An enterprise organization runs hundreds of workloads on Compute Engine virtual machines and Cloud SQL instances across dozens of projects in Google Cloud. The finance and FinOps teams report an unexpected increase in monthly spend and identify two core operational requirements: Automatically detect overprovisioned and idle resources across all projects from a centralized management project without enabling the APIs in every individual project. Accurately allocate infrastructure costs in Cloud Billing reports to specific cost centers and environments across services. Which strategy should the cloud architect recommend to meet these requirements?

Enable the Recommender API in a central billing project, query organizational-level recommenders using the billing-project flag, and apply standard labels (such as costcenter and environment) directly to individual resources for Cloud Billing report filtering.: ### What the Recommended Strategy Is This architecture leverages the **Recommender API** from a centralized project in conjunction with granular **resource labeling** to achieve cross-project cost visibility, right-sizing, and cost attribution. ### How It Meets the Requirements - **Centralized Rightsizing & Detection**: The **Recommender API** needs to be enabled only on a single administrative or billing project. By passing the `--billing-project` flag (or `x-goog-user-project` header) in Google Cloud CLI or REST API requests, administrators can query recommendations and utilization insights across all projects in the entire organization (such as `google.compute.instance.MachineTypeRecommender`, `google.cloudsql.instance.OverprovisionedRecommender`, and `google.resourcemanager.projectUtilization.Recommender`). - **Granular Cost Allocation**: Applying **resource labels** (for example, `costcenter=finance` or `environment=production`) directly to individual VMs and storage resources ensures usage metadata is forwarded directly to the Cloud Billing system, allowing finance teams to filter and group costs by label in Cloud Billing reports and BigQuery billing exports. ### Key Advantages - **Centralized Governance**: Avoids the overhead of managing API enablement across dozens or hundreds of spoke projects. - **Fine-Grained Attribution**: Resource-level labels allow accurate cost breakdown down to the specific workload or component. - **Actionable Optimization**: Automatically surfaces underutilized, idle, or overprovisioned resources based on 30-day usage metrics. - **Native Billing Integration**: Seamlessly integrates with Cloud Billing reports, Looker Studio dashboards, and BigQuery billing exports. ### Why This Is Optimal Using a single billing project to query organization-wide recommendations eliminates management sprawl and API configuration overhead. Combining this with standard key-value labels on resources directly enables exact cost tracking across different cost centers in Cloud Billing reports.

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Recommender Api Billing Labels Cost Allocation

Section: Cost optimization

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An enterprise organization runs hundreds of workloads on Compute Engine virtual machines and Cloud SQL instances across dozens of projects in Google Cloud. The finance and FinOps teams report an unexpected increase in monthly spend and identify two core operational requirements:

Automatically detect overprovisioned and idle resources across all projects from a centralized management project without enabling the APIs in every individual project.
Accurately allocate infrastructure costs in Cloud Billing reports to specific cost centers and environments across services.

Which strategy should the cloud architect recommend to meet these requirements?

Enable the Recommender API in a central billing project, query organizational-level recommenders using the billing-project flag, and apply standard labels (such as costcenter and environment) directly to individual resources for Cloud Billing report filtering.

Enable the Recommender API in each individual workload project, attach network tags to the virtual machine instances, and filter the Cloud Billing reports by network tag.

Create IAM conditional policies using Resource Manager tags for cost center allocation, and query the Service Limit Recommender API to identify overprovisioned VM CPU and memory configurations.

Enable the Service Usage API across all projects, assign project-level labels only, and rely exclusively on Cloud Billing reports to identify underutilized compute instances.

Professional Cloud Architect

Recommender Api Billing Labels Cost Allocation

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