Cloud EngineeringIndustry: Technology & SaaS 2023 - 2024

Automated AWS Cloud Infrastructure & Kubernetes Platform

Containerized enterprise workloads with automated IaC Terraform deployment pipelines and 24x7 Prometheus/Grafana monitoring.

A detailed view of a blue lit computer server rack in a data center showcasing technology and hardware. — Photo: panumas nikhomkhai / Pexels

99.99%

Infrastructure Uptime

Guaranteed SLA uptime across containerized production microservices

10 mins

Deployment Speed

Automated CI/CD pipeline code push to production staging

30%

Cloud Cost Savings

Reduction in monthly AWS compute spend via spot instance orchestration

01 · Challenge

The problem on the ground

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Manual server provisioning led to configuration drift, deployment outages, slow developer onboarding, and high idle cloud infrastructure expenses.

02 · Solution

How we engineered it

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Architected declarative Infrastructure as Code using Terraform, containerized microservices with Docker, deployed to AWS EKS, and built automated GitHub Actions CI/CD pipelines.

03 · Architecture

What's running under the hood

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Terraform IaC: Modular infrastructure code defining VPCs, subnets, EKS clusters, and IAM roles.

Kubernetes Deployment: EKS cluster with HPA (Horizontal Pod Autoscaler) and cluster autoscaling.

CI/CD Pipeline: GitHub Actions workflows with automated container image scanning and canary deployments.

Observability Stack: Prometheus metric collection paired with custom Grafana alert dashboards.

04 · Results

What changed for the client

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Achieved 99.99% operational uptime across all production web services.

Reduced release deployment times from 2 hours to under 10 minutes.

Cut monthly AWS cloud spend by 30% using EC2 spot instance node groups.

Eliminated configuration drift across development, staging, and production environments.

Technical Q&A

How does cluster autoscaling optimize cloud costs?

AWS EKS cluster autoscaler adjusts the number of EC2 worker nodes dynamically based on active pod resource requests, scaling down idle nodes during off-peak hours.

How are production deployments protected against broken builds?

CI/CD pipelines enforce automated linting, unit test coverage minimums, image security scanning, and canary deployment rollouts with instant automated rollbacks on error.

Planning a similar cloud engineering project?

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