Use case
Control AI spend before it sets the budget.
Join model and token economics with configurable routing, caching and cost-per-outcome analysis.
- AI & LLM Cost IntelligenceToken Economics
- Policy-Based LLM Model RoutingToken Economics
- AI Token Caching & ReuseToken Economics
- AI Unit EconomicsToken Economics
End-to-end workflow
Five practical steps to AI cost control.
Steps and permissions adapt to each implementation.
Connect supported AI billing and application telemetry
Attribute model consumption to applications and owners
Analyze token, request and unit-economics trends
Apply routing and caching policies for eligible workloads
Assess the resulting financial and operational effects
Connected capabilities
What powers the AI spend workflow.
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
Book a demo
Bring your AI bill. See where it goes.
Bring two or three priorities, and we’ll show how CloudScore handles them and what it needs from you.