Every AI API has rate limits. You will hit them. The only question is whether you planned for it.
A rate limit is a ceiling on how many requests you can make in a given time window. Exceed it and the API returns a 429 error. Without handling, that 429 propagates to your user as an error. With handling, they never know it happened.
Before: no rate limit handling
Your code calls the API directly. When the API returns 429, this throws an error. Your application crashes or returns an error to the user. At scale, this happens constantly.
After: exponential backoff
The wait doubles on each retry. After three attempts, it gives up and surfaces the error — but 95% of rate limit errors resolve within the first retry.
The smarter approach: request queuing
For applications with variable load, a request queue prevents rate limit errors before they happen. Incoming requests enter the queue. The queue releases them at the rate your tier allows.
Checking your limits
Rate limit information is in your API dashboard and in response headers. The headers show how many requests remain in the current window and when the window resets.
Start here: Add exponential backoff to every API call in your application. It is ten lines of code and prevents the most common production failure mode.