• Developed distributed rate limiting system using Token Bucket algorithm with Redis and FastAPI to prevent API abuse across 3 configurable tiers (100-10,000 req/min); enables horizontal scalability by sharing state across multiple server instances through centralized Redis cache • Built async Python microservice with 73% automated test coverage using pytest, validating rate limiting behavior under load; implemented retry logic with exponential backoff and health monitoring for production reliability • Designed RESTful API returning proper HTTP status codes (429 for rate limits, 400/500 for errors); containerized with Docker and deployed to cloud (Render) with documented architecture enabling reproducible deployment across environments Distributed Rate Limiter System Deployed link, add "/docs" at the end of url to open swagger ui then use custom inputs and check the system. Link: https://distributed-rate-limiter-system.onrender.com/ GitHub: https://github.com/ragbendra/rate-limiter-system.git