JetGraph vs feature stores

Tabular features and graph signals are different jobs

A feature store materializes columns keyed by entity ID. JetGraph materializes a graph and answers relationship questions that are awkward as columns.

What feature stores do well

Point-in-time correctness for training/serving tabular features
Batch and stream pipelines from warehouses
Model-feature registry, backfills, and offline/online parity

What they typically do not do well

First-time relationship checks against a live edge set
Flag-one-node, update-all-neighbors without a custom job
Ad hoc one-hop Cypher at authorize time

How they coexist

Event
  → JetGraph   (graph signals)
  → Feature store (bureau, device ML, aggregations you already run)
  → Model / rules
  → Decision
  → Write back to JetGraph (and to logs / OLTP)

JetGraph is closer to a graph feature engine than to Feast/Tecton. It does not replace your training warehouse.

See Why JetGraph and risk scoring.