AI Cost & Budget Control for Agents

Cost as a governed control

Per-iteration accounting. Every model call is costed from provider token counts and per-model pricing — accumulated in-loop, not reconciled next month.
Hard caps, defined fallbacks. When a run hits its cost cap the policy decides: stop, escalate to a human, or continue degraded — recorded either way.
Per-skill rate limits. Token-bucket limits per skill and per agent stop a runaway loop from starving everything else.
Idempotency cache. Duplicate tool calls short-circuit to cached results — the cheapest call is the one that never runs twice.
Per-instance metering. Spend attributed by customer, org, tenant, and instance for chargeback and showback.

Frequently asked

How does Prove7 control AI agent costs?

Cost is enforced inside the execution loop: each model call is costed from token counts, accumulated against a per-run cap, and when the cap is reached the configured policy fires — stop, escalate to a human, or continue degraded. Rate limits and idempotency caching prevent runaway and duplicate spend, and everything is metered per tenant and instance.

Accountable Autonomy™ — answers as it acts.

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