Lift-and-shift is not modernization
Many enterprises have moved workloads to the cloud — but they have not changed how those workloads run. A monolithic .NET application running on a virtual machine in Azure is still a monolith. It does not auto-scale. It does not self-heal. It does not leverage managed services. The cloud bill grows but the architecture remains the same, and the operational burden has simply shifted from your data center to a cloud console.
Cloud-native refactoring means redesigning applications to take advantage of what the cloud actually offers: auto-scaling, managed databases, event-driven compute, container orchestration and global distribution. It is the difference between renting a server in someone else is data center and building software that is genuinely cloud-native.
Assess & Plan
We analyze the existing application architecture, identify components suitable for containerization, serverless or managed services, and define a refactoring roadmap with clear milestones.
Refactor & Containerize
We refactor application components following 12-factor principles, containerize with Docker, implement health checks, structured logging and configuration externalization.
Deploy & Optimize
Kubernetes deployment with auto-scaling, self-healing and rolling updates. Cost optimization through right-sizing, spot instances and serverless offloading of appropriate workloads.
What we deliver
Packaging applications into Docker containers with optimized images, multi-stage builds and security scanning. From development parity to production readiness in a repeatable pipeline.
Refactoring applications to follow 12-factor principles: externalized configuration, stateless processes, port binding, disposability and dev-prod parity. The foundation for cloud-native operation.
Deployment to managed Kubernetes (AKS, EKS, GKE) with proper resource management, health probes, horizontal pod autoscaling, service mesh and ingress configuration.
Identifying components that benefit from serverless execution: event processors, scheduled jobs, API endpoints with variable traffic. Reducing operational overhead and cost for appropriate workloads.
Replacing self-managed infrastructure components with cloud-managed equivalents: managed databases, message queues, caches and search services. Reducing operational burden while improving reliability.
Application can't scale or deploy fast enough for the business?
Talk to an architect who refactors enterprise apps for the cloud.
AXA Partners
AXA Partners ran a claims processing application as a monolithic .NET service on virtual machines. It could not scale for seasonal demand spikes and required manual intervention for every deployment.
We decomposed the monolith into containerized microservices, deployed on AKS with horizontal pod autoscaling, implemented a CI/CD pipeline and offloaded document processing to Azure Functions.
Auto-scaling handles 5x traffic spikes without manual intervention. Deployment frequency increased from monthly to daily. Infrastructure costs reduced by 35% through right-sizing and serverless offloading.
Where we apply it
- Auto-scale .NET or Java apps on Kubernetes for peak demand
- Containerize VM workloads and cut infrastructure costs 35%
- Eliminate batch job servers with pay-per-use serverless
- Drop database admin burden by adopting managed cloud services
- Replace legacy message queues with real-time event streaming
Technologies
Microsoft 
AWS 
Red Hat