AWS to Azure Migration and Cost Optimization | AI-Powered Hiring Platform

Project Type

Cloud migration and Cost optimization

Industry

Hr Tech

Tech Stack

Conceptual digital illustration of an AI-powered hiring platform interface showing smart matching and video profiles transitioning through an AWS to Azure migration

About the Client

The client had an AI-powered hiring and job discovery platform built to simplify the way talent meets opportunity. They bridge the gap between employers and job seekers using smart matching, video profiles, and intelligent screening tools. Following our successful AWS to Azure migration, Geekyants further enhanced its infrastructure to make hiring faster, fairer, and more effective at scale.

Overview

The client’s over-engineered AWS EKS platform suffered from high cloud spend and poor visibility, with individual load balancers for every service stalling incident response.

We executed a strategic Azure migration in one week, with only one day of downtime, replacing the lift-and-shift approach with a leaner, redesigned architecture. This transformation slashed monthly infrastructure costs and replaced a reactive culture with a data-driven, production-grade platform that is easier to scale and monitor.

50%Reduction in monthly infrastructure cost

80%Reduction in MTTR with monitoring and CI/CD pipelines.

0Reduction in MTTR with monitoring and CI/CD pipelines.

A technical overview graphic showing the transition from an over-engineered AWS EKS architecture to a cost-optimized Azure cloud setup, highlighting a 50% reduction in infrastructure spend.

BUSINESS REQUIREMENT

The client needed a platform that could sustain growth without burning capital. Their existing setup was expensive, hard to operate, and lacked visibility, making it difficult to justify costs or respond quickly to incidents.

Key Requirements:

Reduce monthly cloud expenditure without sacrificing reliability
Improve operational visibility for faster incident response
Build a scalable foundation aligned with real traffic patterns
Migrate from AWS to Azure as part of their business decision

OUR SOLUTION

We proposed a hybrid cloud-native architecture that uses the right level of abstraction for each workload—combining managed services with lightweight orchestration.

Move frontend services to Azure Web App Services using existing Docker images
Deploy backend services on Azure Virtual Machines with direct application hosting (no Kubernetes)
Introduce endpoints monitoring and CI/CD pipelines to improve reliability and delivery speed

This solution intentionally avoided Kubernetes for backend services since scaling was not a business or technical requirement.

A comprehensive recruitment overview featuring high-level metrics.

A management table listing various open roles.

A detailed company view for HireAI. It includes a large purple banner, an About section, company details (Industry, Founded, Size), a Tech Stack section, and a Benefits & Perks section.

CHALLENGES IN EXECUTION & SOLUTIONS

To address runaway cloud costs and infra complexity, we streamlined the platform by decommissioning managed Kubernetes (EKS) and per-service load balancers in favor of right-sized Web Apps and VM-based deployments. This shift drastically reduced operational overhead by focusing only on essential business components.

To further harden and optimize the environment, we replaced bloated Docker images with secured, multi-stage builds and replaced manual, high-risk deployments with an optimized CI/CD pipeline featuring automated PR builds and real-time Slack alerts.

Runaway Cloud Costs.

Reduced infra complexity.

Bloated and Insecure Docker Images.

Manual and Risky Deployments.

OUR APPROACH

We followed a milestone-driven approach to ensure fast delivery with minimal risk.

Infrastructure audit and cost analysis
Architecture redesign based on real traffic patterns
Azure environment provisioning
Migration and validation
Endpoints monitoring, CI/CD, and optimization

Audit & Discovery Architecture Redesign Azure Environment Setup Migration & Validation Endpoint monitoring and CI/CD

Audit & Discovery

We analyzed the existing AWS EKS setup, mapped every service, and identified hidden cost drivers—especially the multiple Classic Load Balancers created per service. We reviewed traffic metrics and usage patterns to understand real-world load.

This helped us answer critical questions:

Which services truly need Kubernetes?
Where is the money being burned?
What level of scaling is actually required?

Cloud infrastructure audit reports and cost analysis charts identifying high spend in AWS EKS and Classic Load Balancers.

Architecture Redesign

The goal was to eliminate unnecessary abstraction while retaining flexibility for growth.

Using insights from Step 1, we redesigned the platform:

Frontends moved to Azure Web App Services (single-instance, PaaS)
Backend services deployed directly on Azure VMs.
Application Gateway as the single entry point.

New Azure environment featuring Azure Web App Services, Virtual Machines, and Application Gateway for optimized scaling.

Azure Environment Setup

Azure Web Apps for all frontend services
Azure Virtual Machines for backend services
Azure Managed PostgreSQL (Flexible Server) with backups
Storage lifecycle policies and ACR retention rules

This created a production-ready foundation before any workload was moved.

Azure Portal showing configured Web Apps, Managed PostgreSQL Flexible Server, and ACR retention rules for the HR Tech platform.

Migration & Validation

Services were migrated in phases. We used a single planned downtime window (one day) to:

Switch traffic
Validate data integrity
Verify application behavior

This ensured a clean cutover with zero unplanned outages.

Developer dashboard showing Jenkins CI/CD pipeline status, Uptime Kuma health monitoring alerts, and Slack integration for real-time incident response.

Endpoint monitoring and CI/CD

We implemented:

Uptime Kuma for endpoint health monitoring
Jenkins pipelines with PR builds and Slack alerts

This provided full visibility and significantly reduced incident response time.

The Import Pool screen allows users to bulk-import candidates. It shows four integration options: CSV Upload, Resume Parser, ATS Import, and LinkedIn Import

RESULTS

We transformed a costly and complex system into a lean and observable foundation for the product. By simplifying the architecture, we cut monthly infrastructure costs in half, dropping from $1,650 to $845.

This overhaul reduced complexity and incident response times, creating a much safer and more predictable deployment process. Most importantly, the team moved to a zero-touch operations model where intervention is only required if an automated alert triggers.

50%Reduction in monthly infrastructure cost

80%Reduction in MTTR with monitoring and CI/CD pipelines.

0Unplanned downtime

Case Studies.