Case Study: How We Built a Stable and Scalable System on AKS for an E-Commerce Client

1. Introduction to the Problem

Our client, a mid-sized e-commerce company, was facing a classic cloud horror story:

A monolithic application running on several virtual machines that were either overloaded or needlessly consuming resources.
Seasonal traffic spikes? A nightmare. Black Friday meant outages, stress, and lost revenue.
Infrastructure chaos: manual scaling, manual version deployments, and zero monitoring.
Costs? Out of control.

We chose Azure Kubernetes Service (AKS). Why? Because it is a stable, flexible, and automated solution that makes sense not only technologically but also financially.

2. Technical Architecture

Azure Kubernetes Service (AKS) — The primary orchestrator. Node Pools for separating different workloads.
Azure Container Registry (ACR) — Private storage for Docker containers.
Azure Load Balancer (ALB) — Load distribution across nodes with health checks.
Azure Application Gateway (WAF) — Protection against common threats. SSL termination.
Azure Monitor & Log Analytics — Real-time metrics monitoring. Central log storage. Grafana dashboards.
Azure Key Vault — Secure storage for API keys, certificates, and credentials.
Horizontal Pod Autoscaler (HPA) — Automatic scaling based on CPU and memory metrics.
Azure DevOps Pipelines — Automated CI/CD pipeline with Helm Charts.
Azure SQL Database — Managed database with high availability and replication.
Azure Virtual Network (VNet) — Network isolation with private connectivity between components.

3. CI/CD Pipeline: Deployment Automation

Continuous Integration: Every commit triggered automatic Docker image builds. Test scripts verified code quality.

Continuous Deployment: Every approved build was automatically pushed to ACR. Helm Charts ensured consistent deployment to AKS. Rollback was ready with a single click.

The result? A new version of the application could be deployed multiple times per day, without outages and without stress.

4. Monitoring and Observability

Real-time metrics: CPU, RAM, I/O operations, network activity
Error and event logging from every pod into Log Analytics
Automatic alerts when critical thresholds are exceeded
Grafana dashboards for both developers and managers

5. Results

99.9% application availability during seasonal traffic spikes
35% cost reduction thanks to automated scaling
Faster deployments (up to 10× per day) without downtime
Full visibility into performance and costs through monitoring and alerting
Enterprise-level security through Key Vault and WAF