In this video, I show you how to add production-grade rate limiting to a Spring Boot app so one attacker can’t hog your entire system and burn your cloud CPU at 100%. The “green 200s” can be a lie—your API may still be melting under spam. The fix is simple: reject abusive traffic early with HTTP 429, before it wastes your CPU cycles. I focus purely on the Java implementation (not the dashboard), and I keep it practical so you can plug it into a real backend. We build a distributed rate limiter using Bucket4j + Redis, which is exactly what you want in microservice setups where multiple instances must share the same limits. I walk through adding the dependencies, configuring Redis + Bucket4j ProxyManager with a TTL strategy (so Redis doesn’t grow forever), implementing a RateLimitingService that creates token buckets (10 requests per minute for demo), and writing a custom OncePerRequestFilter that extracts the real client IP (X-Forwarded-For first) and consumes tokens. Finally, I register the filter in the Spring Security filter chain (before UsernamePasswordAuthenticationFilter) and test everything in Postman, including clean 429 JSON responses and useful headers like X-Rate-Limit-Remaining and retry-after seconds.