Inferencing is where AI meets reality. It is the moment a model is applied to live data, in real time, under real constraints.
Unlike training, inferencing is tightly coupled to operational systems – which means performance, concurrency, and data access patterns become critical. This is where AI stops being theoretical and starts becoming a database problem.
For two decades, DBAs protected production databases from analytics workloads. AI agents have ended that truce and the consequences go deeper than most realise.
AI agents bypass dashboards and query databases directly. This article explains what that means for the architecture of enterprise data systems and systems of record.
Databases were designed for human-paced interaction – sessions, think time and deliberate intent. AI agents remove all three assumptions simultaneously.
AI inferencing is a database problem disguised as an AI problem. Real-time model workloads expose latency and concurrency demands that enterprise storage wasn’t designed for.
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