The previous four posts in this series covered the three architectural pillars of real-time AI at scale: feature pipelines, feature stores, and vector search. Each post addressed the design decisions and failure modes specific to one layer of the stack.
This final post is about the layer that sits above all of them: operations.
You can design a technically sound pipeline, a well-structured feature store, and a carefully maintained vector index — and still have a system that's difficult to run in production, slow to recover from failures, and chronically unclear about whether it's actually working. The difference between a system that's architecturally sound and one that's operationally mature is the difference between a system that was designed and one that was operated.
This post is about what operational maturity looks like for real-time AI systems: how to define what "working" means, how to know when it isn't, and how to recover when things go wrong.
Start With the SLA: What Are You Actually Promising?
