A feature platform is being considered by teams that are in the middle of a massive transformation of their data stack, including laying the foundation for machine learning by moving to new streaming or batch data sources, transitioning to a microservices architecture, adding data quality monitoring, and introducing new CI/CD processes for models. A feature platform can unlock significant value if the team wants to make predictions in real-time, use extremely fresh data to make predictions, has an ML team spending too much time maintaining and not enough time building, wants to scale to multiple ML use cases, or wants to future-proof their ML program. On the other hand, a team may not need a feature platform if they only want to use machine learning for offline analytics with batch historical data, have a limited number of ML use cases that are easy to maintain, haven't laid a foundation of digital modernization, or don't see many opportunities for growth through expanding their use of ML.