Amazon SageMaker Feature Group
Does ZopNight manage Amazon SageMaker Feature Group?
Feature groups bill on two stores at once: the online store meters reads, writes, and per-GB storage for low-latency serving, while the offline store accrues S3 storage indefinitely. ZopNight discovers feature groups on the 6-hour cycle, tracks both costs from Cost Explorer or CUR 2.0, and flags groups whose models are gone.
Rules that fire on Amazon SageMaker Feature Group
No active rule family targets Amazon SageMaker Feature Group today. Rules that used to are retired, and retired rules publish no pages and fire no findings. Scheduling and permissions coverage are unaffected.
At a glance
| Field | Value |
|---|---|
| Scheduling notes | discovery, cost tracking, and recommendations only. |
A SageMaker Feature Store feature group stores curated ML features, billed for online store reads/writes and offline store storage. Feature groups that outlive their models keep charging for storage and provisioned throughput.
Two stores, two ways to pay
A feature group can maintain an online store for low-latency inference lookups, metered on reads, writes, and per-GB storage, with a provisioned-capacity mode that bills for configured throughput whether consumed or not. A feature group can also maintain an offline store, an S3-backed history that accrues per-GB-month storage as long as ingestion continues. The two meters fail differently. Online-store waste is throughput-shaped: provisioned reads and writes for models that stopped serving. Offline-store waste is sediment-shaped: append-only feature history growing monthly, long after the last training job read any of it.
Feature groups against their consumers
ZopNight discovers feature groups automatically on the 6-hour cycle, with storage and throughput cost tracked from Cost Explorer or CUR 2.0. The unused-feature-group recommendation reconnects features to their reason for existing: a group whose consuming endpoints are deleted and whose offline store no training job has queried in months is infrastructure serving a model that no longer exists. Provisioned online stores make the finding urgent, since capacity-mode billing continues at the configured rate regardless. Pure offline groups make it chronic instead, a storage line growing quietly forever.
The pipeline that never stops feeding
Feature engineering pipelines are the leak’s engine: ingestion jobs keep writing fresh feature values on schedule because nothing downstream tells them to stop. The model was retired; the pipeline’s owner changed teams; the feature group keeps growing and, in provisioned mode, keeps billing writes for data with no reader. Duplicate groups compound it, as reorganized teams re-create features under new naming conventions and leave the old generation ingesting in parallel.
Feature Store housekeeping
The SageMaker console’s Feature Store section lists groups with their online/offline configuration and creation dates. The audit question per group is a join: which pipelines write to it, which endpoints or training jobs read it, and whether the read side still exists. Write-only groups are the cleanup list.