TokenAtlasIllustrative example · Mid-Size Enterprise Tech
How a Tech Company Aligned Engineering and Finance Teams on AI Spending Using TokenAtlas
DataCore Systems had AI workloads running across engineering, product, and finance systems. Each department had its own view of the world. The AI bill kept growing, and no one could agree on why. TokenAtlas gave the company one shared source of truth for AI spending.
Illustrative example. This is a composite scenario built from modelled TokenAtlas calculations, not a named customer. The company, figures, and quotes are illustrative and do not represent verified customer results.
The challenge
No shared view of AI spend
DataCore Systems used AI APIs across several departments. Engineering tracked usage in one place, finance tracked invoices in another, and product had its own budget assumptions. Each team had partial data. None of them had the full picture.
"Finance saw the cost, engineering didn't know where it came from."
- Fragmented cost visibility meant each team blamed the others for overruns.
- No team-based allocation made it impossible to hold departments accountable for their AI usage.
- Budget overruns arrived without explanation, slowing planning and eroding trust between teams.
- Without governance or controls, AI spending grew faster than the company could justify.
Before TokenAtlas
Cost management was disconnected
Separate dashboards
Engineering, finance, and product each relied on different tools. No dashboard showed cost, usage, and budget in one place.
No unified cost system
Invoices, usage logs, and forecasts were never reconciled. The same spend was counted differently by each department.
Reactive cost management
Cost reviews happened after the month closed. By then, the money was spent and the conversation had turned defensive.
Unreliable budget planning
Forecasts were built on assumptions, not actual usage trends. Every planning cycle started with disagreement.
The solution
A single AI cost modelling layer
DataCore Systems described all of its AI workloads in TokenAtlas. Every workload was tagged by team, project, and model. The result gave finance, engineering, and product a shared set of modelled numbers they could trust.
Team-based cost allocation
Modelled AI costs were mapped to engineering, product, and finance-defined cost centers using the tags the team assigns. Accountability became clear.
Unified AI cost view
One view showed modelled cost, assumed usage, and projections by team and project — no more conflicting numbers between departments.
Governance planning
Budget thresholds and review conventions defined against the modelled workloads turned reactive reviews into planned checkpoints.
Model usage insights
Cross-team workload patterns revealed which models and workflows drove the most modelled spend, guiding standardization decisions.
TokenAtlas models costs from the workload inputs you provide. It does not connect to provider accounts, ingest live usage, or collect API keys.
Results
AI spending under control
Both teams now work from the same modelled cost data and the same definitions.
Forecasts are based on documented workload assumptions instead of guesswork.
Thresholds set against the modelled workloads surface overruns before the team commits to them.
Leadership reviews modelled AI cost with confidence, not debate.
- Team-level budgets were introduced and tracked in the same dashboard finance and engineering use.
- A recurring budget overrun in the product department was traced to a single model and brought back under control.
- Monthly AI cost reviews that previously took days now take minutes.
- Procurement standardized preferred models based on usage data, reducing unnecessary provider fragmentation.
"TokenAtlas became our AI cost control layer."
— CTO, DataCore Systems
Bring visibility to your AI spending
Align engineering, finance, and product on one shared source of truth for AI costs.