An enterprise organization operates multiple Google Cloud projects attached to a single Cloud Billing account. The infrastructure architecture includes the following workload profiles: Core backend services: Predictable, steady-state Compute Engine N2 virtual machines running 24/7 in a single primary region (us-central1). Microservices and batch processing: Variable workloads running across multiple machine families, Cloud Run, and Google Kubernetes Engine (GKE) distributed across multiple geographic regions. As a DevOps and FinOps engineer, you need to implement a commitment strategy that maximizes cost savings for the predictable baseline, provides flexible coverage for dynamic multi-service compute spend, and prevents commitment underutilization across projects. Which strategy should you implement?

Purchase resource-based Committed Use Discounts (CUDs) for the steady-state N2 baseline in us-central1, purchase spend-based CUDs for variable multi-region and multi-product compute, and enable discount sharing across all projects in the Cloud Billing account.: ### What Resource-Based and Spend-Based CUDs with Discount Sharing Are **Resource-based Committed Use Discounts (CUDs)** provide the deepest discounts (up to 55–70%) in exchange for committing to a specific amount of vCPUs, memory, GPUs, or local SSDs in a single region and specific machine series. **Spend-based (Flexible) CUDs** offer a predictable dollar-per-hour discount across multiple compute services (including Compute Engine, Google Kubernetes Engine, and Cloud Run) regardless of machine series or region. **Discount sharing** at the Cloud Billing account level aggregates eligible usage across all linked projects so commitments apply wherever matching resources run. ### How it Meets the Requirements * **Maximizes savings on predictable baseline**: Securing **resource-based CUDs** for the steady-state N2 instances in `us-central1` captures the highest percentage discount for hardware configurations that will not change. * **Provides flexible multi-service coverage**: Applying **spend-based CUDs** covers fluctuating compute footprints across different services (Cloud Run, GKE, varied VM families) and regions where rigid resource commitments would risk underutilization. * **Prevents underutilization**: Enabling **discount sharing** at the billing account level ensures that if a specific project scales down, another project's matching usage immediately absorbs the committed discount capacity. ### Key Advantages and Capabilities * **Tiered FinOps optimization**: Combines deep discounts for static infrastructure with flexible discounts for dynamic architectures. * **Cross-project resource pooling**: Eliminates stranded commitments caused by project-level architectural reallocations. * **Multi-product applicability**: Spend-based commitments automatically apply across serverless (Cloud Run) and managed container runtimes (GKE). * **Predictable operational budgeting**: Establishes a guaranteed hourly spend baseline while letting peak bursts overflow to on-demand pricing. ### Why This Is Optimal Layering rigid resource-based CUDs for well-understood, single-region baselines under flexible spend-based CUDs for containerized/serverless components—backed by centralized billing discount sharing—delivers the optimal balance between maximum percentage discount and low financial risk.

Optimize Compute Cost Cud Sud Sharing | Professional Cloud DevOps Engineer

Optimize Compute Cost Cud Sud Sharing

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An enterprise organization operates multiple Google Cloud projects attached to a single Cloud Billing account. The infrastructure architecture includes the following workload profiles:

Core backend services: Predictable, steady-state Compute Engine N2 virtual machines running 24/7 in a single primary region (us-central1).
Microservices and batch processing: Variable workloads running across multiple machine families, Cloud Run, and Google Kubernetes Engine (GKE) distributed across multiple geographic regions.

As a DevOps and FinOps engineer, you need to implement a commitment strategy that maximizes cost savings for the predictable baseline, provides flexible coverage for dynamic multi-service compute spend, and prevents commitment underutilization across projects.

Which strategy should you implement?

Purchase resource-based CUDs scoped strictly to individual projects for 100% of all projected peak workloads, and disable billing account discount sharing to maintain strict cost attribution.

Purchase resource-based Committed Use Discounts (CUDs) for the steady-state N2 baseline in us-central1, purchase spend-based CUDs for variable multi-region and multi-product compute, and enable discount sharing across all projects in the Cloud Billing account.

Rely entirely on automated Sustained Use Discounts (SUDs) across all projects while disabling Committed Use Discounts to avoid financial lock-in and multi-year contractual commitments.

Purchase spend-based CUDs exclusively for the N2 baseline instances in us-central1 and purchase resource-based CUDs for the distributed GKE and Cloud Run services.

Professional Cloud DevOps Engineer

Optimize Compute Cost Cud Sud Sharing

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