Average traffic and peak traffic are different planning problems
A rate limit calibrated against average daily request volume looks generously sized on a normal day and then throttles real users the moment traffic concentrates — during a product launch, a marketing push, a viral moment, or simply the predictable daily peak hour most consumer and business applications see. The gap between average and peak is often a multiple of 3–5x or more, not a small margin.
Why teams under-provision for peak by default
A rate limit set during initial development, when traffic is low and mostly synthetic, tends to get validated against the traffic the team is actually seeing at that moment — which is rarely representative of the peak the same feature will see once real users depend on it at scale.
Cost projection has the same blind spot
A monthly cost estimate built from current average daily volume understates the actual cost trajectory once a feature scales, and a team that hasn't run the arithmetic on cost-per-request explicitly can be surprised by a bill that scales linearly with adoption in a way early testing never suggested.
What "headroom above peak" actually means in practice
A rate limit set exactly at estimated peak load leaves zero margin for a traffic estimate that's slightly wrong, or for a peak that runs a bit higher than the historical multiplier suggests — a deliberate headroom margin (commonly 20% or more above the estimated peak) is what actually prevents throttling in the real, imperfectly-predicted traffic pattern a launch or growth event produces.
Running the actual numbers beats guessing
"We'll raise the limit if it becomes a problem" is a reactive plan that fails precisely at the moment — a traffic spike — when reacting quickly is hardest. Running the cost and capacity arithmetic ahead of time, with your team's actual volume and pricing numbers, turns a reactive scramble into a number you can plan and budget around before it's needed.
Frequently Asked Questions
Traffic concentrates around specific hours, launches, or marketing events rather than spreading evenly across a day — a peak multiplier of 3-5x average (or more, depending on the specific traffic pattern) is common, which is why sizing a rate limit off average volume alone tends to under-provision for the moments that matter most.
A limit set exactly at estimated peak leaves no margin for the peak multiplier being slightly off or for real traffic occasionally exceeding the historical pattern — headroom is what actually prevents throttling under the real, imperfectly-predicted variability of live traffic.
It computes real cost from your actual token volume per request and your blended price per million tokens, not just a request count — since cost for most LLM APIs scales with tokens processed, not requests alone.
It's most useful before a launch or rollout — plugging in a projected volume and peak multiplier for the expected event lets you size cost and rate limits ahead of time rather than reacting once real traffic reveals a problem.
No. All calculation happens locally in your browser — your volume, pricing, and rate-limit numbers are never uploaded or logged.