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Token Economics capability

Send each job to the right model. By your policy.

Routing rules make AI model choice a deliberate cost decision, not a fixed default.

Policy, then the model
Routing policysupport-assistantEnforced
  1. 1intent = FAQ and prompt < 2k tokens→Small model
  2. 2needs multi-step reasoning→Large model
  3. 3team budget above 90% this month→Small model + review
  4. —default→Small model

Last 7 days · 412k requests

  • Small model62%
  • Large model31%
  • Small + review7%

Blended cost $0.004 per request, was $0.007 with one model

What it does

Match each workload to the model that fits.

Use routing rules to make AI model choice a deliberate cost decision, not a fixed default.

Data in

Cloud & operational data

Approved sources and scope

→

Analysis

Context & decisions

Rules, economics, ownership

→

Result

Measured outcomes

Review, validate, report

Why it matters

Define the requirements. Choose models against them.

Send each AI request to the right model based on cost, quality and uptime, and track the results over time.

Example

Compare models for a high-volume sorting task where an expensive model isn’t always needed.

Data and results

Routing decisions you can trace and explain.

Required data and dependencies

Integrated applications, available model endpoints, credentials and configured routing policies.

Operational outputs

Model-routing configuration, policy execution context and usage visibility.

Related capabilities

Released capability

See Policy-Based LLM Model Routing in context.

Check how this feature fits your cloud, data, permissions and business needs.

Availability note

The CloudScore product team confirms what’s available; setup depth and requirements depend on your deployment.

Book a demo

Try policy routing on your own traffic.

We’ll show this feature with examples like yours and explain the data and permissions it needs.