Undeploying your model

After deploying and making predictions, you can manually undeploy your model to avoid incurring further charges.

By undeploying your model you avoid incurring further model hosting usage charges. For more information, see the pricing page.

Web UI

  1. Open the AutoML Vision Object Detection UI and select the Models tab (with lightbulb icon) in the left navigation bar to display the available models.

    To view the models for a different project, select the project from the drop-down list in the upper right of the title bar.

  2. Select the row for the model you want to use to label your images.
  3. Select the Test & Use tab just below the title bar.
  4. Select Remove deployment from the banner beneath your model name to open the undeploy option window.

    undeploy popup menu
  5. Select Remove deployment to undeploy the model.

    model deploying
  6. You will receive an email when model undeployment has completed.

    deploy finished email


Before using any of the request data below, make the following replacements:

  • project-id: your GCP project ID.
  • model-id: the ID of your model, from the response when you created the model. The ID is the last element of the name of your model. For example:
    • model name: projects/project-id/locations/location-id/models/IOD4412217016962778756
    • model id: IOD4412217016962778756

HTTP method and URL:

POST https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id:undeploy

To send your request, choose one of these options:


Execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d "" \


Execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-Uri "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id:undeploy" | Select-Object -Expand Content
You should receive a response with the ID of the deploy operation:
  "name": "projects/project-id/locations/us-central1/operations/operation-id",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-08-07T22:19:50.828033Z",
    "updateTime": "2019-08-07T22:19:50.828033Z",
    "undeployModelDetails": {}

You can get the status of an operation with the following HTTP method and URL:

GET https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations/operation-id

The status of a finished operation will look similar to the following:

  "name": "projects/project-id/locations/us-central1/operations/operation-id",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-06-21T16:47:21.704674Z",
    "updateTime": "2019-06-21T17:01:00.802505Z",
    "deployModelDetails": {}
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"


Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

import (

	automl "cloud.google.com/go/automl/apiv1"
	automlpb "google.golang.org/genproto/googleapis/cloud/automl/v1"

// undeployModel deploys a model.
func undeployModel(w io.Writer, projectID string, location string, modelID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "TRL123456789..."

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %v", err)
	defer client.Close()

	req := &automlpb.UndeployModelRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),

	op, err := client.UndeployModel(ctx, req)
	if err != nil {
		return fmt.Errorf("DeployModel: %v", err)
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())

	if err := op.Wait(ctx); err != nil {
		return fmt.Errorf("Wait: %v", err)

	fmt.Fprintf(w, "Model undeployed.\n")

	return nil


Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.ModelName;
import com.google.cloud.automl.v1.OperationMetadata;
import com.google.cloud.automl.v1.UndeployModelRequest;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class UndeployModel {

  static void undeployModel() throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    undeployModel(projectId, modelId);

  // Undeploy a model from prediction
  static void undeployModel(String projectId, String modelId)
      throws IOException, ExecutionException, InterruptedException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
      UndeployModelRequest request =
      OperationFuture<Empty, OperationMetadata> future = client.undeployModelAsync(request);

      System.out.println("Model undeployment finished");


Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

 * TODO(developer): Uncomment these variables before running the sample.
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const modelId = 'YOUR_MODEL_ID';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function undeployModel() {
  // Construct request
  const request = {
    name: client.modelPath(projectId, location, modelId),

  const [operation] = await client.undeployModel(request);

  // Wait for operation to complete.
  const [response] = await operation.promise();
  console.log(`Model undeployment finished. ${response}`);



Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

use Google\Cloud\AutoMl\V1\AutoMlClient;

/** Uncomment and populate these variables in your code */
// $projectId = '[Google Cloud Project ID]';
// $location = 'us-central1';
// $modelId = 'my_model_id_123';

$client = new AutoMlClient();

try {
    // get full path of model
    $formattedName = $client->modelName(

    $operationResponse = $client->undeployModel($formattedName);
    if ($operationResponse->operationSucceeded()) {
        $result = $operationResponse->getResult();
        printf('Model undeployed.' . PHP_EOL);
    } else {
        $error = $operationResponse->getError();
        // handleError($error)
} finally {


Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"

client = automl.AutoMlClient()
# Get the full path of the model.
model_full_id = client.model_path(project_id, "us-central1", model_id)
response = client.undeploy_model(name=model_full_id)

print("Model undeployment finished. {}".format(response.result()))


Before trying this sample, follow the setup instructions for this language on the Client Libraries page.

require "google/cloud/automl"

project_id = "YOUR_PROJECT_ID"
model_id = "YOUR_MODEL_ID"

client = Google::Cloud::AutoML.auto_ml

# Get the full path of the dataset
model_full_id = client.model_path project: project_id,
                                  location: "us-central1",
                                  model: model_id

operation = client.undeploy_model name: model_full_id

# Wait until the long running operation is done

puts "Model undeployment finished."