The per-token billing approach is smart — most AI agent systems I have seen just track total API cost at the provider level, which makes it really hard to attribute spend to specific features or user actions. Breaking it down to the token level gives you the granularity to actually optimize.
One thing I have found useful in similar setups is adding a "token budget" per task type. Instead of just tracking what was spent, you set a ceiling before execution starts. If the agent is about to blow past the budget on a single task, it forces a checkpoint instead of running up the bill silently. Pairs well with the billing system you built here.
Yeah, totally agree, budgeting is the missing control loop.
Per-token billing (what I built here with Kong AI Gateway + Konnect Metering & Billing) gives you accurate attribution, who/what actually consumed tokens. But by itself, it’s reactive.
A token budget adds a runtime guardrail. For agent flows, that means checking expected token usage before each step and stopping or degrading (smaller model, less context, fewer tool calls) instead of silently overspending.
In practice, you need both:
metering for visibility, budgets for control.
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The per-token billing approach is smart — most AI agent systems I have seen just track total API cost at the provider level, which makes it really hard to attribute spend to specific features or user actions. Breaking it down to the token level gives you the granularity to actually optimize.
One thing I have found useful in similar setups is adding a "token budget" per task type. Instead of just tracking what was spent, you set a ceiling before execution starts. If the agent is about to blow past the budget on a single task, it forces a checkpoint instead of running up the bill silently. Pairs well with the billing system you built here.
Yeah, totally agree, budgeting is the missing control loop.
Per-token billing (what I built here with Kong AI Gateway + Konnect Metering & Billing) gives you accurate attribution, who/what actually consumed tokens. But by itself, it’s reactive.
A token budget adds a runtime guardrail. For agent flows, that means checking expected token usage before each step and stopping or degrading (smaller model, less context, fewer tool calls) instead of silently overspending.
In practice, you need both:
metering for visibility, budgets for control.