Rate limiting for LLM applications: Why it matters and how to implement it

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 consistent policies across providers, teams, and API keys, handle fallback routing when limits are hit, and provide observability through dashboards and alerts. Portkey's AI Gateway is presented as a solution for unified control plane management.