An organization is deploying a new public-facing REST API packaged as a stateless container image. The API experiences sudden, unpredictable bursts of traffic alongside extended periods of complete inactivity outside business hours. The engineering leadership has outlined several strict criteria: The compute environment must scale down to zero instances during idle periods to eliminate idle resource costs. Autoscaling must be rapid and fully automated without requiring teams to define or fine-tune complex scaling metrics. Operational overhead for managing underlying infrastructure, servers, and runtime updates must be eliminated. Which compute service should the organization select?

Deploy the containerized API onto Cloud Run: ### What Cloud Run is **Cloud Run** is Google Cloud's fully managed **serverless compute platform** that enables developers to run stateless containerized applications directly on top of Google's scalable infrastructure. It abstracts all underlying server management, automatically provisioning and de-provisioning container instances on demand. ### How it meets the requirements - **Scale down to zero**: **Cloud Run** is designed to scale dynamically based on incoming HTTP requests. When there is no active traffic, the service automatically scales down to **zero running instances**, ensuring you pay nothing for idle compute. - **Rapid and automated scaling**: Unlike virtual machine or cluster-based platforms, **serverless autoscaling** in Cloud Run handles sudden traffic spikes instantaneously without manual intervention or pre-warming. - **Zero infrastructure overhead**: Operating system updates, container orchestration, host security patches, and capacity provisioning are entirely managed by Google Cloud, removing all administrative burden from the engineering team. ### Key advantages and capabilities - **Pure pay-per-use billing**: Charges are calculated strictly per request and for the exact duration (down to 100 milliseconds) each request takes to process. - **Native concurrency**: A single container instance can handle multiple concurrent requests simultaneously, optimizing performance and cost. - **Broad language and container support**: Any stateless container that listens for HTTP requests on a designated port can be deployed seamlessly. - **Direct networking integration**: Supports native integrations with **Application Load Balancers** via **serverless network endpoint groups (NEGs)** and private VPC access. ### Why this is optimal Compared to Infrastructure as a Service (**IaaS**) or container orchestration (**GKE**), **Cloud Run** eliminates the operational overhead of cluster and VM capacity planning while fulfilling the requirement of zero cost during idle periods.

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Serverless Compute Service Selection Cloud Run

Section: Compute resource provisioning

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An organization is deploying a new public-facing REST API packaged as a stateless container image. The API experiences sudden, unpredictable bursts of traffic alongside extended periods of complete inactivity outside business hours. The engineering leadership has outlined several strict criteria:

The compute environment must scale down to zero instances during idle periods to eliminate idle resource costs.
Autoscaling must be rapid and fully automated without requiring teams to define or fine-tune complex scaling metrics.
Operational overhead for managing underlying infrastructure, servers, and runtime updates must be eliminated.

Which compute service should the organization select?

Deploy the containerized API onto Cloud Run

Deploy the containerized API onto Google Kubernetes Engine (GKE) Autopilot with Horizontal Pod Autoscaling (HPA)

Deploy the containerized API onto Compute Engine managed instance groups (MIGs) with predictive autoscaling

Deploy the containerized API onto Google Kubernetes Engine (GKE) Standard using the Cluster Autoscaler

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Serverless Compute Service Selection Cloud Run

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