{"id":2464,"date":"2026-06-22T12:45:49","date_gmt":"2026-06-22T12:45:49","guid":{"rendered":"https:\/\/imesh.ai\/blog\/?p=2464"},"modified":"2026-06-22T12:46:00","modified_gmt":"2026-06-22T12:46:00","slug":"usage-based-rate-limiting-in-envoy-ai-gateway","status":"publish","type":"post","link":"https:\/\/imesh.ai\/blog\/usage-based-rate-limiting-in-envoy-ai-gateway\/","title":{"rendered":"Usage-Based Rate Limiting in Envoy AI Gateway\u00a0"},"content":{"rendered":"

AI adoption is accelerating across industries. Organizations are integrating Large Language Models<\/a> (LLMs) into customer support platforms, internal copilots, document processing systems, software development workflows, and agentic AI applications. While this unlocks tremendous business value, it also introduces a new operational challenge: unpredictable AI costs.\u00a0<\/p>\n

Unlike traditional APIs, AI workloads are not charged based on the number of requests. They are charged based on the number of tokens consumed. A simple prompt might consume only a few tokens, while a complex AI-generated report can consume thousands. Yet most traditional rate limiting systems treat both requests exactly the same.\u00a0<\/p>\n

This creates a governance gap. Teams can easily exceed budgets, shared AI resources can become monopolized by a few users, and finance teams often struggle to predict monthly AI spending.\u00a0<\/p>\n

So, how do organizations enforce meaningful AI consumption policies?\u00a0<\/p>\n

This is where Usage-Based Rate Limiting in Envoy AI Gateway becomes essential.\u00a0<\/p>\n

In this blog, we’ll explore how Usage-Based Rate Limiting works, the architecture behind it, how token-aware enforcement is implemented using Redis, and how organizations can use it to build cost-efficient and production-ready AI platforms.\u00a0<\/p>\n

Video on Envoy AI Gateway Usage-Based Rate Limiting<\/h2>\n

In case you want the\u00a0video, here it is\u00a0<\/p>\n