Rate limiters are supposed to protect your API, but they often introduce surprising latency spikes. In this episode, Lucas and Luna dig into a real-world case: a SaaS company that added a Redis-based rate limiter and saw p99 response times jump from 120ms to over 900ms. They trace the problem to a subtle queue buildup under burst traffic, then walk through three practical solutions — sliding window counters, token buckets at the edge, and careful placement of the limiter in the request pipeline. If you've ever wondered why your API slows down after adding 'protection,' this episode gives you the concrete debugging steps and the specific trade-offs each fix carries. No theory without numbers: the episode opens with a specific latency chart from production and closes with a forward-looking question about AI inference rate limiting.

#RateLimiting #APIPerformance #BackendEngineering #Redis #Latency #NodeJS #WebPerformance #SystemDesign #Scalability #BurstTraffic #TokenBucket #SlidingWindow #TechPodcast #WebDev #SoftwareEngineering #FexingoBusiness #BusinessPodcast #Technology

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