Built for Mass Scale: Hard-Won Lessons from Teams Running High Volume Inference Workloads in Production

Moving AI from a flashy demo to a high-volume production environment is a transition filled with hidden technical debt and infrastructure challenges. There’s a difference between calling the OpenAI API in a weekend prototype and serving 50,000 concurrent users who need sub-200ms latency, graceful fallbacks, and reliable output every single time. It is rarely a “model problem.” Instead, it is a problem of decisions, trade-offs, and architecture.