Make every AI dollar accountable
Most companies can't say what their AI spend is buying. We measure it, cut the waste, and connect each use case to a business outcome.
The problem
AI costs rarely grow the way anyone planned. Usage spreads across teams, agents run long chains of model calls, and bills arrive with no clear owner. Meanwhile, leadership is asked to justify the investment without the numbers to do it.
What the research says
of organizations have no clear owner of AI costs.
of AI spend is estimated to be wasted.
of CEOs report no significant financial benefit from AI so far.
What the AI cost audit covers
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Spend visibility
Map every model, API and AI tool cost to the team, workflow and feature that drives it.
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Waste diagnosis
Find oversized models, repeated context, missing caching and runaway agent loops.
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Model routing
Send routine work to lower-cost models and reserve frontier models for genuinely hard tasks, tested on your real workloads.
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Cost per outcome
Define unit economics for each use case, such as cost per resolved ticket or per processed document.
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Self-hosting assessment
Where data residency or volume justifies it, evaluate running open-weight models on your own or dedicated infrastructure.
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Ongoing monitoring
Dashboards and budget alerts so spikes are caught in hours, not at month end.
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Prompt caching and batch processing
Reuse repeated instructions and documents through provider caching, and move work that can wait to discounted batch processing.
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Token budgets and context control
Trim what goes into every call, cap output length and reasoning effort where quality allows, and stop agents that loop.
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Cost allocation by team
Separate keys, projects and tags for each team and product, so every token has an owner and the bill can be shown back or charged back.
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Forecasts and budget guardrails
Forecast spend from real usage, set budgets for each team, and get warned before a limit is reached.
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Smaller models where they fit
Test small and open-weight models, including fine-tuned ones, on the tasks where they match the quality you need for less.
Deliverables
- AI spend map by team, workflow and model
- Prioritized savings plan with estimated impact
- Routing and caching implementation for agreed workloads
- Cost-per-outcome dashboard
Who it's for
- Companies spending meaningfully on LLM APIs or AI tools
- Finance and engineering leaders who need to explain AI spend to the board
- Teams scaling AI agents into production
How we work
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Scoping call
We learn how you use AI today and agree what matters most.
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Fixed-scope assessment
A defined assessment with a clear deliverable, so you know the cost upfront.
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Implementation
We fix what we find, alongside your team.
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Ongoing support (optional)
Monitoring, reviews and retesting as your AI use grows.
AI cost and ROI questions
How much can we save?
It depends on your workloads. Industry research reports typical savings of 37% to 46% from intelligent model routing alone (Capgemini Invent, The Era of AI Yield (2026)). We estimate your specific opportunity during the audit, before you commit to implementation.
Will cheaper models hurt quality?
Not if routing is tested properly. Every change is validated against a sample of your real tasks, and anything that fails your quality threshold stays on the stronger model.
Which providers do you work with?
Any major model provider or cloud AI platform, plus open-weight models on hosted or self-managed infrastructure.
Do you need access to our data?
For cost work we mainly need billing, usage and logging data. Access is scoped and agreed in writing before we start.
What is tokenomics?
Tokenomics is the economics of paying for AI by the token. The bill depends less on the price list than on how each workflow is built: prompt length, retrieved context, output length, model choice, reasoning effort, caching and how many times an agent loops. That is why the same feature can cost very different amounts in two companies.
What is FinOps for AI?
FinOps for AI applies cloud financial management to AI spend: every model, API and GPU cost visible and assigned to an owner, forecasts and budgets, ongoing optimization, and a clear link between spend and business value.
Is self-hosting a model cheaper?
Not always. Hosted endpoints for open-weight models are often cheaper than running the same model yourself. Self-hosting pays off when data residency, audit, fine-tuning or steady high volume require it, and we assess that case by case (Capgemini Invent, The Era of AI Yield (2026)).
Ready to see where you stand?
Book a free scoping call. We'll tell you honestly whether this is a fit.