An AI service rate limit controls usage over time. Limits can apply to requests per minute, tokens per minute, concurrent runs, daily quotas or other units, and they may vary by customer and model.
Applications should handle limits with queues, backoff, caching and alternative models where appropriate. A free service with strict or changing limits may be suitable for experiments but unreliable for production demand.
ELI5
A service rate limit restricts how much of an AI service a user or application can consume during a period. It may limit requests, tokens, concurrent jobs, or daily usage and can vary by model or customer plan.
For example, an API may allow 100 requests per minute and reject additional calls until capacity becomes available. Applications can use queues, careful backoff, caching, or an approved alternative model instead of immediately retrying and making congestion worse.
Why do AI providers use rate limits?
Limits protect capacity, control abuse, manage fair access and align usage with service tiers or commercial plans.
How should an application handle model API rate limits?
Use bounded retries with backoff, queues, clear user feedback and tested fallback behavior when appropriate.
