Cloud Economics: Scenario-Based Questions

Cloud cost optimization is the practice of maximizing efficiency while minimizing spend on cloud resources. In production, it involves tuning usage patterns, rightsizing infrastructure, and eliminating waste.

💰 Key Optimization Strategies

• Rightsizing: Downsize over-provisioned compute (EC2, GCE, VM) based on actual usage.
• Autoscaling: Scale up or down based on load, avoiding idle capacity.
• Reserved Instances / Savings Plans: Commit to usage for 1–3 years at a discount.
• Spot Instances: Use for fault-tolerant, stateless workloads like CI/CD, batch jobs.
• Storage Lifecycle Policies: Move infrequently accessed data to cheaper tiers (e.g., S3 Glacier, GCS Coldline).

📦 Service-Specific Tips

• Lambda: Tune memory allocation and avoid long-running functions.
• ECS/EKS/GKE: Binpack pods, use spot-capacity nodes, set resource requests accurately.
• Databases: Enable storage autoscaling, optimize queries, archive old data.

📊 Visibility & Governance

• Use cloud cost tools (AWS Cost Explorer, GCP Billing, Azure Cost Management).
• Tag resources for environment, owner, and purpose — track spend by project.
• Set budgets and alert thresholds for runaway services.

✅ Best Practices

• Review cost reports monthly and automate cleanup of orphaned resources.
• Apply policies-as-code to enforce cost-effective defaults (e.g., restrict large instance types).
• Run cost-optimization sprints with engineering + finance collaboration.

🚫 Common Pitfalls

• Leaving unused EBS volumes, IPs, or snapshots running.
• Oversizing infrastructure based on peak, not actual usage.
• Skipping tagging — leads to “mystery” spend with no ownership.

📌 Real-World Insight

Cost optimization isn’t a one-time task — it’s an ongoing practice. High-performing teams integrate cost metrics into dashboards and pipelines, treating dollars like another form of system health.