Training-serving skew remains a top failure mode in production ML, and many teams suspect feature stores don't fully solve it. The core issue isn't poor implementation; it's that feature stores address data definitions, not execution. Skew arises from movement across system boundaries, where timing, code paths, and failure modes diverge, making consistency probabilistic rather than guaranteed.

Feature stores promise consistent definitions and reusable transformations, yet teams still see offline features mismatching online behavior and inference paths diverging from training logic. The problem is structural: training and serving operate in separate execution contexts with different query planners, permissions, and data access. Matching definitions don't guarantee matching execution semantics, leaving a gap where skew thrives.

A solution requires a shared execution layer where training and serving use the same query planner, permissions, and data. Instead of storing pre-computed artifacts, features become inline expressions evaluated at query time. This eliminates synchronization jobs and stale features, making skew a visible failure with a stack trace rather than a silent degradation in quarterly metrics.