Hey everyone! I'm an experienced backend and infrastructure engineer with around 20 years in the field. Currently, I'm working on a tool aimed at solving some tough problems around API governance, rate limits, and anomaly alerts, all rolled into one service. The objective is to help developers catch issues like runaway cron jobs, infinite webhook loops, buggy clients, and unexpected spikes in API or cloud costs before they become problematic. This tool is not just another AI chatbot, nor is it merely focused on metrics or generic solutions like Nginx rate limiters.
What I'm envisioning is a solution that emphasizes real-time enforcement, tailored policies per tenant or route, hard and soft limits, plus built-in alerts and audit trails. Think of it as a strict traffic cop for your APIs, aimed at controlling costs and preventing abuse.
Before I get too deep into development, I'd like your candid feedback on this idea. Have you ever faced runaway API usage or been hit with an unexpected bill? How do you currently protect against these issues? And what must-have features would you look for in such a service? No selling here, just trying to gather honest opinions!
1 Answer
We built something like this at BlueTalon, but it was focused on data access rather than API limits. One challenge was getting teams to actually define what 'normal' usage looked like for their APIs. Everyone wants protection from high costs until the conversation turns to specific thresholds, and then it's a mess of excuses. The anomaly detection aspect is crucial too; if you don't have smart baselining, you risk alert fatigue, especially during busy times like product launches. I'd want granular control on what happens when limits are hit – hard blocking is rarely ideal. Sometimes you need to throttle requests, redirect them, or just log and alert without cutting off service suddenly. Also, it's vital to ensure that your rate limiter is fast enough not to create new bottlenecks itself.
Totally get what you mean about the 'normal' usage problem! Starting with an observation-only mode sounds like a solid approach. It allows gathering data without pressure to define limits immediately. Also, your thoughts on anomaly detection resonate. Context-aware baselining is a must to prevent false alarms during those peak times. And yes, having graceful degradation options instead of hard blocks is key! My go-to is usually throttle first, then queue if needed.