Why this choice matters #
When a business decides to adopt artificial intelligence, the first technical question is: where does the AI model run?
The answer has profound implications for:
- Costs: the initial investment and 3-5 year TCO can vary by 2-3x
- Privacy and GDPR: where data physically resides determines the applicable legal framework
- Performance: latency and response speed affect user adoption
- Scalability: the ability to grow with the company’s needs
- Control: who has access to data and models
Many companies choose based on familiarity (“we already use Azure, let’s put everything there”) or marketing (“ChatGPT is the best”). Both are wrong approaches. What you need is a structured analysis.
The four options compared #
1. On-Premise (servers on your premises) #
AI runs on physical hardware in your server room or in a proximity data centre. Data never leaves your perimeter.
Pros: maximum control, zero data transfer, predictable costs, no internet dependency Cons: higher initial investment, IT expertise needed, hardware maintenance
2. EU Cloud (European data centres) #
AI runs on cloud servers with data centres in the European Union — for example OVH, Hetzner, IONOS, Scaleway. Data stays in the EU but is managed by a third-party provider.
Pros: flexibility, scalability, no hardware to manage, GDPR-friendly Cons: growing recurring costs, provider dependency, variable latency
3. US Cloud (OpenAI, Microsoft Azure US, Google Cloud US) #
AI runs on services like ChatGPT, Microsoft Copilot, Google Gemini. Data transits through servers in the United States.
Pros: immediate setup, powerful models, integrated ecosystem Cons: high GDPR risk, CLOUD Act, data used for training, linear per-user costs, lock-in
4. Hybrid (on-premise + EU cloud) #
HTX’s preferred approach with PRISMA: lightweight, fast models on-premise for daily tasks, more powerful models on EU cloud for complex requests. Sensitive data always stays on-premise.
Pros: optimal cost/performance/privacy balance, maximum flexibility Cons: architectural complexity (managed by HTX)
Detailed comparison #
| Criterion | On-Premise | EU Cloud | US Cloud | Hybrid (PRISMA) |
|---|---|---|---|---|
| Initial cost | EUR 15-25K | EUR 0-2K | EUR 0 | EUR 10-20K |
| Annual cost (50 users) | EUR 3-5K maintenance | EUR 8-15K | EUR 33K+ (EUR 55/user/mo) | EUR 5-10K |
| 3-year TCO | EUR 24-40K | EUR 24-47K | EUR 99K+ | EUR 25-50K |
| Data sovereignty | Maximum | High (EU) | Low (USA/CLOUD Act) | High |
| GDPR compliance | Native | With DPA | Problematic | Native |
| Latency | <100ms | 50-200ms | 200-500ms | <100ms (local tasks) |
| Scalability | Limited to hardware | High | Very high | High |
| Maintenance | Required (or delegated to HTX) | Provider | Provider | HTX |
| Suitable for | >30 users, sensitive data | Flexible SMEs, variable workloads | Personal use, testing | European SMEs, any size |
TCO analysis with real numbers #
Total Cost of Ownership is the figure that matters. Not the first month’s price, but the total cost over 3 years.
Scenario: company with 50 users, daily usage #
ChatGPT Enterprise (US Cloud) #
| Item | Cost |
|---|---|
| Licence: EUR 55/user/month x 50 users | EUR 33,000/year |
| Training and onboarding | EUR 2,000 (one-off) |
| 3-year TCO | EUR 101,000 |
Plus: unquantifiable GDPR risk, OpenAI lock-in, data potentially used for training.
EU Cloud (OVH/Hetzner + open source models) #
| Item | Cost |
|---|---|
| GPU cloud server: ~EUR 800-1,200/month | EUR 9,600-14,400/year |
| Setup and configuration | EUR 3,000-5,000 (one-off) |
| Support and maintenance | EUR 2,000-4,000/year |
| 3-year TCO | EUR 38,000-60,000 |
Data in the EU, open source models with no lock-in, on-demand scalability.
On-Premise (PRISMA) #
| Item | Cost |
|---|---|
| Hardware (server + GPU) | EUR 15,000-25,000 (one-off) |
| Setup, configuration, optimisation | EUR 5,000-8,000 (one-off) |
| Annual maintenance (hardware + software) | EUR 3,000-5,000/year |
| 3-year TCO | EUR 29,000-48,000 |
Maximum control, zero data transfer, costs nearly flat regardless of user count.
Hybrid (PRISMA: on-premise + EU cloud) #
| Item | Cost |
|---|---|
| On-premise hardware (lightweight model) | EUR 10,000-15,000 (one-off) |
| EU cloud for powerful models: ~EUR 200-500/month | EUR 2,400-6,000/year |
| Setup and configuration | EUR 5,000-8,000 (one-off) |
| Annual maintenance | EUR 3,000-5,000/year |
| 3-year TCO | EUR 31,000-53,000 |
Maximum flexibility: daily tasks local, complex tasks on EU cloud. Sensitive data never leaves the perimeter.
The break-even point #
The cost graph reveals a clear pattern:
- Under 15 users: EU cloud is often the most economical choice
- Between 15 and 50 users: on-premise and hybrid become competitive
- Above 50 users: on-premise and hybrid are significantly cheaper than any per-user solution
With ChatGPT Enterprise, costs grow linearly with user count. With on-premise, cost is nearly flat: whether you have 30 or 100 users, the infrastructure is the same.
