Rate Limiting

Rate limiting is a mechanism that restricts the number of API requests a client can make within a defined time window. For AI services, rate limits may be expressed as requests per minute, tokens per minute, tokens per day, or concurrent request limits. AI providers implement rate limiting to protect their infrastructure from overload, manage resource allocation across customers, and enforce usage tiers tied to pricing plans. When a client exceeds the rate limit, additional requests are typically rejected with a 429 (Too Many Requests) HTTP status code until the limit resets. For organisations building AI applications, implementing rate limiting on their own AI services serves similar purposes: controlling costs, preventing individual users or applications from monopolising resources, protecting backend services from overload, and detecting potential abuse or security issues.

Rate limiting is a critical cost control mechanism for AI operations. Without rate limits, a single misconfigured application, runaway loop, or malicious actor could generate enormous API costs in minutes. Rate limiting provides a safety net that prevents bill shock and ensures budgets are respected. For customer-facing AI products, rate limiting ensures fair access. Without limits, a small number of heavy users could degrade service quality for everyone. Tiered rate limits align with pricing models, allowing different service levels for different customer tiers. Implementing rate limiting requires careful calibration. Limits that are too strict frustrate users and reduce the value of the AI service. Limits that are too generous fail to control costs or protect infrastructure. Monitoring actual usage patterns and adjusting limits based on data is the best approach.