Vertex AI Endpoint
Does ZopNight manage Vertex AI Endpoint?
A Vertex AI endpoint bills per node-hour for every serving node its deployed models hold, 24 hours a day, whether or not predictions arrive. ZopNight discovers endpoints through Cloud Asset Inventory, watches prediction traffic, and flags endpoints whose deployed models sit idle. The fix is undeploying the model, which releases the nodes.
Rules that fire on Vertex AI Endpoint
A Vertex AI endpoint serves online predictions from deployed models on dedicated machines that bill per node-hour around the clock. Endpoints left serving after experiments end are a leading source of hidden ML spend.
Node-hours are the meter, predictions are not
The moment a model is deployed to an endpoint, Vertex AI provisions serving nodes of the machine type chosen at deployment and starts charging for each node-hour. Query volume never enters the calculation: an endpoint answering millions of predictions and an endpoint answering none pay identically for the same node count. Autoscaling can widen the fleet under load, but the configured minimum replica count is a floor the bill never drops below, and GPU-backed serving nodes multiply the stakes.
How ZopNight watches for idle serving
ZopDev discovers endpoints via Cloud Asset Inventory, monitors prediction traffic, and flags endpoints with idle deployed models in recommendations. Because the cost driver is the deployment rather than the endpoint shell, the signal that matters is a deployed model receiving no requests over the observation window, which is precisely what the idle-endpoint recommendation surfaces.
Undeploying is the lever that releases nodes
An endpoint is not schedulable in ZopNight: a deploy/undeploy executor exists internally, but the type is not exposed as schedulable. The effective action is undeploying models that no longer need to serve. Undeployment releases the serving nodes and ends their node-hour charges, while the model itself stays registered in the Model Registry and can be redeployed later. Deleting the empty endpoint shell afterwards is free hygiene.
How endpoints outlive their experiments
The recurring pattern is a model deployed for a demo, an A/B test, or a one-quarter experiment whose conclusion arrived months ago. Two variants show up constantly: a losing model version left co-deployed at a small traffic split after the winner was chosen, and a minimum replica count sized for a launch spike that never receded.
Reaching endpoints in the console
Google Cloud console → Vertex AI → Online prediction lists endpoints per region. Each endpoint’s page shows its deployed models, machine types, and replica counts (the numbers that decide the bill).