AI Controlling & Model Selection
Which AI model delivers the required value at a sustainable cost?
IN BRIEF
AI controlling brings quality, usage and costs into one decision process. It connects model comparisons with the financial perspective of management, CFOs and controlling teams. Beyond model prices, it considers integration, review, operations and the cost of self-hosted infrastructure.
When to involve us.
When AI spending grows, teams use different models or a choice between hosted services and self-hosting is needed, a shared cost and quality baseline is essential.
We compare suitable models against your tasks and agreed quality criteria. Usage scenarios account for volume, response times, human review and operations. The result is an evidence-based model choice, costs per business transaction and budgets with owners and review dates.
What the work produces.
We agree on the scope around your goals, starting point and available capacity. The following elements illustrate what an engagement can include.
WHAT WE ADDRESS
- Model comparison using quality and cost criteria
- Cost per use case and usage scenario
- Comparison of hosted and self-hosted options
- Budgets, ownership and ongoing cost monitoring
POTENTIAL OUTPUTS
A well-founded model decision and a cost model that business, IT and finance teams can use together.
A focused starting point.
A concrete use case, typical inputs and outputs, and an estimate of usage volume. Existing invoices and quality requirements help make the comparison realistic.
The question first. Then the scope.
The first conversation clarifies the objective, deliverables and people involved. We agree whether the engagement covers design, pilot support or technical implementation, who takes on each task and how the deliverables will be assessed.
Discuss a starting point by emailIs the cheapest model also the most economical solution?
Not necessarily. A low price per request helps little if outputs need frequent review or rework. What matters is total cost per successfully completed task, including model usage, integration, delays, human review and operations. Self-hosted models also incur infrastructure and support costs. We therefore compare concrete usage scenarios against the same quality requirement and revisit the decision when volume, models or commercial terms change.
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