Decision framework: when to choose what #
Choose On-Premise if: #
- You have more than 30-50 users who will use AI daily
- You process highly sensitive data (health, financial, legal, industrial)
- You have a stable, predictable workload
- You have an internal IT team (or rely on HTX for management)
- You want zero dependency on external providers
- You’re in a regulated industry (healthcare, finance, defence)
Choose EU Cloud if: #
- You have variable workloads (seasonal peaks, temporary projects)
- You have a limited IT team and don’t want to manage hardware
- You want to start quickly without significant upfront investment
- You need to scale rapidly in case of growth
- Your data is sensitive but doesn’t require the highest level of isolation
Choose Hybrid (PRISMA) if: #
- You want the best of both worlds: local control + cloud power
- You have different tasks with different privacy and performance requirements
- You want to start with cloud and gradually migrate on-premise
- You want an optimised TCO without privacy compromises
- You’re a European SME looking for the most balanced solution
Don’t choose US Cloud (ChatGPT/Copilot) if: #
- You process personal data of clients or employees
- You’re subject to GDPR (all European businesses)
- You have trade secrets or intellectual property to protect
- You want cost predictability in the long term
- You’re concerned about lock-in with a single vendor
HTX’s PRISMA approach #
PRISMA (Private Intelligence Stack for Modular AI) was designed specifically for European SMEs, with a guiding principle: privacy is not an add-on, it’s the foundation.
How the hybrid architecture works #
-
Local layer (on-premise): optimised LLM models (7B-14B parameters) for daily tasks — chat, document search, text generation. Minimal latency, zero data transfer.
-
EU cloud layer (optional): more powerful models (70B+ parameters) on certified European cloud for complex tasks — deep analysis, specialist translation, coding. Data is anonymised before transmission where possible.
-
Intelligent router: the system automatically decides which layer to use based on request complexity and data sensitivity. The most sensitive data always stays local.
Products on PRISMA #
- ORCA: private enterprise chatbot — works both on-premise and on EU cloud
- MANTA: NL2SQL — typically on-premise as it works directly on company databases
- KOI: clinical AI — always on-premise for maximum healthcare compliance
How to migrate from public cloud to private AI #
If your company is already using ChatGPT or Microsoft Copilot and wants to migrate to a private solution, the path is simpler than you think.
Phase 1: Assessment (1 week) #
HTX analyses:
- Which AI services you use today and how
- What data is being processed
- What the performance requirements are
- What budget is available
The result is a personalised roadmap with a specific recommendation (on-premise, EU cloud, or hybrid) and a TCO estimate.
Phase 2: Parallel pilot (2-4 weeks) #
The private solution is configured in parallel with current ChatGPT use. Users can compare the two solutions and provide feedback. No service interruption.
Phase 3: Gradual migration (4-8 weeks) #
Users are migrated progressively, department by department. Data and configurations are transferred in a structured manner. The old service is decommissioned only when all users are operational on the new platform.
Phase 4: Optimisation (ongoing) #
After migration, HTX monitors performance and optimises the system: fine-tuning models on company data, adjusting resources, advanced user training.
Next steps #
- Take the free Assessment — Get a personalised TCO analysis for your company
- Discover PRISMA — The modular AI architecture for European SMEs
- Discover ORCA — Private enterprise chatbot
- Contact us — Let’s talk about your AI infrastructure
HTX — Human Technology eXcellence. Private AI for European businesses. Trieste, Italy.
FAQ
Is on-premise always better than cloud for privacy?
Not necessarily. On-premise offers maximum data control, but a certified European cloud (with EU data centres and GDPR-compliant contracts) can be equally secure. The key difference is with US clouds: there, data is subject to the American CLOUD Act, which allows US authorities to access it.
How much does an on-premise server for AI cost?
A server with adequate GPU to run enterprise LLM models starts at EUR 15,000-25,000. With HTX's PRISMA, the cost includes setup, optimisation, and support. Annual maintenance costs are typically EUR 3,000-5,000. For many SMEs, the cost pays for itself within 12-18 months compared to cloud solutions.
Can I start in the cloud and then move to on-premise?
Yes, and this is exactly the hybrid approach that PRISMA supports. Many companies start with a European cloud to validate use cases, then migrate on-premise when volumes justify the investment. HTX designs solutions to make this transition seamless.
How many users do I need to justify on-premise?
As a rule of thumb, above 30-50 users on-premise becomes economically advantageous compared to per-user cloud solutions like ChatGPT Enterprise. But the calculation also depends on usage frequency and task type. HTX's Assessment provides a personalised TCO analysis.
Are on-premise open source models as good as GPT-4?
For most business tasks — document chat, data analysis, text generation — open source models like LLaMA, Mistral, and Qwen achieve comparable performance to GPT-4. For highly specialised tasks there may be differences, but PRISMA's hybrid approach covers those cases too.
What happens if the on-premise server fails?
HTX includes a disaster recovery and backup plan in the PRISMA service. For companies with high availability requirements, redundant solutions are configured. In case of hardware failure, the system can fail over to EU cloud transparently with the hybrid configuration.