Feature Platforms as Reliability Systems: A Taxonomy, Control Matrix, and SLI/SLO Playbook

Abstract

Feature platforms fail in ways that standard service monitoring does not capture. A platform can be available while models degrade because of stale values, online-offline mismatch, join drops, or semantic drift. This paper treats the feature platform as a reliability system with explicit objectives for correctness, freshness, and availability. We contribute a compact failure taxonomy, a set of reliability gates that turn contracts and measurements into release decisions, and a starter SLI/SLO playbook with an error-budget policy. We also report empirical results from a local benchmark that injects common faults in a Feast and Redis stack and measures detection latency and sampling overhead.