Making enterprise AI spend visible, measurable and easier to control
An AI cost management layer that tracks model usage, token consumption, workload cost and application performance across enterprise GenAI workloads. The solution helps teams select the right model, reduce unnecessary token usage and allocate AI spend to business owners and use cases.
AI usage was growing, but there was no consistent view of what each workload was costing.
As teams introduced copilots, RAG applications and AI agents, model usage increased across different providers and applications. Finance and technology teams needed a way to connect consumption to business value.
What we found
- Different AI applications used different models with different pricing structures.
- Token consumption was not consistently captured at application and business owner level.
- Teams often selected models based on capability without comparing cost against the required quality.
- Large prompts and repeated context increased input token consumption.
- Applications repeated similar requests that could be served through caching.
- AI costs were visible at provider level but not always at workload or business process level.
- There were limited thresholds for identifying unexpected usage increases.
What the business needed
- A common measurement framework for AI tokens, model usage and cost.
- Cost visibility by application, team, model, business process and use case.
- Model routing based on task complexity, quality requirements and cost.
- Prompt and context optimization to reduce unnecessary token consumption.
- Controls for repeated calls, excessive usage and unexpected consumption.
- Budget alerts and chargeback or showback for AI workloads.
- A repeatable AI FinOps model that could scale as new applications were added.
We built an AI FinOps layer that measures every workload and creates optimization opportunities before costs scale.
The solution combines usage telemetry, workload classification, model routing, prompt optimization and business level cost reporting.
LLM cost and token optimization platform
The platform provides a common operating layer for measuring and managing enterprise AI consumption.
- Captured input tokens, output tokens, model, application, user group and workload information for governed AI requests.
- Calculated usage cost using model specific pricing and internal allocation rules.
- Grouped workloads by business process and use case to show where AI spend was concentrated.
- Introduced model routing so simpler tasks could use lower cost models where quality requirements allowed.
- Reduced unnecessary prompt context through structured prompt templates and context management.
- Used semantic caching for repeated or equivalent requests where the business process allowed it.
- Created thresholds and alerts for abnormal usage, rapid cost increases and high consumption workloads.
- Provided dashboards for technology, finance and business owners to track AI spend and optimization opportunities.
A shared AI cost layer connects applications, models and enterprise reporting.
The architecture provides a consistent view of consumption while keeping model selection and optimization independent from individual applications.
A six stage approach to move from AI usage visibility to continuous cost optimization.
The program starts with measurement and builds optimization controls using actual workload behavior.
Baseline
Inventory AI applications, models, providers, workloads and current consumption patterns.
Measure
Capture tokens, calls, latency, model usage and cost at application and workload level.
Attribute
Map AI consumption to teams, business processes, products and use cases.
Optimize
Apply model routing, prompt reduction, context controls and caching where appropriate.
Control
Introduce budgets, thresholds, alerts and ownership for AI consumption.
Improve
Review cost and quality together and continuously tune models and AI workloads.
The value comes from controlling consumption without reducing the quality required by the business.
Lower AI spend
Model routing, prompt optimization and removal of unnecessary calls can reduce avoidable GenAI consumption.
Lower token usage
Better prompt construction, context management and reuse can reduce average tokens consumed per request.
Fewer unnecessary calls
Caching and workload controls can prevent repeated requests where the business process does not require a new model response.
Lower cost per transaction
Workload level measurement helps identify expensive processes and move suitable workloads to more efficient model options.
Faster cost investigation
Centralized telemetry reduces the effort required to identify which application or workload is driving unexpected AI consumption.
Spend visibility target
Governed AI workloads can be reported by application, model, team and business use case through a common FinOps view.
AI becomes easier to scale when every workload has a measurable cost and an accountable owner.
The AI FinOps layer gives technology and finance teams the information needed to balance model quality, speed and cost.
Controlled AI Spending
Business owners can see where AI consumption is increasing and understand the workloads responsible for the spend.
Better Model Economics
Teams can choose models based on the required quality and business outcome rather than using the most expensive model for every task.
Improved Engineering Efficiency
Reusable routing, prompt and caching patterns reduce repeated optimization work across individual AI applications.
Finance Ready AI Reporting
AI usage can be connected to business ownership, budgets and cost allocation so GenAI becomes measurable as an operating expense.
Make every AI workload measurable and economically efficient.
Build the visibility and optimization layer needed to manage tokens, models, usage and AI spend as enterprise adoption grows.
Discuss your AI cost optimization program