# SageMaker Endpoint Non-Production Scheduling Opportunity

> Real-time endpoints bill their ML instances 24/7, and a dev/test endpoint used 8 hours a weekday is idle roughly two-thirds of the week. ZopNight quantifies the saving from each endpoint's measured weekly idle heatmap, never a flat assumption, and abstains for endpoints with no usage history.

Source: https://zop.dev/integrations/aws/recommendations/sagemaker-endpoint-non-production-scheduling-opportunity
Updated: 2026-08-19

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## No native off switch, and the rule is honest about it

Most schedulable resources stop and start. SageMaker real-time endpoints do not; the only
"off" is deleting the endpoint and recreating it from its saved configuration. That is why
this recommendation ships as advisory rather than a one-click schedule: the remediation
describes the delete-and-recreate cycle (EventBridge plus a small Lambda, or your CI),
keeping the EndpointConfigName so recreation is byte-identical each morning. The endpoint
config and model objects persist through the cycle; only the [hourly-billed instances](https://aws.amazon.com/sagemaker/pricing/) come
and go.

## Measured idleness or nothing

The saving is the endpoint's measured cost multiplied by the idle fraction its weekly
CPU heatmap actually shows: an endpoint idle 65% of the week saves 65%. Without heatmap
data for the specific endpoint there is no number worth printing, and the rule abstains
rather than assume a nights-and-weekends pattern. One measurement caveat runs in the safe
direction: [endpoint CPU is summed across vCPUs](https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html), so multi-core endpoints can read busier
than they are and the idle fraction under-measures. The rule under-fires or understates,
never inflates.

## The eligibility fence

Only dev/test-named, non-production-tagged, in-service endpoints on a single production
variant qualify. Prod names or tags veto outright; endpoints with any schedule-related tag
are assumed handled; Savings Plan or reservation coverage vetoes too, because tearing down
committed capacity saves nothing, since the commitment bills anyway. Multi-variant endpoints
abstain since the heatmap reflects only the first variant's activity, and deleting a whole
endpoint on one variant's silence could drop another variant's live traffic.

## See the weekly shape yourself

```bash
aws cloudwatch get-metric-statistics \
  --namespace /aws/sagemaker/Endpoints --metric-name CPUUtilization \
  --dimensions Name=EndpointName,Value=my-dev-endpoint Name=VariantName,Value=AllTraffic \
  --start-time "$(date -u -v-14d +%Y-%m-%dT%H:%M:%SZ)" \
  --end-time "$(date -u +%Y-%m-%dT%H:%M:%SZ)" \
  --period 3600 --statistics Average
```

Hourly datapoints over two weeks make a business-hours pattern obvious at a glance.

## When serverless beats a schedule

An endpoint whose traffic is genuinely sporadic (minutes of inference scattered through
the day) may be better migrated to a serverless endpoint than scheduled: per-inference
billing removes the teardown machinery entirely. The schedule wins when usage is
predictable and sustained inside the window; serverless wins when it is not.