advisors
Analysis of tax regulations, summaries of legislative changes, drafts of client communications. With AI connected to the regulatory database, the advisor asks and receives answers citing the specific article.
Industrial companies
Technically searchable manuals in natural language, maintenance procedures, technical training for new employees. The AI connected to plant documentation answers the technician's questions in real time.
Private health clinics
With clinical data within the perimeter, AI can help with clinical reports, patient history searches, and staff training. Compliance with health data protection regulations is total.
Law firms and notaries
Contract analysis, search in internal case law, preparation of briefs. The firm never sends privileged information to external servers.
Most popular open language models in 2026
The open-source model ecosystem has matured enormously:
- Llama 3.1 (Meta): the most versatile, available in 8B, 70B, and 405B parameters
- Mistral and Mixtral: excellent quality-speed ratio, very popular in Europe
- Gemma 2 (Google): optimised for consumer hardware
- Qwen2.5 (Alibaba): especially good at code tasks
- Phi-4 (Microsoft): designed for devices with limited resources
For most SMEs, Llama 3.1 8B or Mistral 7B are sufficient. Larger models (70B) are justified for more complex tasks or when quality is critical.
The deployment process
A typical deployment has these phases:
Week 1 - Diagnosis: we identify the priority use case, evaluate the existing infrastructure, and define the most suitable model.
Weeks 2-3 - Deployment: we install the model on the server, configure the user interface (usually Open WebUI or similar), integrate with existing systems.
Week 4 - Pilot: the team tests the solution with real cases. We adjust parameters, prompts, and permissions based on feedback.
Week 5 onwards - Production: knowledge transfer to the internal technical team, documentation, and support.
Frequently asked questions from SMEs
How many users can it handle simultaneously? Depends on hardware. With an A10 GPU and a 7B model, 5-10 concurrent users without issues. For more users, multiple instances can be deployed.
Does it work without internet access? Yes. Once installed, the model doesn't need internet to function. Ideal for environments with connectivity restrictions.
Can I use my own documents as a knowledge base? Yes, it's one of the most valuable use cases. The RAG technique allows the AI to answer questions based on your internal documents (PDFs, Word, databases).
Is it legal in Spain? Absolutely. It's exactly what the AEPD recommends: keeping data under your own control. The AI Act also favours this approach for high-risk systems.
Conclusion
On-premise AI for SMEs is not science fiction nor reserved for large companies. In 2026, with current open-source models and available hardware, any Spanish company with 10 or more employees can have its own artificial intelligence assistant running in weeks, GDPR-compliant and with no per-user cost.
The first step is identifying what task the AI would handle that currently consumes your team's time. From there, the path is short.