LLM API Rate Limits and Retries: The 2026 Survival Guide
LLM API rate limits explained: 429 errors, retries with backoff, quota planning, and multi-provider fallback so your app never stalls.
Rate limits are the ceiling on how fast you can call an LLM API. Hit them and you get 429s; ignore them and your app stalls. The fix is a combination of smart retries, backoff, queues, and sometimes a second provider.
What rate limits are
Providers throttle by requests per minute and tokens per minute, separate from monthly cost caps. You can be under budget yet still rate-limited on bursts.
Handling 429 responses
- Read the Retry-After header if present.
- Retry with exponential backoff and jitter.
- Cap total retries to avoid hammering the API.
- Queue requests and batch where the API supports it.
- Fall back to another provider if configured.
Backoff formula
Quota planning
- Budget for peak, not average, if bursts matter.
- Pre-warm tokens when possible.
- Use async/batch endpoints for heavy workloads.
- Separate interactive (low) vs background (high) limits.
Multi-provider fallback
A second provider absorbs rate-limit spikes and gives resilience. Route around a throttled provider with equivalent models — this is where an OpenAI-compatible gateway simplifies your code.
FAQ
Why do I get 429 errors from LLM APIs?+
You exceeded the provider's requests-per-minute or tokens-per-minute limit. Check your tier and burst patterns.
How long should retries wait?+
Start small (≈1s) and back off exponentially with jitter, capping around 60s. Respect Retry-After when provided.
Can I avoid rate limits entirely?+
Not entirely, but you can minimize them with queuing, batching, lower burst peaks, and a second provider as fallback.
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