The TCO calculator for an honest cost comparison in 2026
Your own AI hardware or the cloud API? The answer comes down to the break-even: the number of users at which your own GPU pays off. We show the real prices for cloud APIs and on-premise hardware in 2026, the formula behind them, and an interactive calculator to model your own scenario.
As of July 2026 — indicative values, not legal or tax advice
On-premise AI typically pays off from a two-digit user count or with document- and agent-heavy usage; for light chat usage under about 30 employees, an EU cloud API is usually cheaper.
Cloud APIs cost nothing when idle and scale per token — ideal for few users or fluctuating load. Your own hardware creates fixed costs (purchase, electricity, maintenance) that only amortise above a certain usage volume. The more expensive the chosen cloud model and the more intensive the usage, the sooner on-premise pays off. On top of that come arguments that never show up in the raw price: data sovereignty, GDPR, professional secrecy and independence from the vendor.
Cloud AI is billed per token — separately for input and output. The spread is enormous: top-tier US premium models cost a multiple of cheap EU models. The following indicative values (in US dollars per 1 million tokens) reflect the price structure in mid-2026:
| Model class | Input ($/1M) | Output ($/1M) | Positioning |
|---|---|---|---|
| Premium US (GPT-5.5 / Opus class) | 5.00 | 27.00 | Highest quality, US provider |
| Mid-range EU (Mistral Large 2) | 2.00 | 6.00 | Strong quality, EU-hosted |
| Low-cost EU (Mistral Small 3) | 0.10 | 0.30 | For simple tasks, EU-hosted |
Converted at 0.92 USD/EUR. Prices change constantly; adjustable in the calculator below.
An employee with medium usage (RAG, knowledge work) consumes around 200,000 input and 25,000 output tokens per working day. At 21 working days and the EU mid-range model, that is roughly EUR 10.60 per user per month. With a US premium model, the same consumption rises to around EUR 32 per user per month — the factor of three to four between EU mid-range and US premium is the single biggest lever in any calculation.
On-premise means one or more GPUs in your own server room, plus the surrounding infrastructure. Hardware cost depends on the GPU class, which in turn determines how large a model you can run. Three typical tiers:
| Setup | GPU | Price (hardware) | Suitable for |
|---|---|---|---|
| Entry | 1× RTX 5090 32 GB | EUR 3,000–3,500 | Models 7–32B quantised |
| Mid | 1× RTX PRO 6000 Blackwell 96 GB | EUR 7,800–12,200 | 70B+, multiple sessions |
| Enterprise | 1× H100 80 GB | EUR 23,000–30,000 | Highest load, large models |
An entry GPU draws around 600 watts under load. At 50% utilisation and 22 ct/kWh commercial electricity, that is roughly EUR 45 to 50 of power per month. For context: German commercial electricity for SMEs is realistically around 27 ct/kWh (at 10,000 kWh annual use), with new industrial contracts closer to 16.7 ct/kWh. The subsidised industrial electricity price of 5 ct/kWh discussed politically in 2026 explicitly applies only to around 91 energy-intensive sectors — not to the ordinary mid-market. Calculations that make on-premise AI look good with 5 ct are unrealistic for the vast majority of companies.
Between the cloud API and your own server sits dedicated GPU rental in EU data centres. It combines data sovereignty and predictable fixed costs with zero acquisition cost — and can be cancelled monthly. Ideal for trialling on-premise before buying hardware. Indicative values in mid-2026:
The break-even is the user count at which the fixed monthly on-premise cost drops below the cloud cost, which rises with the number of users. The cloud curve climbs linearly, the on-premise curve is flat — the intersection is the decision point.
Simplified formulas:
Cloud/month = users × tokens/day × working days ÷ 1M × price × rate On-premise/month = GPU price × (1 + surcharge) ÷ (years × 12) + electricity + maintenance Break-even = on-premise/month ÷ cloud cost per user
The raw cost comparison leaves out factors that tip the balance for many companies — and are even legally mandatory for those bound by professional secrecy:
With your own or EU-rented hardware, personal data never leaves your sphere of control. No third-country transfer, no dependence on shifting adequacy decisions.
Law firms, medical practices, tax advisers and psychotherapists are bound by confidentiality. Passing client or patient data to external processors is a criminal offence — on-premise or tightly secured EU processing is often the only clean route here.
Local models respond without a network detour and keep working even if the internet line or the cloud provider goes down.
Running open models on your own infrastructure means you are not tied to price hikes, API changes or a single provider retiring a model.
Set the number of users, usage intensity, cloud model class and on-premise setup. The calculator shows your break-even, the cost curve and the total cost over the chosen period. All assumptions can be adjusted under “Advanced assumptions”.
From 26 users on, on-premise is cheaper
Total cost over 3 years
| Cloud API total | €7,651 |
| On-premise totalHardware once + electricity & maintenance ongoing | €9,861 |
| Difference | €2,210Cloud cheaper |
Recommendation
At the current 20 users, the EU cloud API is the more economical choice. On-premise pays off in this profile from roughly 26 users on — or earlier, once data sovereignty, GDPR and professional-secrecy rules tip the balance.
As of July 2026, adjustable. Indicative values without warranty — not legal or tax advice. Hardware, electricity and cloud API prices change constantly.
As a rule of thumb, on-premise AI pays off from a two-digit user count. With medium usage on a cheap EU cloud API, the break-even against an entry setup is around 26 to 27 users. With expensive US premium models, the maths tips at around 9 users. Document- and agent-heavy usage lowers the threshold further.
An RTX 5090 with 32 GB runs quantised models from 7 up to around 32 billion parameters. An RTX PRO 6000 Blackwell with 96 GB fits 70B models and several parallel sessions. An H100 with 80 GB is meant for the highest load and large models. What matters is parameter count, quantisation and the desired context length.
No, GDPR does not mandate an on-premise solution. An EU-hosted cloud can be GDPR-compliant too if the data processing agreement, server location and technical measures are right. On-premise becomes a strong argument, though, when highly sensitive data is processed or parties bound by § 203 professional secrecy are involved.
An entry GPU at around 600 watts of sustained load costs roughly EUR 45 to 50 of electricity per month at 50% utilisation and 22 ct/kWh commercial power. The subsidised industrial price of 5 ct/kWh discussed in 2026 applies only to around 91 energy-intensive sectors, not the ordinary mid-market.
Yes. Dedicated GPU rental in EU data centres combines data sovereignty with predictable fixed costs and no acquisition. An RTX PRO 6000 with 96 GB is available from EU providers from around EUR 889 per month, smaller GPUs considerably cheaper — ideal for trialling on-premise before buying hardware.
We run your specific scenario, check the right model and hardware class, and implement the solution — on-premise or as a GDPR-compliant EU instance. Pragmatic and without the hype.
To on-premise AI consultingOr start with a structured AI potential and compliance analysis.
Go to the AI readiness analysis