In my opinion, a team truly needs a Feature Store when machine learning moves beyond a few experimental models and starts becoming a production system with multiple models, shared datasets, and different teams working in parallel, because that’s when feature duplication, inconsistency, and training–serving mismatches start becoming real problems. At that stage, manually managing features becomes inefficient and error-prone. The most important benefits in practical use cases are consistency between training and production (which directly improves model reliability), feature reuse across multiple models (which saves a lot of engineering effort), and centralized governance with versioning (which helps maintain control and traceability as systems scale). Real-time feature serving is also critical for use cases like fraud detection or recommendation systems where low-latency predictions matter. Overall, a Feature Store becomes truly valuable when ML systems need to scale reliably, consistently, and collaboratively across the organization.