An integration that worked fine in testing with a handful of requests starts getting rejected under real load. The API is not broken — the request rate crossed an allowed threshold. Here is why limits exist at all, how to tell them apart from a genuine outage, how to smooth load on your side, and why polling status once a second usually does not get you an answer any sooner.

Why services cap request frequency

A limit protects shared infrastructure: without one, a single client running an aggressive script could soak up resources everyone else needs. It is also fair distribution of capacity — a per-account cap guarantees that someone else's integration cannot eat the throughput you paid for. Hitting a limit looks specific: the service answers with a 429 status and often attaches a header suggesting how long to wait before retrying. That is fundamentally different from an outage — a failure returns errors to every client across unrelated operations, while throttling targets your key specifically because of your own request rate. If the response carries a clear rate-limit code, the infrastructure is alive and working as intended — the problem is your pace, not the service.

A steady pace instead of bursts

Most limits are counted over a time window — a second or a minute. Fire a hundred requests in one burst and then sit idle, and the window's counter empties instantly, even if the average load over an hour is modest. The cap reacts to the peak, not the average. The same amount of work, spread evenly across the window, sails through without a single rejection. Smoothing your pace is not about sending fewer requests — it is about not bunching them into a narrow slice of time.

A queue and capped concurrency on your side

Work rarely arrives evenly: a batch can land all at once, say after a data import or a bulk user action. If every task fires an API call the moment it appears, a burst on your side becomes a burst at the service's edge. A queue with controlled dispatch fixes that mismatch — tasks pile up in the queue while a steady stream of requests goes out. It also pays to cap the number of concurrent connections to the API: even with a queue, ten threads hitting a rate-limited endpoint at once create the same spike as one unthrottled thread.

Status polling and backing off after a rejection

Polling status once a second is almost always wasteful: the result does not arrive faster because you asked more often. The underlying operation — SMS delivery, port setup, payment processing — is bound by network time and the service's own internal queue, and polling faster than that pace just burns your quota without pulling the answer closer. A sensible interval matches how long the operation typically takes, not how badly you want the result right away.

After a rate-limit rejection, do not retry immediately at the same pace — that all but guarantees a longer block. The right response is exponential backoff: each retry waits longer than the last, say one second, then two, four, eight. If the service returns a header naming a wait time, follow it instead of guessing.

Separate limits per key and watching the threshold

A limit is almost always tied to a key or account, not to the service as a whole. If a background sync job and a user-facing feature share the same API key, a heavy background task can eat the whole quota and leave a real user without an answer. Separate keys per task get separate counters — the same split used for the proxy API. It also pays to watch usage proactively: many APIs return the remaining quota in the response, and the dashboard shows how close you are to the threshold before the rejections start, not after the first 429.

Frequently Asked Questions

Can a rate limit be raised for a specific account?

Some services raise the cap with a plan upgrade or after a support request describing the load pattern. Do not count on it upfront — design the integration for the current limit and treat any increase as a bonus, not a plan.

Why does a 429 show up even at a low request rate?

A common cause is a shared key already spending part of the quota elsewhere. Another is a shorter window than expected: the per-minute average looks low, but requests spike within a single second. Check both before touching the integration's logic.

Is the limit the same across every API method?

Usually not: heavy operations like search or bulk lookups are capped tighter than a simple status check. Check the per-method limits table in the docs rather than assume one global number.

Exact limit values, error codes, and the retry-wait header are documented in the OTP API reference.