Cloud Cost Optimization
Combined blind spend cut up to 55%; idle EC2 eliminated; SP coverage from 0% to 78% where it fit.
- Role
- Cloud Consultant
- Context
- Fintech / E-Commerce / SaaS
- Duration
- 2 weeks per client (6 weeks total)
- Team
- Solo consultant
Tech stack
- AWS Cost Explorer
- Trusted Advisor
- Python
- Terraform
- CloudWatch
- S3
- Lambda
- SNS
- QuickSight
- AWS Budgets
Business problem and production context.
Business problem
Three enterprise clients were overspending on AWS. One had significant idle EC2 inventory, another was running oversized RDS instances at low utilization, and the third had no Savings Plans despite predictable workloads, all with no visibility into per-team or per-service costs.
Architecture decision
Conducted 2-week architecture audits per client using AWS Cost Explorer, Trusted Advisor, and custom Python scripts. Implemented resource right-sizing (EC2, RDS, ElastiCache), converted qualifying workloads to Savings Plans and Reserved Instances, migrated batch jobs to Spot/Fargate Spot, enforced S3 lifecycle policies, and built FinOps dashboards with cost allocation tags and per-team budgets with SNS alerts.
The useful proof is the decision surface, not only the result number.
These are the parts of the work that show production judgment: ownership, constraints, rollback, cost, and observability.
Owned scope
- Multi-client AWS cost audit narrative with billing analysis, right-sizing, lifecycle policies, and FinOps automation
- Savings methodology that separates quick cuts from durable operating changes
- Cost visibility model for engineering and finance to discuss the same drivers
Evidence artifacts
What would fail first?
Every project has constraints. The useful work is naming them early enough that rollback, cost, and ownership are designed before an incident.
Mixed workloads
Savings came from different levers across compute, storage, databases, logs, and commitment strategy.
Business risk
Cutting cost could not reduce reliability or remove capacity required for real traffic.
Proof framing
Client details are sanitized, so savings need clear methodology and NDA-safe context.
Trade-offs accepted on purpose.
Production risks and how they were controlled.
Small steps, visible changes, fewer surprises.
- 01
Ran 3-day discovery per client: extracted Cost Explorer data, Trusted Advisor reports, and CloudWatch utilization metrics across all regions.
- 02
Built Python analysis scripts to identify idle resources (EC2 at <5% CPU, RDS at <8% utilization, unattached EBS volumes, unused Elastic IPs).
- 03
Modeled Savings Plan scenarios using AWS Cost Explorer APIs, comparing 1-year vs 3-year, All Upfront vs No Upfront, across Compute and EC2 families.
- 04
Implemented automated enforcement: S3 lifecycle policies (30-day IA, 90-day Glacier), Lambda-based schedulers for dev environment shutdown, and budget alerts at 80/100% thresholds.
- 05
Built QuickSight dashboards with cost allocation by team, service, and environment, giving CFOs and engineering leads visibility they never had.
Before and after.
Rollback path
High-risk cost changes were staged, monitored, and reversible; lifecycle and schedule-based changes were kept explicit.
Cost considerations
The public story stays sanitized: ₹50L+/yr client savings and selected 30-40% reductions where details can be shared.
Observability notes
Useful signals were cost by owner, unit cost, anomaly alerts, idle inventory, and post-change service health.
Related services
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