AWS to Azure Migration and Cost Optimization | AI-Powered Hiring Platform
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.
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.
Reduction in MTTR with monitoring and CI/CD pipelines.
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 reliabilityImprove operational visibility for faster incident responseBuild a scalable foundation aligned with real traffic patternsMigrate from AWS to Azure as part of their business decision
Key Requirements:Reduce monthly cloud expenditure without sacrificing reliabilityImprove operational visibility for faster incident responseBuild a scalable foundation aligned with real traffic patternsMigrate 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 imagesDeploy backend services on Azure Virtual Machines with direct application hosting (no Kubernetes)Introduce endpoints monitoring and CI/CD pipelines to improve reliability and delivery speedThis solution intentionally avoided Kubernetes for backend services since scaling was not a business or technical requirement.
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.
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 analysisArchitecture redesign based on real traffic patternsAzure environment provisioningMigration and validationEndpoints monitoring, CI/CD, and optimization
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?
This helped us answer critical questions:Which services truly need Kubernetes?Where is the money being burned?What level of scaling is actually required?
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.
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.
Azure Environment Setup
Azure Web Apps for all frontend servicesAzure Virtual Machines for backend servicesAzure Managed PostgreSQL (Flexible Server) with backupsStorage lifecycle policies and ACR retention rulesThis created a production-ready foundation before any workload was moved.
Migration & Validation
Services were migrated in phases. We used a single planned downtime window (one day) to:Switch trafficValidate data integrityVerify application behaviorThis ensured a clean cutover with zero unplanned outages.
Endpoint monitoring and CI/CD
We implemented:Uptime Kuma for endpoint health monitoringJenkins pipelines with PR builds and Slack alertsThis provided full visibility and significantly reduced incident response time.
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.
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.
Unplanned downtime
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