Envoy AI Gateway Usage-Based Rate Limiting Explained | Control AI Costs & Protect LLM APIs

AI applications powered by LLMs can quickly become expensive and difficult to govern without proper controls. In this video, learn how Envoy AI Gateway enables usage-based rate limiting to control AI consumption, enforce quotas, prevent abuse, and optimize costs across multiple AI providers. You'll see how organizations can implement intelligent rate limiting policies for users, teams, applications, or tenants while maintaining performance and security for production AI workloads. 🎯What You'll Learn ✅What is usage-based rate limiting in Envoy AI Gateway ✅How to enforce AI consumption quotas ✅Protect LLM APIs from abuse and overuse ✅Control AI costs across teams and applications ✅Improve governance and multi-tenant AI security ✅Build production-ready AI platforms with Envoy AI Gateway Purpose This video demonstrates how to implement usage-based rate limiting in Envoy AI Gateway to secure AI workloads, optimize spending, and enforce enterprise AI governance. ⏱️ Timestamps ▶️ 00:00 - Introduction ▶️ 00:13 - Agenda of Usage-based Rate Limiting ▶️ 00:29 - What is Usage-based Rate Limiting ▶️ 01:04 - Architecture ▶️ 02:54 - Request Lifecycle ▶️ 03:50 - Rate Limiting Calculation based on Token Types ▶️ 04:53 - Budget Enforcer Demo ▶️ 06:09 - Demo Session Meant For Platform Engineers DevOps Engineers SREs Kubernetes Administrators AI Platform Teams Cloud Architects API Gateway Engineers Organizations building production AI applications Explore how Envoy AI Gateway helps enterprises securely scale AI adoption with observability, governance, and intelligent traffic management. 🔗For enterprise support and consulting on Envoy AI Gateway reach out to us at 👉 https://imesh.ai/enterprise-envoy-ai-... #EnvoyAIGateway #EnvoyGateway #AIInfrastructure #LLMOps #Kubernetes #PlatformEngineering #APIGateway #AIEngineering #GenerativeAI #CloudNative #RateLimiting #OpenSource #DevOps #AIGovernance #LLMSecurity #KubernetesAI #EnvoyProxy #AIPlatform #MultiLLM #AIOps