How Stripe & Cloudflare Rate Limit Millions of Requests (System Design)

Hey everyone, In this video, I walk through how to design a Rate Limiter that actually holds up at scale — from the core algorithms to distributed state management and how real systems like Stripe and Cloudflare solve the hard problems. What algorithm should you use — Token Bucket, Leaky Bucket, Fixed Window, or Sliding Window? Where does state live when you have 20 servers? How do you fix race conditions with Redis atomic operations and Lua scripts? And what happens when you go multi-region? This is the system design playbook for building a production-grade distributed rate limiter: Token Bucket rate limiting, Leaky Bucket architecture, Fixed Window Counter trade-offs, Sliding Window approximation, Redis rate limiting with atomic INCR, Lua scripts for atomicity, distributed rate limiting across regions, sticky sessions vs eventual consistency, and how Cloudflare and Stripe architect rate limiting at scale. We cover real trade-offs in rate limiter system design — correctness vs latency, centralized vs distributed Redis, strict limits vs approximate limits — focusing on the architecture layer that keeps APIs protected and scalable. Perfect for backend engineers, system design interview prep, and anyone building APIs at scale. Follow Me : Works: https://rustix.in/ LinkedIn:   / rustix069   X: https://x.com/rustix69 GitHub: https://github.com/Rustix69 Hashtags : #RateLimiter #SystemDesign #BackendEngineering #DistributedSystems #Redis #APIDesign #TokenBucket #SlidingWindow #SystemDesignInterview #RateLimiting #APIRateLimiting #BackendArchitecture #ScalableArchitecture #SoftwareEngineering #RedisCache