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What causes a 429 error from an LLM API?

A 429 means you sent more requests or tokens in a rolling minute than your rate limit allows. Providers enforce at least two limits at once, requests per minute (RPM) and tokens per minute (TPM), and a 429 fires the moment you cross either one. It is a normal, expected response to bursting above your share, not a bug in your code or the model.

Why does my LLM app hit rate limits when my average usage is low?

Because rate limits are enforced against your instantaneous burst, not your average. A limit is a token bucket that refills at a fixed rate and stores about a minute of budget, so spending a minute's worth in two seconds empties it and the overflow gets a 429. Agentic workloads make this worse, since one user action can fan out into dozens of near-simultaneous calls that carry more tokens than a whole minute's budget.

What is the difference between RPM and TPM rate limits?

RPM caps how many API calls you may start per minute, counting each call as one unit regardless of size, while TPM caps how many tokens you may consume per minute across input and output. High-fan-out, low-token workloads trip RPM first, and token-heavy work like long-context RAG trips TPM first. TPM usually binds first for GenAI apps, and it charges you for the max_tokens you reserve, not just the output you receive.

How should you handle a 429 rate limit error?

Retry with exponential backoff plus jitter, and honor the Retry-After header when the provider sends one. The jitter is what stops many clients that failed together from retrying in lockstep and re-tripping the limit, and Retry-After tells you exactly when budget refills so you stop guessing. Better still, pace yourself with a client-side token bucket sized below the provider limit so most 429s never happen, and degrade to a smaller model or cached answer rather than showing a user an error.

Why is exponential backoff not enough without jitter?

Because plain exponential backoff makes every client that failed at the same instant retry at the same instant, so each retry wave is a fresh burst that re-trips the limit and the retries themselves become the overload. Jitter adds randomness to each wait so the retries spread across the window and arrive smeared instead of stacked, letting the bucket refill between them. AWS measured that jitter substantially reduces the contention that pure backoff leaves behind.

Should you use multiple LLM providers for failover?

Yes for surviving a full provider outage, but only as a last resort behind cheaper defenses, and only after the secondary passes the same eval gate as the primary. Different providers answer the same prompt differently, with different output formatting, tool-calling conventions, and refusal behavior, so a blind failover can silently regress quality or break a downstream parser. This consistency tax is why failover is the most expensive layer, used for outages rather than routine throttling.

Your LLM feature returns 429 errors during traffic spikes, but your dashboard shows average TPM utilization sitting around 45 percent. What is the most likely explanation?

Rate limits are enforced against your instantaneous burst against a token bucket, not your average utilization, so a short spike empties the bucket while the average stays low, A rate limit is a token bucket that refills at a fixed rate and holds about a minute of budget. Spend that budget in a burst and the bucket empties, so the overflow gets a 429 even though the average utilization stays well under the cap. This is exactly why average-based capacity planning fails for bursty LLM traffic: the limit is on your peak draw, not your mean, so you can be at 45 percent average and still drop a tenth of your calls.

A request has a 500-token prompt, and you set max_tokens to 4096 out of habit even though replies are usually short. How does this affect your TPM rate limit?

It reserves roughly 4596 tokens against your TPM per call, because the limiter estimates cost from input plus the max_tokens you reserved, not the real output, The limiter has to estimate a request's token cost before generation runs, and it cannot know the real output length in advance, so it charges you for the max_tokens ceiling you asked for plus the input. A 500-token prompt with max_tokens 4096 counts as about 4596 tokens against TPM even when the reply is two sentences. Trimming max_tokens to what the feature needs is a direct cut to your 429 rate at no cost.

Fifty of your calls all receive a 429 at the same instant. You retry them with plain exponential backoff (1s, 2s, 4s) and no jitter. What happens?

The fifty retries stay synchronized and land together on each wave, so every wave is a fresh burst that re-trips the limit, Plain exponential backoff makes every client that failed at the same instant wait the same delays and retry at the same instant, so the fifty calls retry in lockstep and each wave is another fifty-call burst hitting the same bucket. The retries become the overload, a thundering herd. Jitter fixes it by randomizing each wait so the retries spread across the window and the bucket refills between them, which is the result AWS measured in its backoff and jitter writeup.

Under sustained rate limiting, your primary provider keeps returning 429s. You want to fail over to a second provider. What must be true before that failover is safe?

The secondary must have passed the same eval gate as the primary, because a different model formats output, calls tools, and refuses differently, and a blind swap can silently regress quality, Two providers are not two copies of the same function. The secondary may format JSON differently, use a different tool-calling convention, refuse different requests, or reason to a different conclusion, so if your prompts, parsers, and evals were built against the primary, a blind failover can break a parser or quietly drop quality. That is the consistency tax, which you pay by running the secondary through the same eval gate first. Failover is also a last resort for a real outage, not a response to routine throttling that your client bucket, backoff, and degraded modes should absorb.

Rate Limits and Provider Failure: The Number One Production Error

Name: System Design Masterclass
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