LLM API Rate Limits: How to Design Around Quotas and Traffic Spikes
Rate limits are one of the first production issues AI teams hit. A feature works in testing, then fails when customers, background jobs, or batch workflows create traffic spikes.
Designing around rate limits keeps your AI product reliable.
Types of limits
Providers may limit:
- requests per minute
- tokens per minute
- concurrent requests
- daily spend
- model-specific usage
- account-level quota
Understand all limits, not just request count.
Queue background jobs
Batch tasks, document processing, and embeddings should usually run through queues. Queues smooth traffic and avoid sudden provider spikes.
Retry carefully
Retries can make rate-limit problems worse. Use:
- exponential backoff
- jitter
- retry caps
- error-specific retry rules
- fallback providers
Do not retry invalid requests.
Add customer-level quotas
If one customer can consume all provider capacity, every other customer suffers.
Apply limits per:
- tenant
- user
- API key
- plan
- feature
Use fallback providers
When one provider is rate limited, route some traffic to another model or provider. This requires compatibility testing before the incident happens.
Final thoughts
Rate limits are normal in LLM systems. Use queues, backoff, quotas, fallback, and routing to keep traffic predictable and customer experience stable.