Cloud Run core concepts

Cloud Run is a fully managed application platform for running your code, function, or container on top of Google's highly scalable infrastructure. On Cloud Run, your code can run as a service, job, or worker pool. All of these resource types are running sandboxed container instances in the same execution environment and can integrate with Google Cloud services.

Services vs. Jobs vs. Worker pools

• Cloud Run services: Used for code that handles requests or events (e.g., web apps, APIs). They provide an HTTPS endpoint and automatically scale based on traffic.

• Cloud Run jobs: Used for code that performs a specific task and then exits (e.g., data processing, database migrations). They can run a single task or an array of parallel tasks.

• Cloud Run worker pools: Designed for continuous, non-HTTP, pull-based background processing (e.g., Kafka consumers).

Resource model

Cloud Run organizes resources as follows:

1. Service The top-level resource. You can deploy a service from a container, repository, or source code. 1. Revision: An immutable snapshot of a service's configuration and container image. Each service deployment creates a new revision. 1. Service instances: The running container that processes requests. Each service revision receiving requests is automatically scaled to the number of instances needed to handle all these requests. 1. Cloud Run functions: Deploy functions as Cloud Run services. You can deploy single-purpose functions that respond to events emitted from your cloud infrastructure and services

2. Job: Executes one or more containers to completion. A job consists of one or multiple independent tasks that are executed in parallel in a given job execution.

3. Worker pools: If your code processes workloads from an external source but not from an HTTP request, such as pulling work from a message queue, you can deploy it to a Cloud Run worker pool .

Autoscaling for Cloud Run services

Cloud Run services scale automatically based on:

• Request concurrency: The number of concurrent requests per instance.
• CPU utilization:: The average CPU utilization of existing instances over a one minute window.
• Scale to zero: Cloud Run autoscales from one to zero instances only after verifying that an instance is no longer processing requests. If you use instance-based billing, Cloud Run instances are charged for the entire lifecycle of instances, even when there are no incoming requests.

Container contract

Your container image can run code written in the programming language of your choice and use any base image, provided that it respects the constraints listed in the Container runtime contract.

Executables in the container image must be compiled for Linux 64-bit. Cloud Run specifically supports the Linux x86_64 ABI format.

Cloud Run accepts container images in the Docker Imag Manifest V2, Schema 1, Schema 2, and OCI image formats. Cloud Run also accepts Zstd compressed container images.

If deploying a multi-architecture image, the manifest list must include linux/amd64.

For functions deployed with Cloud Run, you can use one of the Cloud Run runtime base images that are published by Google Cloud's buildpacks to receive automatic security and maintenance updates. For more information about the supported runtimes, see the Runtime support schedule.

Container requirements

When deploying containers to Cloud Run, the following requirements must be met:

• Container deployed to services must listen for requests on the correct port
• A Cloud Run service starts Cloud Run instances to handle incoming requests. A Cloud Run instance always has one single ingress container that listens for requests, and optionally one or more sidecar containers. The following port configuration details apply only to the ingress container, not to sidecars.
• The ingress container within an instance must listen for requests on 0.0.0.0 on the port to which requests are sent. Notably, the ingress container should not listen on 127.0.0.1. By default, request are sent to 8080, but you can configure Cloud Run to send requests to the port of your choice. Cloud Run injects the PORT environment variable into the ingress container.

VPC network connectivity

Cloud Run services and jobs support Direct VPC egress. This means that they can send traffic to private resources within your configured VPC network, such as databases or internal services. Cloud Run services and jobs don't support Direct VPC ingress. Cloud Run worker pools support both Direct VPC egress and Direct VPC ingress. When you configure Direct VPC for your Cloud Run worker pool deployment, each worker instance receives a private IP address on the configured network and subnet. Only resources from your VPC network can connect to the worker pool private IP address endpoint. For more information about obtaining the private IP addresses of your worker pool instance, see Retrieve the private IP addresses using the metadata server (MDS).

For Cloud Run worker pools with Direct VPC ingress, such as database connections or any other custom TCP-based protocol, the container must listen for TCP connections on the port exposed in your container image through the Dockerfile or specified by the PORT environment variable.

AI and GPU support

Cloud Run supports hosting AI inference models. You can configure services with GPUs (e.g., NVIDIA RTX PRO 6000 Blackwell GPU, NVIDIA L4) to accelerate workloads like LLM inference using Gemma 3. For more information, see GPU support for services, jobs, and worker pools.

Pricing

Cloud Run uses a pay-as-you-go model:

• Request-based: Charged for resources used during request processing.
• Instance-based: Charged for the entire lifetime of an instance.

For the latest pricing, visit: Cloud Run pricing.