Token-aware controls
Set rate, concurrency and token policies. Monthly human-user budgets can warn or freeze further model access.
AI cost control
Know the usage.
Control the spend.
Centralize model traffic, manage token demand and make estimated costs visible before they disappear into a provider invoice.
AI cost control workflowGoverned
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01
RouteApproved provider and model alias
-
02
Control demandToken limits and compression policy
-
03
Apply human-user budgetsMonthly warning and freeze thresholds
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04
ReportEstimated costs and tokens saved
Identity · Policy · EvidenceWorkflow illustration
Limits, budgets & visibility
Set limits before
usage becomes spend.
Prompt compression
Compact eligible prompts after security scanning. Record compression estimates and token savings for review.
Estimated cost reporting
Use configured input and output token rates to report model usage and estimated spend. Reconcile final figures with provider billing.
A practical cost-control process
Connect the rate card
to the request path.
Set a usable rate card
Configure model and provider token rates so cost estimates reflect your commercial agreements.
Apply budgets where supported
Human users can inherit individual budgets from groups or receive explicit overrides. System principals use gateway limits rather than these monthly user budgets.
Validate optimization with your workload
Compression outcomes vary by prompt and policy. Evaluate task quality and token changes on representative traffic before expanding the policy.
Explore the platform
See the controls behind the outcome.
See AI Warden in action
See where model usage
becomes a cost decision.
Walk through token policies, human-user budgets and estimated cost reporting across supported providers. Bring a representative workload to discuss compression and task quality.