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.

June 26, 2025 - With Cloud Run worker pools, built for continuous, non-HTTP, pull-based background processing, and Kafka Autoscaler, you can adjust consumer instances based on demand.

Worker pools are a Cloud Run resource specifically designed for non-request workloads, such as pull queues.

Use the Google Cloud console, Google Cloud CLI, or YAML to see more details about a worker pool: ... Select Worker pools from the menu to display the available worker pools. Click the worker pool to display its details pane.

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.

2 weeks ago - Traditional serverless models often force background work into an HTTP push format, which can lead to timeouts, overscaling, or message loss during traffic surges. Cloud Run worker pools solve this by providing an always-on environment where the worker pool instances pull tasks or messages from a queue at their own pace, providing built-in backpressure that protects your infrastructure from crashing under load.

September 30, 2025 - Worker Pools is an operational modal in addition to Services and Jobs, and they solve a specific problem that’s plagued Cloud Run users trying to build reliable Pub/Sub consumers.

Your work pool is now ready to receive scheduled flow runs! Now you can launch a Cloud Run service to host the Cloud Run worker. This worker will poll the work pool that you created in the previous step. Navigate back to your terminal and run the following commands to set your Prefect API key and URL as environment variables.

gcloud run deploy SERVICE \ --source . \ --build-worker-pool WORKER_POOL ... SERVICE with name of your service. WORKER_POOL with the name of the private pool. If you are deploying a function, add the --function flag with the function entry point from your source code. You can delete build worker pools for existing services. To clear the Cloud Build worker pool for source deployments, use the --clear-build-worker-pool flag:

resource "google_cloud_run_v2_worker_pool" "default" { name = "cloudrun-worker-pool" location = "us-central1" deletion_protection = false template { containers { name = "foo-1" image = "us-docker.pkg.dev/cloudrun/container/worker-pool" depends_on = ["foo-2"] } containers { name = "foo-2" image = "us-docker.pkg.dev/cloudrun/container/worker-pool" startup_probe { http_get { path = "/healthz" port = 8080 } period_seconds = 5 timeout_seconds = 2 failure_threshold = 3 } } } }Copy

4 days ago - WORKERPOOL: the name of your worker pool. VOLUME_NAME: the name you want to give your volume. SIZE: the disk size—for example, 100Gi. The size must be at least 10Gi for ephemeral-disk volumes. MOUNT_PATH: the relative path where you are mounting the volume, for example, /mnt/my-volume. If you use the Cloud Run volume mount feature, you access a mounted volume using the same libraries in your programming language that you use to read and write files on your local file system.

After you've created a Cloud Run worker pool, you can use Workflows to automate its scaling.

If you use multiple arguments, specify each on its own line—for example, as shown, ARG-N. Create or update the worker pool using the following command: gcloud beta run worker-pools replace workerpool.yaml

3 weeks ago - FROM python:3.12-slim RUN pip install google-cloud-pubsub COPY worker.py . CMD ["python", "-u", "worker.py"] Deploy the consumer worker pool with 0 instances for CREMA to scale up:

Polls external event sources, such as Apache Kafka topics or GitHub Runner Scaler. Calculates the required instance count based on your YAML configuration. Updates the worker pool's instance count automatically.

1 week ago - But the services, jobs, or worker pools the targets deploy to can be in different projects and regions, as long as the service account has access to those projects. In the target definition, create a run stanza to identify the location where the Cloud Run service will be created.

For L4 GPU, configure a minimum of 4 CPU for your worker pool, with 8 CPU recommended, and a minimum of 16 GiB of memory, with 32 GiB recommended. For NVIDIA RTX PRO 6000 Blackwell GPU, configure a minimum of 20 CPU and a minimum of 80 GiB of memory. Determine and set an optimal maximum concurrency for your GPU usage. To get the permissions that you need to configure and deploy Cloud Run worker pools, ask your administrator to grant you the following IAM roles on workerpools: