Case Study / Cloud Modernization & DevOps

From 38 Azure VMs to Containers:
Halving Cloud Spend for a Healthcare SaaS Platform

Azure Container Apps for stateless services, Azure Kubernetes Service where fine-grained control mattered, and GKE for GPU-backed AI workloads β€” migrated in small waves with zero maintenance windows and strict PHI compliance.

Client US Healthcare SaaS Provider
Timeline 18 Weeks Assessment β†’ Cutover
Scope HIPAA Compliance & GitOps
Scale 38 VMs / 44 Services / Multi-Cloud
Before Azure Infrastructure Baseline - 38 Virtual Machines πŸ” Expand Initial Azure VM Baseline
53%
Lower Monthly Infrastructure Spend ($27.6k β†’ $13.1k)
3 β†’ 96
Production Deployments Per Month
9 min
Mean Time to Recover (was 2 h 40 min)
99.96%
Platform Uptime over 12 Months

⚠️ The Challenge

Five years of rapid growth left the platform running on 38 hand-provisioned Azure VMs across East US and a Central US DR site. Fleet-wide CPU utilization averaged a wasteful 17%.

Releases required manual RDP/SSH sessions and Saturday night downtime windows, restricting production deployments to 3 times per month. A single GPU VM for AI inference ran 24/7 at 20% of total compute cost despite serving traffic only a few hours daily. Recovering from failed VMs took nearly 3 hours, configuration drift caused weekly environment bugs, and annual HIPAA audits required days of manual documentation.

⚑ The Cyber Oxygen Approach

We architected and executed an 18-week, multi-cloud modernization roadmap:

  • Inventory & Decommissioning (Weeks 1–3): Catalogued 44 services across 38 VMs. Retired 4 obsolete services and 7 idle VMs, including always-on DR duplicates, prior to migration.
  • Multi-Cloud Architecture (Weeks 3–6): Deployed 31 stateless APIs to Azure Container Apps (with scale-to-zero), core identity/audit to AKS, and 4 GPU-backed AI models to GKE for superior cost-efficiency. Everything codified in Terraform.
  • Automated CI/CD & GitOps (Weeks 5–8): Implemented GitHub Actions vulnerability scanning and ArgoCD GitOps reconciliation, eliminating manual drift.
  • Zero-Downtime Wave Cutover (Weeks 9–15): Migrated workloads in weighted traffic waves (10% β†’ 50% β†’ 100%). Migrated SQL Server and PostgreSQL with under 3 minutes of write-freeze; kept PHI inside Azure compliant boundaries.
  • Optimization & Hand-Over (Weeks 16–18): Enabled KEDA autoscaling, configured scale-to-zero GPU pools, and conducted intensive Kubernetes training for client engineers.

Quantified Cloud Modernization Impact (12-Month Measurement)

Measure Before Cyber Oxygen After Cyber Oxygen (12 mo.)
Monthly Infrastructure Spend $27,600 $13,100 (βˆ’53%, β‰ˆ $174k/yr saved)
Fleet-wide Average CPU Utilization 17% 62%
Production Deployments / Month 3 (Saturday windows) 96 (avg. 4–5 per working day)
Lead Time (Merge to Production) 4 days 38 minutes
Mean Time to Recover (P1/P2) 2 h 40 min 9 minutes (βˆ’94%)
Time to Add Capacity for Traffic Spikes 55 min (manual) < 2 minutes (automatic via KEDA)
Platform Availability 99.64% 99.96% (~28 h/yr regained uptime)
Configuration-Drift Incidents ~1 per week 0 (enforced via GitOps)
HIPAA Evidence Preparation 4–5 days (manual) Under 2 hours (Terraform + Git history)
AI Assistant GPU Compute Cost 24/7 Always-on VM βˆ’71% reduction (scales to zero)

Infrastructure Evolution & Telemetry Proof Elements

Azure Container Apps Target Architecture πŸ” Expand Azure Container Apps View

Azure Container Apps Modernization

31 stateless services migrated across production, QA, dev, and sandbox environments running on scale-to-zero workload profiles, eliminating idle compute costs.

AKS Kubernetes Dashboard Overview πŸ” Expand AKS Dashboard

AKS Core Infrastructure Cluster

Kubernetes dashboard monitoring CPU/memory health across stateful services, core authentication, cron jobs, and custom sidecar integrations on Azure Kubernetes Service.

Google Cloud Platform GKE Workloads πŸ” Expand GKE Workloads View

GKE GPU Workloads & Monitoring

Isolated Google Kubernetes Engine cluster hosting GPU-backed AI inference services and telemetry collectors, lowering GPU compute expenses by 71%.

Grafana Cost and Utilization Dashboard Chart πŸ” Expand Grafana Cost Analytics

Spend Reduction & CPU Optimization

Real-time Grafana telemetry tracking monthly compute spend dropping from $27.6k to $13.1k over 18 weeks, while fleet-wide CPU utilization rose from 17% to 62%.

β€œWe used to plan releases around who could stay late on a Saturday. Now someone merges a pull request at 2 p.m. and it’s live before their coffee is cold β€” and our Azure bill is half what it was.”
β€” VP of Engineering, Healthcare SaaS Provider

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