[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-feature-store::en":3,"gloss-cluster-feature-store::en":23,"gloss-next-feature-store::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"feature-store","data-infra","Feature Store","A feature store is a central system for defining, storing, and serving the input features that machine-learning models consume. A \"feature\" is a computed signal — a user's 30-day purchase count, average session length, days since signup. The feature store computes these once and makes them available both for training models (offline, in bulk) and for real-time inference (online, low latency).\n\nThe core problem it solves is training\u002Fserving skew: if your training pipeline and your production app compute the same feature slightly differently, the model behaves worse in production than in tests. A feature store enforces one definition used in both places.\n\nFor SaaS builders adding ML — churn prediction, recommendations, fraud scoring — a feature store is worth it once features are reused across multiple models or teams. Tools include open-source Feast and managed offerings from Tecton and the cloud vendors. Practical note: you likely don't need one for your first model. Start with straightforward pipelines; adopt a feature store when duplication, freshness, and consistency of features become a real bottleneck.","A feature store defines, stores, and serves the input features ML models consume — computing each signal once for both training and low-latency inference.",null,[11,14,17,20],{"slug":12,"name":13},"data-pipeline","Data Pipeline",{"slug":15,"name":16},"embedding","Embedding",{"slug":18,"name":19},"reverse-etl","Reverse ETL",{"slug":21,"name":22},"vector-database","Vector Database",[24,28,31,34,37,41,44,47,50,53,57,60],{"slug":25,"category":5,"name":26,"updated_at":27},"acid","ACID","2026-08-24T02:46:37+00:00",{"slug":29,"category":5,"name":30,"updated_at":27},"ann-search","ANN Search",{"slug":32,"category":5,"name":33,"updated_at":27},"backpressure","Backpressure",{"slug":35,"category":5,"name":36,"updated_at":27},"batch-processing","Batch Processing",{"slug":38,"category":5,"name":39,"updated_at":40},"bm25","BM25","2026-08-24T02:46:38+00:00",{"slug":42,"category":5,"name":43,"updated_at":27},"cache","Cache",{"slug":45,"category":5,"name":46,"updated_at":27},"cap-theorem","CAP Theorem",{"slug":48,"category":5,"name":49,"updated_at":27},"change-data-capture","Change Data Capture (CDC)",{"slug":51,"category":5,"name":52,"updated_at":27},"chroma","Chroma",{"slug":54,"category":5,"name":55,"updated_at":56},"chunk-overlap","Chunk Overlap","2026-08-24T03:30:02+00:00",{"slug":58,"category":5,"name":59,"updated_at":27},"columnar-storage","Columnar Storage",{"slug":61,"category":5,"name":62,"updated_at":27},"connection-pooling","Connection Pooling"]