Feature stores have emerged as a critical component for managing, serving, and reusing features across different models and teams in the world of machine learning (ML). A feature store is a centralized repository that standardizes the storage, retrieval, and sharing of features. This helps maintain consistency between training and inference, reduces redundant computation, and enables collaboration across teams. Popular feature store options include Feast, Feathr, Hopsworks, AWS SageMaker, and GCP Vertex AI, each with its strengths and considerations for different scale, cost, and performance requirements. The architecture of a feature store consists of several key components, including the offline store, online store, registry, and server, which work together to provide a centralized management system. The dual-layer design of offline and online stores is critical in resolving fundamental tensions in production ML systems, where training requires reproducible historical features with time-travel capabilities, while serving demands sub-10ms access to the latest values. Evaluating offline and online store options involves considering factors such as scale, latency, and operational efficiency, with databases like BigQuery, DuckDB, ScyllaDB, and Dragonfly offering unique strengths for different use cases. In practice, feature stores unlock the ability to build real-time ML applications efficiently, powering systems like real-time bidding and fraud detection, and enabling personalized recommendations at scale.