Agency17 June 20264 min read
The infra failures nobody warns you about in a dockerized AI stack
The thing that takes down an AI system in production usually isn't the model or the app. It's the boring infrastructure underneath, and it fails green. Four real ones from a dockerized AI stack: a mount writing to the void, a runaway container, a tripped autoscaler, dead proxy routes.
The short answer
The failure that takes down a production AI system is rarely the model or the application. It is the boring infrastructure underneath, and it tends to fail green: a container mount that writes to the void while reporting success, a container that balloons until the host starves, an autoscaler that trips a circuit breaker, a proxy quietly serving dead routes. The fix is not a smarter model. It is treating the infra layer as a first-class part of the system you observe.
Short version: the thing that takes down a production AI system is almost never the model or the application code. It is the boring infrastructure underneath, and the worst of it fails green: it reports success while quietly losing data or starving the host. We run a dockerized AI stack for a fiduciary, and the failures that cost us real time were not exotic. They were mounts, memory limits, autoscalers, and proxy routes, each one happy to look healthy while doing the wrong thing.