Feature works for few users breaks at scale

This usually means the feature was built for controlled usage, not production pressure. At small volume, assumptions stay hidden. At scale, those assumptions become expensive because latency, load, edge cases, and failure paths all show up at once.

Scaling problems are often not about model quality alone. They can come from infrastructure limits, queueing issues, dependency bottlenecks, or workflows that were never designed for broad usage.

A feature that works for a few users but breaks at scale needs stress testing, performance visibility, and operational redesign. The model may be fine, but the system around it is not ready yet.