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Difficulty: HardOptimizing Business Processes through FinOps and Cloud Cost Management

A digital publishing platform hosts its core services on Google Cloud Platform across multiple projects within an organizational hierarchy. The architecture includes steady-state core API services running continuously, unpredictable web traffic spikes during breaking news events, simple stateless web microservices, and standard relational database backends. The enterprise FinOps team requires a cost governance strategy that minimizes overall spend while maintaining operational performance and avoiding wasteful over-provisioning. Which architectural strategy should the Cloud Architect recommend?

  1. Apply Flexible Committed Use Discounts (CUDs) to cover baseline compute across Cloud Run and Compute Engine, leverage Cloud Run for stateless microservices to scale to zero during idle periods, and retain Cloud SQL for standard relational workloads.Answer
  2. B
    Purchase 3-year standard Compute Engine Committed Use Discounts sized to cover peak anticipated traffic during breaking news events across all compute instances.
  3. C
    Migrate all relational database backends to multi-region Cloud Spanner instances to handle peak query spikes, supported by 3-year resource-based commitment contracts.
  4. D
    Deploy all simple stateless web microservices into dedicated multi-zonal Google Kubernetes Engine (GKE) clusters with static node pools provisioned for historical peak traffic.

Answer

The optimal solution is to apply Flexible Committed Use Discounts (CUDs) for baseline compute expenditure across Cloud Run and Compute Engine, utilize Cloud Run for simple stateless microservices to leverage scale-to-zero capabilities, and maintain Cloud SQL for standard relational database requirements.
Combining Flexible Committed Use Discounts (CUDs) for continuous baseline spend with serverless scale-to-zero services (Cloud Run) for stateless microservices ensures maximum financial efficiency. Cloud SQL provides the appropriate right-sized cost profile for standard relational data storage.

Step-by-Step Solution

1
Analyze workload traffic patterns and resource requirements.
Identified steady-state baseline API demand, unpredictable breaking news spikes, simple stateless microservices, and standard relational database needs.
Different compute and storage patterns require tailored cost optimization mechanisms rather than a one-size-fits-all commitment.
2
Evaluate compute commitment models and serverless options.
Flexible CUDs cover baseline spend across multiple compute services, while Cloud Run automatically scales to zero for variable stateless microservices.
Flexible commitments prevent locking into rigid VM instance types, and serverless scaling eliminates cost overhead during low-traffic periods.
3
Align database selections with architectural and cost requirements.
Cloud SQL satisfies standard relational database needs cost-effectively without requiring high-cost global Spanner provisioning.
Avoiding over-engineered database solutions preserves capital while meeting standard uptime and performance SLAs.

Key Concept

FinOps Cost Optimization and Compute Commitment Models
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Topics

Optimizing Business Processes through FinOps and Cloud Cost Management