An enterprise is optimizing compute costs for three distinct workloads hosted on Google Cloud: Workload 1: A core relational database running 24/7 on dedicated N2 Compute Engine virtual machines in us-central1 with predictable, steady-state CPU and memory utilization. Workload 2: A dynamic microservices architecture deployed across Compute Engine, Google Kubernetes Engine (GKE), and Cloud Run across multiple regions, with a predictable aggregate baseline hourly spend. Workload 3: A nightly high-throughput batch processing job that is stateless, fault-tolerant, and capable of resuming after sudden node terminations. Which combination of Google Cloud pricing models and discount strategies should you recommend to minimize costs?

Apply resource-based Committed Use Discounts (CUDs) for Workload 1, purchase Compute flexible spend-based CUDs for Workload 2, and provision Spot VMs for Workload 3.: ### What the Recommended Pricing Strategy Is This multi-tiered pricing architecture aligns specific Google Cloud commitment models and execution tiers to the distinct predictability, geographical distribution, and fault-tolerance characteristics of each workload. ### How It Meets Each Requirement - **Workload 1 (Predictable Regional DB):** **Resource-based Committed Use Discounts (CUDs)** provide deep discounts (typically up to 55% for general-purpose series and up to 70% for memory-optimized machines) in exchange for committing to a specific quantity of vCPUs and memory within a specific region (`us-central1`) and machine series (N2). - **Workload 2 (Multi-Service Microservices Fleet):** **Compute flexible spend-based CUDs** are purchased at the **Cloud Billing account level** and automatically apply across **Compute Engine**, **Google Kubernetes Engine (GKE)**, and **Cloud Run** regardless of region or machine family, making them ideal for predictable aggregate spend across heterogeneous runtimes. - **Workload 3 (Fault-Tolerant Batch Jobs):** **Spot VMs** offer steep discounts (60–91% compared to standard on-demand rates) for flexible, fault-tolerant batch workloads that can handle VM preemption without impacting business SLAs. ### Key Advantages and Capabilities - **Deepest Hardware Discounts:** Maximizes savings on steady 24/7 database instances by fixing commitments to specific regional hardware resources. - **Cross-Service Flexibility:** Avoids lock-in to specific machine types or single compute services for containerized microservices spanning Compute Engine, GKE, and Cloud Run. - **Aggressive Cost Reductions for Batch:** Reclaims spare Google Cloud capacity for stateless batch jobs at minimal cost without long-term commitment overhead. - **Automated Credit Application:** Google Cloud applies resource-based CUDs first to eligible usage before utilizing flexible spend-based CUDs. ### Why This Is Optimal This architecture pairs the highest discount tier available for fixed, long-running regional workloads (resource-based CUDs) with broad elasticity for distributed architectures (flexible CUDs) and hyper-discounted ephemeral capacity (Spot VMs).

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Gcp Pricing Models Cud Spot Vms Optimization

Section: Cost optimization

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An enterprise is optimizing compute costs for three distinct workloads hosted on Google Cloud:

Workload 1: A core relational database running 24/7 on dedicated N2 Compute Engine virtual machines in us-central1 with predictable, steady-state CPU and memory utilization.
Workload 2: A dynamic microservices architecture deployed across Compute Engine, Google Kubernetes Engine (GKE), and Cloud Run across multiple regions, with a predictable aggregate baseline hourly spend.
Workload 3: A nightly high-throughput batch processing job that is stateless, fault-tolerant, and capable of resuming after sudden node terminations.

Which combination of Google Cloud pricing models and discount strategies should you recommend to minimize costs?

Apply spend-based CUDs for Workload 1, configure resource-based CUDs across multiple regions for Workload 2, and rely on standard on-demand VMs with Sustained Use Discounts for Workload 3.

Purchase Compute flexible spend-based CUDs for Workload 3, apply resource-based CUDs for Workload 1, and run all instances in Workload 2 as Spot VMs across GKE and Cloud Run.

Apply resource-based Committed Use Discounts (CUDs) for Workload 1, purchase Compute flexible spend-based CUDs for Workload 2, and provision Spot VMs for Workload 3.

Rely entirely on automated Sustained Use Discounts (SUDs) across all workloads and attach regional reservations to Workload 3.

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Gcp Pricing Models Cud Spot Vms Optimization

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