Your checkout dashboard is calm at 10 AM, then a release lands and failed payments suddenly climb. In a fintech app, a quiet overnight graph can hide a burst of tiny transactions. In an AI product, the warning might be a prompt-cost spike, a latency jump, or a cluster...
Your web app feels quick on a developer laptop. The first screen appears, the test account signs in, and the demo looks polished. Then real traffic arrives. Personalization calls compete with analytics, a recommendation service waits on a database query, a third-party...
A payments team opens its morning dashboard and finds a card-testing ring that started during the night. The fraud model ran in a batch window, so the alert arrived six hours after the attackers had tested cards, triggered declines, and moved on. A real-time system...
Friday evening, a checkout service starts taking twice as long to respond. The existing alert checks whether latency has crossed a fixed limit, so nothing fires. By Monday morning, the product team sees the commercial impact, support has a queue of complaints, and...
