API Webhook Retry Cost Forecast
Quantify how webhook retries inflate traffic and operating costs. Supply your daily event count, observed failure rate, and configured retry attempts to surface total call volume, expected success percentage, residual failures, and the infrastructure cost of replays. Optional inputs convert per-call costs and customer value into cash impact while sizing worst-case delivery delays from exponential or fixed backoff settings.
Operational planning aid only. Validate retry strategies, cost assumptions, and customer impact with your engineering and finance teams before adjusting production systems.
Examples
- 50,000 events, 18% failure rate, 1 retry, 120-second backoff, $0.0014 per call, $3.80 per success ⇒ Total webhook attempts per day 59,000 (9,000 retry calls) • Expected successful deliveries 48,380 of 50,000 (96.76%) • Residual failures after retries 1,620 event(s) • Retry traffic lift 18.00% • Processing cost at $0.0014 per attempt: $82.60 USD • Revenue at risk from residual failures: $6,156.00 USD • Worst-case retry window: 2.00 minutes (0.03 hours).
- 12,000 events, 5% failure rate, 2 retries, optional fields blank ⇒ Total webhook attempts per day 13,200 (1,200 retry calls) • Expected successful deliveries 11,999 of 12,000 (99.99%) • Residual failures after retries 1 event(s) • Retry traffic lift 10.00% • Processing cost at $0.0010 per attempt: $13.20 USD • Worst-case retry window: 1.00 minutes (0.02 hours).
FAQ
How can I model exponential backoff?
Enter the average delay between attempts or rerun the calculator with the longest delay you allow to see the worst-case delivery window.
Does this include dead-letter queues?
No. The tool stops at the configured retry count. Model DLQ handling separately based on how you remediate failed events.
What if retries use jitter?
Use the expected or maximum backoff interval in the optional field so the worst-case delivery window reflects your policy even with jitter applied.
Can I compare provider SLA penalties?
Yes. Multiply the residual failure count by any SLA credit per event externally to see the exposure alongside the revenue-at-risk output.
Additional Information
- Assumes independent retry probability—each attempt has the same failure rate entered.
- Additional attempts equal failed first deliveries multiplied by the retry count; queues or DLQs are not modeled beyond the configured retries.
- Worst-case retry window multiplies the number of retries by the backoff seconds entered; adjust if your strategy uses exponential backoff.