Load shedding: 4 real scaling case studies
4 of the 73 engineering write-ups in our database are tagged with the technique "load shedding". They come from 4 companies (Discord, Meta, Stripe and Zalando) and were published between 2017 and 2026. Often combined with fan-out (2) and load balancing (2). 3 of the 4 state concrete scale numbers, for example "30 million+ WhatsApp messages/sec" (Meta). Each card below links to our short summary and the original post.
2026 World Cup: Breaking Records, Spiky Charts and Reliability at Scale
How Meta absorbed the 2026 World Cup, when WhatsApp set a record of more than 30 million messages per second during the final. Pre-provisioned capacity buffers were the most effective lever, alongside country-level shedding of non-critical load, load-testing and resizing 100+ services, forecasting each region's peak separately, and no deploy freeze.
Client-Side Load Balancing at a Million Requests Per Second
Zalando moved the internal fan-out of its Product Read API off the shared Skipper ingress into an in-process JVM load balancer that rebuilds the same xxHash64 consistent-hash ring. They added N-ring fade-in so new pods warm only the products they will keep, bounded load based on occupancy (Little's Law) weighted by latency, and a capped ring walk. Pods ran more evenly, the autoscaler target rose from 50% to 65% CPU, and the fleet shrank by over 25%.
How Discord Scaled Elixir to 5,000,000 Concurrent Users
Discord tuned its Elixir guild processes and fan-out paths, building custom libraries to push events to millions of concurrent users.
Scaling your API with rate limiters
The four kinds of rate limiters and load shedders Stripe uses to protect its API.