LLM workloads are inherently unpredictable due to variable token consumption, multi-step agent workflows, and provider-enforced limits. This guide covers rate-limiting strategies for LLM applications including request-based, token-based, cost-based, and time-window limits. It explains how a centralized AI gateway can enforce
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LLM applications make traffic unpredictableRate-limiting strategies for LLM applicationsImplementing rate-limiting using an AI gatewayTrack rate-limit and usage metricsFAQsSort: