Solution · Cost & Budget Controls

AI spend is enforced at the moment of use

AI Security Runtime™, the enforcement layer that secures every AI interaction, also meters what each one costs. Spend is measured on the governed path as it happens and attributed to the people, teams, and agents who created it. Budgets enforce inline, before the model call and before the bill arrives.

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First Recon AI · Admin Console
Usage and Spend dashboard: total spend, API calls, cache savings, and budget utilization, with spend over time and a needs-attention queue
  • Metered on the pathUsage measured per request as it happens, on the same path where policy enforces, across providers.
  • Caps that holdBudgets enforce inline, before the model call.
  • Attribution that sticksEvery request carries user, team, department, cost center, model, and provider.
  • Agents metered tooAgent workloads carry the same attribution and meet the same caps; a runaway loop hits its budget at enforcement time, not on the invoice.
  • Finance-ready reportingDashboards and exportable reports for chargeback, showback, finance reviews, and board updates.
  • Optimization signalsRecommendations for model substitution, caching, and over-provisioned agents, drawn from the traffic that ran.

Caps act before the model call

Spend is enforced where security is enforced. Every metered interaction crosses the same four functions, and a cap is a decision made at enforcement time.

LIVE SPENDEvery interaction metered as it happens on the enforcement path, attributed at request level, across providers
BUDGETS THAT HOLDCaps enforce at the same point policy does, inline, before the model call; at the cap, your policy decides what happens next
REAL ALLOCATIONSpend attributed to user, team, department, cost center, model, and agent from runtime context, carried on the request itself
ONE CONSOLESpend and adoption sit beside threat activity, fed by the same runtime decisions

Common questions

How does the runtime's metering relate to provider invoices?

The runtime meters usage as it happens on the enforcement path, per request, with the user, team, model, and provider attached at enforcement time. Provider invoices remain the bill; they arrive later and aggregate. Runtime metering exists for control and attribution at the moment of use. It enforces caps, allocates spend to the teams that created it, and catches a runaway workload while it is still running.

What happens when a budget hits its cap?

Whatever your policy says. At budget enforcement the runtime supports allow (record the overage), alert (notify owners and keep going), hold for human review (further calls wait for an approval), or block (calls stop until the budget is raised or resets). Caps enforce inline, before the model call, so a cap is a decision made at enforcement time rather than a number discovered at month end.

How granular is attribution? Can we run chargeback?

Request-level. Every metered interaction carries user, team, department, cost center, model, and provider, and agent workloads are attributed the same way. Dashboards and exportable reports support chargeback, showback, finance reviews, and board updates without spreadsheet reconstruction.

Secure every
AI interaction.

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