AI economics, token usage & governance
Price every model call like the decision it is.
Every AI request costs money, so see that cost by app, team and model.
Daily AI spend by application · $
| Application | Route | Tokens / req | Share of spend |
|---|---|---|---|
| Support assistant | Smaller model | 3.1k | 46% |
| Document summary | Larger model | 6.8k | 38% |
| Product search | Cache first | 0.9k | 16% |
AI economics in practice
Manage AI cost without losing the business story.
Connect model consumption, application demand and optimization policies to measurable AI efficiency.
01
Break down AI model spend by app, team, provider and other usage details.
02
Apply released model-routing and token-caching capabilities within configured application and endpoint policies.
03
Measure AI cost per request, workflow or business unit, alongside adoption and workload changes.
The AI FinOps capability set
Economics, tuning and guardrails for AI.
Explore each released capability, its data dependencies and the decisions it supports.
AI & LLM Cost Intelligence
Attribute the economics of inference
Policy-Based LLM Model Routing
Select the model that fits the workload
AI Token Caching & Reuse
Avoid unnecessary repeat inference costs
AI Unit Economics
Measure cost per useful AI outcome
AI Spend Governance
Establish accountability for AI model consumption
Dynamic Reporting
Your cloud data, your questions, your reports
Cloud Cost Anomaly Detection
Investigate spend changes before they become run rate
How it works
From model telemetry to economic control
Link AI consumption, policy decisions and economic measurement in a repeatable operating cycle.
Connect supported model billing and application telemetry
Allocate model and token usage to owners and workloads
Investigate request, model and cache cost drivers
Configure routing and caching controls for eligible traffic
Review usage efficiency and economic results over time
See the experience
Trace AI cost back to its drivers.
See model spend, token activity and cost per request side by side.
- Break down AI model spend by app, team, provider and other usage details.
- Apply released model-routing and token-caching capabilities within configured application and endpoint policies.
- Measure AI cost per request, workflow or business unit, alongside adoption and workload changes.
See Token Economics with scenarios like yours.
Book a demoEcosystem
Built on the cloud data you already have.
These are typical sources CloudScore connects to; coverage depends on your setup and permissions.
Frequent questions
Is model routing available today?
Yes. Model routing is available now, and supported providers and setup are checked for each customer.
Can we quantify the value of token caching?
If usage data and a baseline are available, you can see how caching changed token use and cost.
Does CloudScore connect to every model provider?
Supported AI providers and cost detail depend on the integrations set up for you.
Book a demo
Give AI spend the same rigor as cloud.
Bring two or three priorities, and we’ll show how CloudScore handles them and what it needs from you.