Cloud Migration with Reliability and Cost Controls for a High Growth Platform
A high growth platform needed to improve reliability and reduce cloud waste while preparing for higher traffic. We redesigned their cloud architecture, introduced infrastructure as code, and implemented observability and cost controls to stabilize deployments and lower spend.
Confidential engagement. NDA available upon request.
40%
Cost Reduction
99.95%
Uptime
3
Deploys per Day
10
Weeks to Cutover
About the Client
Industry
FinTech
Company Size
70 to 120 employees
Background
A platform scaling quickly with increasing traffic and higher reliability expectations. Their infrastructure was manually managed and difficult to reproduce across environments.
Problems to Fix
Unpredictable deployments
Manual changes and environment drift caused outages and slow rollbacks.
Rising cloud spend
Overprovisioned resources and lack of visibility drove unnecessary cost.
Limited observability
Logs and metrics were fragmented, delaying incident response.
Scaling constraints
The system needed a clearer auto scaling strategy and safer capacity planning.
The Mission
Stabilize deployments, improve reliability, and reduce cloud spend with a reproducible infrastructure and strong observability.
How We Approached It
01. Assessment
Week 1 to 2- Architecture review and risk assessment
- Cost and utilization review
- Observability gap analysis
- Target architecture and migration plan
02. Implementation
Week 3 to 8- Infrastructure as code implementation
- Auto scaling and load balancing improvements
- Logging and metrics standardization
- Security and IAM hardening
03. Cutover and tuning
Week 9 to 10- Staged migration and validation
- Load testing and tuning
- Runbooks and alerts
- Post cutover monitoring
Vulnerabilities Discovered
0
CRITICAL
2
HIGH
2
MEDIUM
0
LOW
Environment drift due to manual changes
Manual updates caused differences between staging and production, increasing outage risk.
Manual updates caused differences between staging and production, increasing outage risk.
Overprovisioned compute
Compute was sized for peak without auto scaling, increasing cost significantly.
Compute was sized for peak without auto scaling, increasing cost significantly.
Missing service level alerting
Alerts were not tied to user impact, delaying detection of performance issues.
Alerts were not tied to user impact, delaying detection of performance issues.
IAM policy sprawl
Permissions were broader than needed, increasing blast radius during incidents.
Permissions were broader than needed, increasing blast radius during incidents.
How We Fixed It
Infrastructure as code and environment parity
Moved infrastructure to version controlled definitions with repeatable builds across environments.
Cost and scaling controls
Implemented auto scaling, right sizing, and cost visibility to reduce waste.
Observability
Standardized logs, metrics, and alerts with clear runbooks for incident response.
Measurable Outcomes
The platform reduced cost while improving reliability and deployment confidence through reproducible infrastructure and better observability.
40%
Cost Reduction
99.95%
Uptime
3
Deploys per Day
60%
Faster Incident Response
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