Imported from derailed-dash/gemini-review-action (starter-examples/skills/cloud-run-basics/SKILL.md). Install upstream with npx skills add derailed-dash/gemini-review-action --skill cloud-run-basics. Copyright stays with the author.

Cloud Run Basics

Cloud Run is a fully managed application platform for running your code, function, or container on top of Google's highly scalable infrastructure. It abstracts away infrastructure management, providing three primary resource types:

1. Services: Responds to HTTP requests sent to a unique and stable endpoint, using stateless instances that autoscale based on a variety of key metrics, also responds to events and functions.
2. Jobs: Executes parallelizable tasks that are executed manually, or on a schedule, and run to completion.
3. Worker pools: Handles always-on background workloads such as pull-based workloads, for example, Kafka consumers, Pub/Sub pull queues, or RabbitMQ consumers.

Prerequisites

1. Enable the Cloud Run Admin API and Cloud Build APIs:

   gcloud services enable run.googleapis.com cloudbuild.googleapis.com --quiet

2. If you are under a domain restriction organization policy restricting unauthenticated invocations for your project, you will need to access your deployed service as described under Testing private services.

Required roles

You need the following roles to deploy your Cloud Run resource:

• Cloud Run Admin (roles/run.admin) on the project
• Cloud Run Source Developer (roles/run.sourceDeveloper) on the project
• Service Account User (roles/iam.serviceAccountUser) on the service identity
• Logs Viewer (roles/logging.viewer) on the project

Cloud Build automatically uses the Compute Engine default service account as the default Cloud Build service account to build your source code and Cloud Run resource, unless you override this behavior.

For Cloud Build to build your sources, grant the Cloud Build service account the Cloud Run Builder (roles/run.builder) role on your project:

gcloud projects add-iam-policy-binding PROJECT_ID \
    --member=serviceAccount:SERVICE_ACCOUNT_EMAIL_ADDRESS \
    --role=roles/run.builder \
    --quiet

Replace PROJECT_ID with your Google Cloud project ID and SERVICE_ACCOUNT_EMAIL_ADDRESS with the email address of the Cloud Build service account.

Deploy a Cloud Run service

You can deploy your service to Cloud Run by using a container image or deploy directly from source code using a single Google Cloud CLI command.

CRITICAL RULE: Any deployed code MUST listen on 0.0.0.0 (not 127.0.0.1) and use the injected $PORT environment variable (defaults to 8080), or it will crash on boot.

Deploy a container image to Cloud Run

Cloud Run imports your container image during deployment. Cloud Run keeps this copy of the container image as long as it is used by a serving revision. Container images are not pulled from their container repository when a new Cloud Run instance is started.

Supported container images

You can directly use container images stored in the Artifact Registry, or Docker Hub. Google recommends the use of Artifact Registry since Docker Hub images are cached for up to one hour.

You can use container images from other public or private registries (like JFrog Artifactory, Nexus, or GitHub Container Registry), by setting up an Artifact Registry remote repository.

You should only consider Docker Hub for deploying popular container images such as Docker Official Images or Docker Sponsored OSS images. For higher availability, Google recommends deploying these Docker Hub images using an Artifact Registry remote repository.

To deploy a container image, run the following command:

    gcloud run deploy SERVICE_NAME \
        --image IMAGE_URL \
        --region us-central1 \
        --allow-unauthenticated \
        --quiet

Replace the following:

• SERVICE_NAME: the name of the service you want to deploy to. Service names must be 49 characters or less and must be unique per region and project. If the service does not exist yet, this command creates the service during the deployment. You can omit this parameter entirely, but you will be prompted for the service name if you omit it.
• IMAGE_URL: a reference to the container image, for example, us-docker.pkg.dev/cloudrun/container/hello:latest. If you use Artifact Registry, the repository REPO_NAME must already be created. The URL follows the format of LOCATION-docker.pkg.dev/PROJECT_ID/REPO_NAME/PATH:TAG. Note that if you don't supply the --image flag, the deploy command will attempt to deploy from source code.

Deploy from source code

There are two different ways to deploy your service from source:

• Deploy from source with build (default): This option uses Google Cloud's buildpacks and Cloud Build to automatically build container images from your source code without having to install Docker on your machine or set up buildpacks or Cloud Build. By default, Cloud Run uses the default machine type provided by Cloud Build.

  • To deploy from source with automatic base image updates enabled, run the following command:

    gcloud run deploy SERVICE_NAME --source . \
    --base-image BASE_IMAGE \
    --automatic-updates \
    --quiet

    Cloud Run only supports automatic base images that use Google Cloud's buildpacks base images.

    • To deploy from source using a Dockerfile, run the following command:

     gcloud run deploy SERVICE_NAME --source . --quiet

    When you provide a Dockerfile, Cloud Build runs it in the cloud, and
    deploys the service.
    

• Deploy from source without build (Preview): This option deploys artifacts directly to Cloud Run, bypassing the Cloud Build step. This allows for rapid deployment times. To deploy from source without build, run the following command:

  gcloud beta run deploy SERVICE_NAME \
   --source APPLICATION_PATH \
   --no-build \
   --base-image=BASE_IMAGE \
   --command=COMMAND \
   --args=ARG \
   --quiet

  Replace the following:

  • SERVICE_NAME: the name of your Cloud Run service.
  • APPLICATION_PATH: the location of your application on the local file system.
  • BASE_IMAGE: the runtime base image you want to use for your application. For example, us-central1-docker.pkg.dev/serverless-runtimes/google-24-full/runtimes/nodejs24. You can also deploy a pre-compiled binary without configuring additional language-specific runtime components using the OS only base image, such as osonly24.
  • COMMAND: the command that the container starts up with.
  • ARG: an argument you send to the container command. If you use multiple arguments, specify each on its own line.

