# GCP Vertex AI Model Not Deployed

> Vertex AI models sitting in the registry with deployedModelCount at 0 serve nothing. The registry itself is free, so ZopNight only surfaces a model when GCP billing attributed real spend to its UID. The shown figure is measured billing, never a fabricated storage estimate, and artifact cleanup is the real win.

Source: https://zop.dev/integrations/gcp/recommendations/gcp-vertex-ai-model-not-deployed
Updated: 2026-08-19

---

## The registry is free; the residue is not

Google documents the Model Registry itself as costing nothing: registering a model
creates no standing charge, and serving costs belong to the endpoint it deploys to. So an
undeployed model is not a monthly leak in the way an idle VM is. What it represents is
clutter with attached spend: training residue billed against the model's identity, and
artifact bytes parked in Cloud Storage. The rule fires on models whose
`deployedModelCount` reads exactly 0 and to which billing has attributed real spend;
deleting them closes out that spend and, more importantly, prunes a registry where the
next engineer can no longer tell live models from dead ones.

## Discovered by polling, not asset inventory

Cloud Asset Inventory, ZopNight's bulk discovery surface for most GCP resources, does
not index `aiplatform.googleapis.com/Model` at all. Models are found by a live REST
poller calling `models.list` per region, covered by the `aiplatform.models.list`
permission inside `roles/aiplatform.viewer`, which also stamps each model's deployment
count. A model this rule names was therefore seen directly on the API, not inferred from
a stale inventory snapshot.

## Check deployment state per model

```bash
gcloud ai models list --region us-central1 \
  --format="table(name,displayName,deployedModels.len())"
```

A zero in the last column reproduces the rule's gate. Confirm the version is not staged
for an imminent deployment before removing it.

## Artifacts live in your bucket

Deleting the registry entry does not delete the artifact. Model files live in a GCS
bucket in your project and bill to that bucket. Cleanup that actually recovers storage
money means archiving or deleting the artifact folder too, after exporting anything with
retraining value.

## Firing conditions are narrow

Abstention covers most of the fleet by design: models whose deployment count is anything
but a recorded "0", and models with no billing-attributed cost, produce nothing. There is
no minimum-age gate yet (GCP's Model resource carries no last-undeployed timestamp), so
treat a very recently created model that appears here with corresponding caution.