What AI Really Costs
Why model selection determines whether your automation costs €200 or €12,000 per month
The Subsidy Problem
When you use ChatGPT, Claude, or Gemini today, you’re not paying the real price. Every major provider sells their models below production cost to gain market share. OpenAI burns billions. Google subsidizes through advertising revenue. For you as a user, that sounds great at first.
But there’s a consequence: if you build your processes on the most expensive model today because it “only” costs €20 per month, you’ll be surprised tomorrow. Subsidies end. Prices rise. And suddenly, the process you never questioned costs ten times more.
The conclusion is not that AI is too expensive. The conclusion is: Choosing the right model for the right task is not a technical decision – it’s an economic one.
Not Every Task Needs a Frontier Model
The AI landscape doesn’t consist of one model. There are dozens, with radically different costs and surprisingly small quality differences for many standard tasks.
| Model | Input / 1M Tokens | Output / 1M Tokens | Typical Use |
|---|---|---|---|
| Claude Opus 4.6 | $15.00 | $75.00 | Complex analysis, strategy |
| Claude Sonnet 4.6 | $3.00 | $15.00 | General purpose, good balance |
| Claude Haiku 4.5 | $0.80 | $4.00 | Classification, extraction |
| Gemini Flash 2.0 | $0.10 | $0.40 | Simple tasks, routing |
| Local Open-Source | ~$0.00 | ~$0.00 | Data-sensitive tasks |
The difference between the most expensive and cheapest model is not 2× or 5×. It’s a factor of 300. And for many business tasks, the cheaper model is not worse – it’s sufficient.
The critical skill is knowing which model delivers the most value per euro at which point.
Case Study: 500 Invoices per Day
A mid-sized logistics company processes 500 incoming invoices per day. Previously: manual, three full-time staff, error-prone during peak loads. Two ways to solve this with AI:
Approach A: One Model for Everything
Every invoice through the frontier model
- Model: Claude Opus 4.6
- Cost per invoice: ~$0.80
- 500 invoices × 22 working days
- Accuracy: 97.2%
- Human review needed: ~3%
Monthly cost: ~$8,800
Approach B: Intelligent Routing
Multi-stage pipeline with model mix
- Stage 1: Classification → Gemini Flash → $0.01
- Stage 2: Extraction (90%) → Haiku → $0.05
- Stage 3: Edge cases (10%) → Sonnet → $0.35
- Accuracy: 98.1%
- Human review needed: ~2%
Monthly cost: ~$680
Same task. Better accuracy. 13× lower costs.
Why Internal Teams Rarely Solve This
The AI landscape changes weekly. In April 2026, there are already over 30 relevant models from eight providers, with different strengths in language, code, image processing, and logical reasoning. New models appear on a weekly basis.
An internal team would need to continuously evaluate new models, run benchmarks against their own data, recalculate costs, and adjust pipelines. For a single company, this effort rarely pays off. For a specialized integrator who runs this evaluation for many clients, it does.
100%
Flash / Local
Pre-filtering & Routing
All inputs
85%
Haiku / Small Models
Standard processing
Auto-resolved
12%
Sonnet / Mid Models
Complex cases
Escalated
3%
Opus / Frontier
Edge cases & Audit
Highest tier
<1%
Human
Final decision
Human-in-the-Loop
Three Principles of Cost-Effective AI Integration
The question is not “Can we use AI?” but “How do we deploy AI so the investment pays off within months, not years?”
01
The cheapest model that solves the task
Not the best. The cheapest that achieves the required accuracy. Frontier models are for edge cases, not standard processes. 85% of your tasks can be handled by models costing 1/50th of the frontier price.
02
Multiple models instead of one
Like a toolbox: hammer and scalpel, not just hammer. A fast model pre-filters, a mid-tier processes, a strong one verifies. Where it’s critical, two models decide against each other – or a human does.
03
Measure, don’t assume
Every model is tested against your real data before going into production. With every new release, again. No relying on benchmarks or marketing – only on what works with your documents, your processes.
What This Means for You
If you want to build AI automation in your company, you face a fundamental decision. Either you bet on the most expensive, most powerful model and hope the subsidies last. Or you invest once in a well-designed architecture that uses the right model at the right point – and whose costs you can control.
The first is an experiment. The second is a system.
We build systems.
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In a 30-minute initial consultation, we analyze where AI has the greatest leverage in your processes – and what it realistically costs. No slides, no buzzwords.