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Hardware Cost Analysis · April 2026

Run AI locally – secure, scalable, cost-efficient

On-premise LLM infrastructure instead of cloud dependency: one-time investment, full data sovereignty, predictable costs. This dashboard summarizes the cost analysis interactively.

−75%
Savings vs. cloud at 200 users
€8vs. 30 €
Cost per user/month (RTX 6000 Ada vs. cloud)
from 50
users on-premise becomes cheaper than cloud
100%
internal data processing, GDPR-compliant

Cloud vs. On-Premise

Pay-per-use models scale costs with every query – and sensitive data leaves the company. Ensemble reverses both of these.

Cloud LLMs

  • High ongoing costs: ~0.01–0.10 € per query, explodes with growth
  • Data privacy risk: external processing of sensitive data (GDPR, compliance)
  • Vendor lock-in: no control over updates and limits
  • Latency: depends on internet connection and provider
  • Scaling limits: API limits, expensive upgrades

Eniware EnsembleOn-Premise

  • Cost transparency one-time investment, no hidden costs
  • Data sovereignty: data stays 100% within the company
  • Scalable: hardware upgradable – from 10 to 500+ users
  • Performance local real-time inference without internet latency
  • Future-proof: supports large models up to Llama 3 405

Interactive Cost Calculator

Set your number of users – the calculator determines the appropriate hardware and compares total costs over 3 years with the cloud (baseline: €30/user/month, in line with the AWS/GCP scenario in the analysis).

User
Hardware selection:
Cloud (AWS/GCP), 3 years
On-premise, 3 years (incl. power & maintenance)

Hardware Comparison

All prices include all components, as of April 2026. Click a card to run it through the cost calculator.

Tokens/s based on a 7B model · max. model size at 4-bit quantization · power and maintenance figures for RTX 5090 and H200 are approximate.

Cost Analysis: Cloud vs. On-Premise over 3 Years

Reference scenario from the analysis: 50 users, 10,000 queries per day.

Acquisition Power (3 yrs) Maintenance (3 yrs) Cloud usage costs
Cloud (AWS/GCP)54,750 € · 30 €/user/month
RTX 6000 Ada14,350 € · 8 €/User/month
H100 PCIe42,300 € · 23 €/user/month
Result: From 50 users onward, on-premise with RTX 6000 or H100 becomes cheaper than the cloud – the RTX 6000 Ada saves 40.400 € (−74 %) over 3 years in this scenario. The more users, the greater the savings.

Scaling Plan

Recommended entry point: pilot project with 10–30 users on the RTX 6000 Ada – good balance of price and performance, clear migration path for growth.

Phase 1

H100 PCIe

~10–30 User
Investment11.850 €
Model size7B–13B
Power~300 €/year
Tokens/s (7B)~5.000
Setup time~4 week
Internal chatbots, document analysis – scalable via a second machine or H100 upgrade.
Phase 2

H100 PCIe

~50 User
Investment37.800 €/year
Model size70B–100B
Tokens/s (7B)~5.000
Automation, support tickets, more sophisticated models.
Phase 3

H100 PCIe

150+ User
Investment51.600 €
Model size200B+
Power~300 €/year
Tokens/s (7B)~6.000
Enterprise AI, R&D, largest open models (405B+).

Next Steps

From requirement to running pilot project in four steps.

Clarify use case

Which applications are planned – chatbot, document analysis, code generation? How many active users?

Select hardware

Suitable GPU configuration based on your requirements – see cost calculator above

Start pilot project

Setup in your infrastructure in about 4 weeks, test phase with 10–30 users.

Plan scaling

Joint growth plan – hardware upgrades without migration disruption

"Run AI locally – secure, scalable, cost-efficient