The alert went off at 2:17 AM. CPUThrottlingHigh . My phone buzzed on the nightstand. Before I could even open my laptop, another one hit:
GPUMemoryExhausted .
- Our real-time fraud detection API , the service protecting millions of dollars in transactions was down. Hard down. Every minute that passed, we were bleeding $15,000 in fraudulent charges.
- I watched in horror as our Kubernetes autoscaler, blind to the real problem, began spinning up new GPU nodes like a panicked gambler pulling a slot machine lever. In less than an hour, we had burned through $40,000 in emergency AWS spend.
We had fallen into the most expensive trap in modern engineering: we thought the answer to a scaling problem was more GPUs.
We were wrong. Completely wrong. Serving AI at scale isn’t a hardware problem; it’s an architecture problem. That outage was our rock bottom. The six-month rebuild that followed was our redemption. We now serve over 100,000 requests per second (RPS), and we cut our inference costs by 83%.
This is the playbook that saved us.