Rate Limits, Retries and Backoff
Handle 429s and overloads gracefully with backoff, jitter, client side pacing and retries that never cause storms.
A taste of a lesson
During our launch, 429s got worse after we added retries. How is that possible?
Your retries probably fired in sync and without enough waiting, so every failed request came back almost immediately, adding load exactly when you were over the limit. Check three things: that waits grow exponentially, that you add random jitter so clients spread out, and that you are not stacking your retries on top of the SDK's own, which multiplies attempts. Then add pacing so you send below the limit in the first place. How many attempts does your SDK make by default?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Describe request, token and concurrency limits and read limit headers
- Retry only transient errors with capped exponential backoff and jitter
- Prevent retry storms and multiplied retries across layers
- Pace requests proactively with a token bucket or worker queue
- Keep retries from repeating side effects such as emails or writes
Lesson plan
- 1 How limits work Understand the kinds of limits providers apply and how they show up. Start
- 2 Retry the right failures Separate transient errors from errors that retrying cannot fix. Start
- 3 Backoff and jitter Implement capped exponential backoff with jitter and attempt limits. Start
- 4 Avoiding retry storms Stop retries from making a struggling service worse. Start
- 5 Pacing instead of bouncing Stay under limits proactively with pacing and queues. Start
- 6 Retries and side effects Make sure retried work never duplicates actions. Start
Try asking
About this tutor
For developers whose LLM features fail under load, launch day traffic or large jobs. You learn how providers limit requests and tokens per time window, how to read limit information from responses, and how to retry correctly: which errors to retry, exponential backoff with jitter, respecting Retry-After, capping attempts and total time, and keeping retries from multiplying across layers. Then you go further with client side pacing, queues and concurrency limits so you stay under limits instead of bouncing off them, and with care for side effects so retries never send an email twice.
Reviews
4.7
3 ratingsSample
- Oskar B.Sample
We were retrying inside an SDK that already retried three times. Nine attempts per failure. Fixed that and added jitter, and our next big job ran clean.
- Precious N.Sample
The token bucket lesson was the most useful part. Our side effect issue with duplicate emails was covered too. A bit fast in the middle.
- Akira H.Sample
Clear timelines for what happens when many clients retry. I finally understand why jitter matters.
About the teacher
Keeps LLM features fast, affordable and available as traffic grows: cost, caching, retries and observability
9 tutors 347 lessons taught Sample
Most of my working life has been on platform and reliability teams, and these days I spend it on LLM features: the bills that surprise people, the 429 errors on launch day, the logs nobody can read. I teach the operational side of building with model APIs. We estimate costs before writing code, add retries that do not make outages...
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