Error rate rises
API errors, request latency, and token consumption increase at the same time.
RETRY STORM
Failed model calls can still multiply token cost
When error rate, latency, and token spend rise together, retries may be amplifying the bill.
TokenPilot helps teams connect failed calls, retry behavior, and token cost to identify retry storms before they become billing incidents.
RetryLayered retry behavior
FailFailed calls keep costing money
Retries protect reliability, but they can amplify AI spend
Every retry can become a new model call, and every model call creates new token consumption.
Without limits, circuit breakers, and cost-aware retry policies, failed requests can be multiplied by application code, gateways, queues, and agent frameworks.
Typical incident signals
Failed calls repeat
The same task produces repeated failed calls and a rising failed-call cost share.
Traffic is misread
Cost grows without matching successful business output because failure is being amplified.
What should be tracked?
- API
- Status code
- Error reason
- Retry count
- Retry interval
- Call chain
- Upstream trigger
- Request ID
- Failed-call token cost
How TokenPilot identifies retry storms
TokenPilot links token consumption with API status, error codes, retry counts, and call chains.
When an API shows synchronized increases in errors, latency, and token cost, teams can identify retry storm risk and decide whether to rate-limit, break, or adjust retry strategy.
Do not keep paying for failed calls
If your system uses LLM APIs, agent frameworks, or automated task queues, retry storm risk should be visible in the cost layer.
Get a retry storm diagnosis