  For examples on deploying from source without build, see Examples of deploying from source without build.

Create and execute a Cloud Run job

To create a new job, run the following command:

gcloud run jobs create JOB_NAME --image IMAGE_URL OPTIONS --quiet

Alternatively, use the deploy command:

gcloud run jobs deploy JOB_NAME --image IMAGE_URL OPTIONS --quiet

Replace the following:

• JOB_NAME: the name of the job you want to create. If you omit this parameter, you will be prompted for the job name when you run the command.

• IMAGE_URL: a reference to the container image—for example, us-docker.pkg.dev/cloudrun/container/job:latest.

• Optionally, replace OPTIONS with any of the following flags:

  • --tasks: Accepts integers greater or equal to 1. Defaults to 1; maximum is 10,000. Each task is provided the environment variables CLOUD_RUN_TASK_INDEX with a value between 0 and the number of tasks minus 1, along with CLOUD_RUN_TASK_COUNT, which is the number of tasks.
  • --max-retries: The number of times a failed task is retried. Once any task fails beyond this limit, the entire job is marked as failed. For example, if set to 1, a failed task will be retried once, for a total of two attempts. The default is 3. Accepts integers from 0 to 10.
  • --task-timeout: Accepts a duration like "2s". Defaults to 10 minutes; maximum is 168 hours (7 days). For tasks using GPUs, the maximum available timeout is 1 hour.
  • --parallelism: The maximum number of tasks that can execute in parallel. By default, tasks will be started as quickly as possible in parallel.
  • --execute-now: If set, immediately after the job is created, a job execution is started. Equivalent to calling gcloud run jobs create followed by gcloud run jobs execute.

  In addition to these preceding options, you also specify more configuration such as environment variables or memory limits.

For a full list of available options when creating a job, refer to the gcloud run jobs create command line documentation.

Wait for the job creation to finish. You'll see a success message upon a successful completion.

To execute an existing job, run the following command:

gcloud run jobs execute JOB_NAME --quiet

If you want the command to wait until the execution completes, run the following command:

gcloud run jobs execute JOB_NAME --wait --region=REGION --quiet

Replace the following:

• JOB_NAME: the name of the job.
• REGION: the region in which the resource can be found. For example, europe-west1. Alternatively, set the run/region property.

Deploy a worker pool

You can deploy a Cloud Run worker pool using container images or deploy directly from the source.

Deploy a container image

You can specify a container image with a tag (for example, us-docker.pkg.dev/my-project/container/my-image:latest) or with an exact digest (for example, us-docker.pkg.dev/my-project/container/my-image@sha256:41f34ab970ee...).

Supported container images

You can directly use container images stored in the Artifact Registry, or Docker Hub. Google recommends the use of Artifact Registry since Docker Hub images are cached for up to one hour.

You can use container images from other public or private registries (like JFrog Artifactory, Nexus, or GitHub Container Registry), by setting up an Artifact Registry remote repository.

You should only consider Docker Hub for deploying popular container images such as Docker Official Images or Docker Sponsored OSS images. For higher availability, Google recommends deploying these Docker Hub images using an Artifact Registry remote repository.

To deploy a container image, run the following command:

gcloud run worker-pools deploy WORKER_POOL_NAME --image IMAGE_URL --quiet

Replace the following:

• WORKER_POOL_NAME: the name of the worker pool you want to deploy to. If the worker pool does not exist yet, this command creates the worker pool during the deployment. You can omit this parameter entirely, but you will be prompted for the worker pool name if you omit it.

• IMAGE_URL: a reference to the container image that contains the worker pool, such as us-docker.pkg.dev/cloudrun/container/worker-pool:latest. Note that if you don't supply the --image flag, the deploy command attempts to deploy from source code.

Wait for the deployment to finish. Upon successful completion, Cloud Run displays a success message along with the revision information about the deployed worker pool.

Deploy a worker pool from source

You can deploy a new worker pool or worker pool revision to Cloud Run directly from source code using a single gcloud CLI command, gcloud run worker-pools deploy with the --source flag.

The deploy command defaults to source deployment if you don't supply the --image or --source flags.

Behind the scenes, this command uses Google Cloud's buildpacks and Cloud Build to automatically build container images from your source code without having to install Docker on your machine or set up buildpacks or Cloud Build. By default, Cloud Run uses the default machine type provided by Cloud Build.

To deploy a worker pool from source, run the following command:

gcloud run worker-pools deploy WORKER_POOL_NAME --source . --quiet

Replace WORKER_POOL_NAME with the name you want for your worker pool.

What to do if a deployment fails:

1. IAM/Permission Error: Read iam-security.md.
2. Crash on Boot / Healthcheck failed: Fetch the logs immediately using gcloud logging read "resource.labels.service_name=SERVICE_NAME" --limit=20 to find the exact runtime error.
3. Native Dependency Error (Node/Python): If using --no-build, switch to --source . (Buildpacks) to compile native extensions properly for Linux.

Reference Directory

• Core Concepts: Services vs. Jobs vs. Worker pools, resource model, and auto-scaling behavior for services.

• CLI Usage: Essential gcloud run commands for deployment and management.

• Client Libraries: Using Google Cloud client libraries to interact with Cloud Run.

• MCP Usage: Using the Cloud Run remote MCP server.

• Infrastructure as Code: Terraform examples for services, jobs, worker pools, and IAM bindings.

• IAM & Security: Roles, service identities, and ingress/egress controls.

• Networking Best Practices & Cost Optimization: Cost optimization strategies, Direct VPC egress, IP address and port exhaustion strategies, performance throughput tuning, and MTU settings.

If you need product information not found in these references, use the Developer Knowledge MCP server search_documents tool.