Private ChatGPT for businesses: how to run your own language model
Learn how to deploy a private ChatGPT within your company without sending data to the cloud, staying GDPR-compliant and keeping full control over your AI.
Private ChatGPT for businesses: how to run your own language model
More and more companies use ChatGPT to boost productivity: drafting emails, summarising documents, preparing reports. But there's a problem that many IT managers and executives are becoming aware of: everything you type into ChatGPT is sent to OpenAI servers in the United States.
For a business that handles client contracts, an accounting firm with tax data, or a law firm with privileged information, that's simply not acceptable. The solution exists, works today, and is more accessible than you'd think: a private ChatGPT deployed on your own infrastructure.
What exactly is a "private ChatGPT"?
A private ChatGPT is a language model (LLM) that runs on your servers — in your office, on a dedicated server, or in a private European cloud — and is only accessible to your team. Nobody else sees your conversations. No data leaves your organisation.
Technically, you're not using OpenAI models but open-source models such as Llama 3 (Meta), Mistral, Gemma (Google) or Qwen (Alibaba). These models have comparable quality to GPT-3.5/4 for many everyday tasks and can run locally on reasonable hardware.
The user interface can be identical to ChatGPT's: chat box, history, file upload. The end user barely notices a difference. But under the hood, everything happens inside your company.
Why Spanish companies are moving to private AI
1. GDPR and AI Act compliance
The General Data Protection Regulation requires that personal data of European citizens be processed with sufficient guarantees. Sending data to a US provider involves international transfers requiring specific contractual clauses that can be challenged.
With on-premise AI, data never leaves Europe. Full stop.
The European AI Act, in force since 2025, adds audit and explainability requirements for high-risk AI systems. With a model deployed on your own servers, you can meet those requirements with much more control than with a third-party API.
2. Real confidentiality, not just contractual
OpenAI's terms of service have changed several times. Although they promise not to use your data for model training, the history of breaches on SaaS platforms creates legitimate uncertainty.
With private AI, confidentiality doesn't depend on a contract — it depends on the architecture. Data physically cannot leave.
3. Deep customisation
With your own model you can:
- Connect the AI to your internal systems (CRM, ERP, client database)
- Train on your own documents so it responds according to your processes and terminology
- Control the tone and restrictions of responses
- Audit every conversation that happens in your company
None of this is possible with the standard version of ChatGPT.
What your company needs for private AI
Option 1: On-premise on your own servers
If you have your own servers with GPUs, this is the most private option. Modern models like Llama 3.1 8B run well on a consumer GPU (RTX 4080 or similar). For production with many users, more powerful GPUs are needed (A100, H100).
When it makes sense: law firms, notaries, clinics, industrial companies with sensitive data.
Option 2: Dedicated server in a Spanish data centre
If you don't have your own infrastructure, you can rent a dedicated server with GPU in a data centre in Madrid or Barcelona. Data stays in Spain, but you don't have to maintain hardware.
When it makes sense: SMEs without their own IT department, growing companies.
Option 3: European private cloud (VPC)
Some clients prefer deploying in a Virtual Private Cloud in AWS Frankfurt or Azure Europe, ensuring data doesn't leave the EU. It's a compromise between cloud flexibility and GDPR compliance.
When it makes sense: cloud-first companies that need to scale quickly.
The typical technology stack
For a typical deployment at a Spanish company we use:
- Base model: Llama 3.1 (8B or 70B depending on needs) or Mistral
- Inference engine: Ollama or vLLM
- Chat interface: Open WebUI (identical to ChatGPT in user experience)
- RAG (Retrieval-Augmented Generation): so the AI answers based on your internal documents
- Authentication: integration with your company's Active Directory / Azure AD
The result is a platform the team adopts in hours because they already know how to use it.
How much does having your own ChatGPT cost?
The investment depends on user volume and use case, but as a rough guide:
- Dedicated server with GPU (rental): from €300-800/month
- Initial setup and configuration: typical project of 2-6 weeks
- Maintenance: minimal once properly configured
Compared to the cost of ChatGPT Teams licences ($25/user/month) for 20 people — that's $500/month with no control over the data. With private AI, the monthly cost is similar but you have full ownership of the solution.
Most common use cases in Spanish companies
Intelligent document management: upload agreements, contracts or internal regulations and ask the AI directly. The response includes exact citations from the document.
Assistant for sales teams: connected to the CRM, the salesperson asks for a client's history and receives a summary in seconds.
Communication automation: email drafts, responses to frequent clients, internal translations.
Internal technical support: the technician asks the AI about a machine manual or incident resolution procedure.
How to get started
The first step is defining the priority use case: what task would the AI handle that your team currently does manually. With that clear, the technical design is straightforward.
At Galileo Studio we have deployed private AI solutions for companies in Madrid and other Spanish cities. The typical process takes 3-4 weeks from kick-off to production pilot.
If you want to explore whether private AI fits your company, we can have a 30-minute diagnostic call with no commitment.