From Monolith to Microservices on Azure

Author: Prateek Arora
Date: Mar 10, 2023   |   Read time: 6 min

Azure Microservices Architecture

This post walks through our journey migrating a tightly coupled monolithic application into a cloud-native microservices architecture hosted on Azure. The decision wasn't made lightly — it came after years of technical debt, scaling bottlenecks, and painful release cycles. We needed flexibility, faster deployments, and clearer boundaries between services.

After internal discussions and a proof-of-concept, we chose to break the system into containerized services and run them on Azure Kubernetes Service (AKS). We also adopted Terraform to manage infrastructure-as-code, and baked in observability from the beginning using Prometheus and Grafana.

🔧 Key Tooling Decisions

  • Azure Kubernetes Service (AKS): Managed orchestration gave us autoscaling, rolling deployments, and integration with Azure AD.
  • Azure Container Registry (ACR): Hosted private images close to our cluster and enabled geo-replication.
  • Terraform: Allowed us to declare infrastructure safely, with clear change tracking and rollback via version control.
  • GitHub Actions: Enabled a lightweight CI/CD pipeline for image build, test, and deployment into AKS with environment-specific configs.
  • Prometheus + Grafana: Helped set up custom metrics, latency breakdowns, and alerting — crucial for debugging in distributed environments.

📉 Trade-offs

The shift gave us better modularity and scaling flexibility, but came at a cost. Developer onboarding was harder due to the learning curve of Kubernetes, and managing service-to-service communication introduced its own complexities.

Deployment workflows had to be carefully coordinated, especially around versioning APIs between services. We also underestimated how much time observability and logging would consume in the early stages.

📚 Lessons Learned

Start small and plan observability early. We began with two core services before scaling out — this helped us validate the deployment model and tooling. Having metrics and logs from day one saved hours in debugging and post-mortems.

Operational maturity matters more than tools. Kubernetes is powerful but not forgiving. Without team buy-in and basic SRE practices, even managed services can become operational overhead.

✅ Final Outcome

After six months, we saw a 40% drop in deployment rollback incidents, faster delivery cycles, and clearer ownership boundaries across services. It wasn’t painless — but it was worth it.