Autonomous AWS FinOps Optimization Engine
Automated rightsizing, scheduled teardown of idle staging resources, and auto-scaling cost controls that reduced monthly cloud infrastructure spend by 30% to 40%.
- Company
- TezHire
- Role
- DevOps Engineer
- Key result
- -40% AWS Monthly Spend
Executive Summary
Rapid feature iteration across distributed staging environments caused noticeable compute and database sprawl. Idle EC2, RDS, and test container instances remained online overnight and over weekends, accumulating steady operational costs without serving user traffic.
To eliminate waste without hurting developer velocity, I architected an event-driven FinOps automation engine that enforces scheduled resource lifecycle management, rightsized compute tasks, and introduces proactive cost anomaly alerts.
Impact at a glance
| Metric | Result | Detail |
|---|---|---|
| Direct Monthly Reduction | -40% | Achieved across staging and dev environments within 60 days. |
| Provisioning Error Rate | -50% | Standardized Terraform & CDK modules eliminated drift. |
Architectural Strategy
The FinOps architecture combines declarative infrastructure as code with serverless event orchestration:
- Tag-Driven Lifecycle Enforcement: Cloud resources tagged with environment tiers (
staging,sandbox,preview) register with an automated state machine. - Scheduled Teardown via Serverless Cron: AWS EventBridge rules trigger Python Lambda handlers to gracefully spin down non-critical services outside active sprint hours.
- Auto-Scaling Rightsizing: Dynamic scaling thresholds replace static instance sizing, adapting task capacities based on actual memory and CPU telemetry gathered through Amazon CloudWatch.
KEY OPERATING PRINCIPLE
Automation must never surprise developers. All shutdown events dispatch Slack notifications 15 minutes before execution, providing single-click webhook overrides when developers require extended test sessions.
Declarative Standardization with Terraform & CDK
All target workloads were migrated to reusable Terraform modules and AWS CDK constructs. By defining consistent tagging schemas (Environment, CostCenter, Owner), financial allocation became transparent across engineering teams.