HIGH
AI Rate Limiting & Abuse Prevention
Protecting production AI endpoints from abuse, token-stuffing attacks, prompt injection at scale, and runaway costs requires rate limiting strategies specifically designed for AI workloads where a single request can consume vastly different amounts of compute. Traditional API rate limiting based on request count is insufficient — AI endpoints need token-aware limits, cost-based quotas, and behavioral analysis to detect sophisticated abuse patterns. When evaluating solutions, assess their support for multi-dimensional rate limiting (requests, tokens, cost), per-user and per-application quotas, adaptive limits based on usage patterns, abuse detection algorithms, and graceful degradation strategies that maintain service for legitimate users during attack conditions.