# GCP Vertex AI Endpoint Idle

> Vertex AI endpoints keep dedicated serving nodes running regardless of prediction traffic. ZopNight flags endpoints whose VertexPredictionCount shows 0 activity over the window, preferring a scheduled off-hours undeploy when a measured idle heatmap exists, and otherwise recommending teardown of the deployment, since there is no native stop for an endpoint.

Source: https://zop.dev/integrations/gcp/recommendations/gcp-vertex-ai-endpoint-idle
Updated: 2026-08-19

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## Serving nodes never sleep on their own

Deploying a model to a Vertex AI endpoint allocates dedicated serving machines that bill
by the node-hour from deployment until undeploy. Prediction traffic does not modulate the
charge: a model that answered its last request in March costs the same as one under load.
An endpoint harvests 12 different monitoring series, so the rule reads
`VertexPredictionCount` strictly by name and fires only when both average and maximum are
0 across the harvested window; any measured prediction clears it.

## Two levers, schedule preferred

When the aggregator has built a weekly usage heatmap for the endpoint, the recommendation
becomes a recurring off-hours undeploy/redeploy window, priced as the endpoint's cost
times its measured idle fraction. That is the safe lever. Serving is unchanged during
working hours, and nothing is destroyed. Only when no schedule data exists does the rule
fall back to the full teardown recommendation, with the entire monthly cost as the
recoverable amount.

## No stop API means undeploy and redeploy

Vertex endpoints cannot be paused. "Off" is implemented as undeploying the model, via the
`aiplatform.endpoints.undeploy` permission in `roles/aiplatform.user`, with
`aiplatform.endpoints.deploy` restoring it from the saved DeployedModel configuration. It
is typically automated with Cloud Scheduler and a small Cloud Function. Sporadic traffic
is better served by moving to an autoscaling deployment with a zero minimum where the
model allows it.

## Inspect the endpoint's deployments

```bash
gcloud ai endpoints describe ENDPOINT_ID --region us-central1 \
  --format="value(deployedModels.id,deployedModels.dedicatedResources.minReplicaCount)"
```

A non-empty deployment list with a flat prediction graph is the exact state this rule
prices.

## The pricing catch on Vertex serving

Vertex serving SKUs are only attributed to an endpoint when billing-export data ties
spend to its UID; ZopNight's calculated rack-rate path deliberately prices these
endpoints at $0 rather than guess a rate, so the rule fires for billing-connected
accounts. It also abstains when the prediction metric is absent or when any traffic is
observed. An unmeasured endpoint is never torn down on assumption.