In my opinion, a Feature Store becomes increasingly necessary as ML teams grow and start managing multiple models, datasets, and teams working in parallel, because it brings consistency, reusability, and better collaboration to the feature engineering process. For small teams with only one or two models, it may not be essential since features can often be managed manually, but as the number of projects increases, duplicated transformations, inconsistent definitions, and training-production mismatches become common problems. A Feature Store helps solve these issues by centralizing feature definitions, supporting versioning, governance, and real-time serving, which improves reliability and speeds up model development. It usually becomes worth adopting when an organization reaches the stage where multiple teams need shared features, deployment complexity is increasing, or production models require low-latency access to trusted features. Overall, it may not be mandatory in the early stages, but for growing ML operations, it often becomes a valuable investment for long-term scalability and efficiency.