Scaling Without Breaking: Handling Traffic and Latency in AI Workflows
When the first real trading bot found my x402 API and started calling it every 5 minutes for Korean price data, I had a brief moment of excitement followed by a longer moment of anxiety. What happens when there are 10 bots calling every 5 minutes? What about 100? Is a single free Oracle instance going to handle that? The honest answer: I don't know yet. I haven't hit a scaling wall. But thinking through where the bottlenecks will appear — and building the architecture to delay them as long as possible — is work I've done. This post is about that work, with clear markers for what I've tested in production versus what I've planned but not stress-tested. What This Post Covers The specific scaling patterns I use across my projects, why caching is the single biggest performance lever for AI-powered APIs, how async processing prevents slow AI calls from blocking fast endpoints, the latency numbers I actually see in production, and the line between "scaling fo...