Cloud Architecture: Scenario-Based Questions

Cloud-native environments offer elasticity and scalability — but without active monitoring and right-sizing, costs can quickly spiral. Cost optimization involves architectural, operational, and cultural strategies to align cloud spend with business value.

📊 Key Cost Drivers

• Compute: Over-provisioned VMs, long-running containers, idle Lambda invocations.
• Storage: Orphaned volumes, inefficient object storage tiers, uncompressed data.
• Network: High inter-zone/region traffic, unnecessary NAT or egress costs.
• Licensing: Proprietary database and marketplace software.

🎯 Optimization Strategies

• Right-Sizing: Auto-scale compute resources, remove unused volumes and IPs.
• Spot & Reserved Instances: Use EC2 Spot, GCP Preemptible, or RIs for stable workloads.
• Storage Tiering: Move infrequent data to Glacier/Coldline/Archive tiers.
• Scheduled Shutdowns: Turn off dev/test environments after hours.

🧰 Tools for Visibility & Management

• AWS Cost Explorer / Azure Cost Management / GCP Billing Reports
• CloudHealth, Spot.io, Kubecost: Detailed cost attribution and optimization recommendations.
• FinOps Dashboards: Shared visibility across finance and engineering.

✅ Best Practices

• Tag resources by team, service, environment for chargeback and alerts.
• Review billing alerts monthly — embed FinOps reviews in sprints.
• Align architecture decisions with cost (e.g., serverless vs containers vs VMs).

🚫 Common Pitfalls

• Leaving test/staging environments running indefinitely.
• Not monitoring per-service or per-team costs.
• Using premium managed services without evaluating alternatives.

📌 Final Insight

Cost efficiency is a shared responsibility — product, engineering, and finance teams must align on goals. Smart teams treat cost like any other engineering metric: observable, testable, and actionable.