As large language model (LLM) services become increasingly popular, managing concurrency and preventing overload is a critical challenge. This article details a production-ready solution using OpenResty and Lua to implement precise rate limiting and automatic fallback mechanisms. The approach leverages OpenResty's high-performance event-driven architecture to handle thousands of requests per second while applying fine-grained rate limits based on user or API key. When limits are exceeded, the system gracefully degrades by queuing requests or returning fallback responses, ensuring service stability. This pattern is essential for any organization deploying LLMs at scale, as it prevents resource exhaustion and maintains a consistent user experience. The technical depth and practical focus make this a valuable resource for backend and infrastructure engineers.
This article presents a practical implementation of rate limiting and automatic fallback for large language model (LLM) services using OpenResty and Lua. It addresses the critical need for precise concurrency control and overload protection in AI deployments. The solution is valuable for backend and infrastructure engineers building scalable AI systems.