In this video I break down a scaling trick I don’t see people recommend enough: using Cloudflare Workers as a “load balancer” even though that’s not what it’s marketed as. The core idea is simple—put a Worker in front of your backend as the first line of defense, because Workers can handle an absurd number of requests without falling over. Your backend can be Node.js, Python, whatever. The Worker just proxies requests, and it’s cheap enough that you’ll genuinely question why more teams don’t do this. The one big gotcha is latency. By default, Workers run globally, and that can be terrible if your backend lives in one region—your Worker might execute far away and then make a long trip back to your server. The fix is Smart Placement (or explicitly pinning a region), which keeps the Worker close to the upstream that gets the most traffic. From there, I use a hard-coded list of backend IP:port targets and route each incoming request to a random target. Over time, randomness distributes load evenly, and you can add retries/timeouts so a dead instance (or even a deleted VM) doesn’t take your service down. The takeaway: you get scale, availability, and cost efficiency with minimal backend changes—just don’t forget your database pooling story as you add more processes.