Designing and implementing a Kubernetes solution for an enterprise, particularly when migrating existing microservices running on virtual machines with Docker Compose, is a complex undertaking that demands a structured, multi-phased approach. This transition is not a single-day activity but rather a process that unfolds over multiple levels, potentially taking weeks or even months.
What You'll Learn in This Journey
In this business case, you’ll discover how I orchestrated the full migration of 100+ microservices from a legacy Docker Compose environment to Amazon EKS. You’ll get a step-by-step look at our planning and discovery phase, Kubernetes architecture design, CI/CD pipeline implementation with GitOps, security hardening, and phased rollout strategy across dozens of AWS accounts. Whether you’re a DevOps engineer or a cloud architect, this case study delivers practical, real-world insights for managing and scaling containerized workloads in production-grade Kubernetes clusters.
:
Properly assess your current environment and requirements
Avoid common pitfalls that derail Kubernetes projects
Structure your clusters for different environments
Implement production-grade configurations
Scale your implementation globally
Why This Approach Works
Most Kubernetes implementations fail because teams jump straight into creating clusters without proper planning. This blueprint follows a methodical approach that:
1. Starts with Assessment
We begin with comprehensive requirement gathering (Level Zero) to understand your current architecture, resource needs, and business requirements before writing a single YAML file.
2. Validates with PoC
Before committing to full implementation, we validate our approach with a controlled Proof of Concept using representative services (Level One).
3. Gradual Rollout
We implement Kubernetes progressively through development, staging, and finally production environments, refining our approach at each stage.
Key Success Factors
From my experience leading these migrations, here are the critical factors that determine success:
Team Structure Awareness: Knowing which teams own which services is crucial for namespace design and RBAC
Resource Measurement: Accurate CPU/memory metrics prevent cluster overallocation or starvation
Criticality Classification: Categorizing services by importance guides migration sequencing
Cost Analysis: Comparing VM costs with Kubernetes projections ensures financial viability
Ready to Begin?
This structured approach has helped me successfully migrate dozens of microservices to Kubernetes with minimal disruption. Let's start with Level Zero: Requirement Gathering to lay the proper foundation for your implementation.
we didn’t jump straight into creating clusters or deploying workloads. Instead, we followed a structured, multi-phased approach—starting with Level Zero,
the most critical phase is requirement gathering and strategic planning. This phase laid the groundwork for the entire migration effort. It involved gaining a deep understanding of the current system in identifying all existing microservices, mapping out team ownership, evaluating business criticality, analyzing resource utilization (CPU, memory, disk), and estimating current versus future infrastructure costs.
This process helped us avoid a chaotic or rushed migration. Instead, we defined a phased onboarding plan, ensured proper team-level isolation with Kubernetes namespaces, sized the clusters based on real usage metrics, and prepared a clear cost-benefit analysis to align stakeholders. Only after completing this detailed groundwork did we move to the next phase—building a Proof of Concept (PoC).
The following sections walk through this Level Zero process in depth, showing how thoughtful planning drives successful Kubernetes adoption at scale.
Key Activities
Inventory all microservices in your application
Identify teams responsible for each service
Categorize services by business criticality
Measure current resource utilization
Calculate cost comparisons (VMs vs Kubernetes)
Document everything in a migration plan
Present and discuss with stakeholders
Microservice Inventory
User Interface
React-based frontend
Medium Priority
Payment Service
Handles transactions
Critical
Order Service
Processes orders
Important
Shipment Service
Manages deliveries
Medium Priority
Notification Service
Sends alerts
Low Priority
Resource Utilization
| Service |
CPU (cores) |
Memory (GB) |
Disk (GB) |
Current Cost |
| Payment Service |
4.2 |
8 |
50 |
$120/mo |
| Order Service |
2.8 |
4 |
30 |
$85/mo |
| UI Service |
1.5 |
2 |
10 |
$45/mo |
| Shipment Service |
3.1 |
6 |
40 |
$95/mo |
Ready for Next Level
Once you've completed requirement gathering and have stakeholder approval, you're ready to proceed to Level One: Proof of Concept where you'll validate your approach with a small subset of services.
Level One: Proof of Concept (PoC) begins right after completing the requirement gathering from Level Zero. The goal here is not to jump straight into building Dev, Staging, or Production clusters, but to first validate whether the existing microservices can actually run on Kubernetes. For this, a small set of 15–20 representative microservices are selected across various business criticality levels (critical, important, medium, and less critical) and application types (stateless apps, databases, cache, queues). A lightweight Kubernetes cluster is then created—typically with 3 control plane nodes and 3 worker nodes, each with about 8 CPUs and 8 GB RAM. For each selected service, Kubernetes manifests such as Deployment, StatefulSet, Service, and Ingress are written. An Ingress Controller like AWS ALB is also configured. Once deployed, these services are tested by the QA team to verify basic functionality and traffic flow. If any pods crash or misbehave (like showing CrashLoopBackOff), they are debugged, and liveness/readiness probes are tuned. This PoC stage typically takes 2–4 weeks and helps ensure the services are Kubernetes-compatible before larger environments are built. A successful PoC confirms that your migration path is valid, paving the way for Level Two: building the Dev cluster and scaling gradually..
PoC Setup
Select 15-20 representative microservices
Include mix of criticality levels
Include both stateful and stateless services
Create small Kubernetes cluster (3 control plane, 3 worker nodes)
Prepare Kubernetes manifests (Deployments, Services, Ingress)
Choose ingress controller (e.g., ALB on AWS)
Involve QA team for testing
PoC Cluster Configuration
| Node Type |
Count |
CPU |
Memory |
Purpose |
| Control Plane |
3 |
2 cores |
4GB |
Cluster management |
| Worker Nodes |
3 |
8 cores |
8GB |
Running PoC workloads |
Common PoC Challenges
CrashLoopBackOff Issues
Implement proper liveness and readiness probes to ensure services are functioning correctly before traffic is routed to them.
Resource Constraints
Set appropriate resource requests and limits based on your Level Zero measurements to prevent pods from being evicted or starving other services.
Stateful Services
For databases and other stateful services, ensure proper PersistentVolume provisioning and test failover scenarios.
Ready for Next Level
After successfully validating your approach in the PoC environment and addressing any issues, you're ready to proceed to Level Two: Dev Kubernetes Cluster where you'll implement your first full environment.
Level Two: Setting Up the Development (Dev) Kubernetes Cluster marks the true beginning of Kubernetes implementation, following the successful Proof of Concept in Level One and guided by the resource analysis from Level Zero. In this phase, a fully functional Dev cluster is built to host a larger subset of microservices, allowing development teams to actively test their applications. The cluster typically includes three control plane nodes (managed by the cloud provider if using EKS/AKS/GKE) and at least three worker nodes, sized according to the total resource needs calculated earlier—for example, allocating 48 CPUs and 70 GB RAM if your services require 40 CPUs and 60 GB RAM. Within this cluster, namespaces are created per team (e.g., payments-dev, transactions-dev) to logically isolate services. This isolation supports RBAC (Role-Based Access Control), ensuring developers can access only their team’s namespace. Further, Resource Quotas are applied to prevent any one team from over-consuming resources, and Limit Ranges along with resource requests and limits are defined at the pod level to control how much CPU and memory each pod can use. Once services are deployed using the manifests prepared during the PoC, teams validate functionality by accessing their respective environments. Although powerful, the Dev environment is inherently unstable—designed for experimentation and frequent changes—so it’s common to encounter and resolve issues. This entire setup and validation phase can take up to 30 days, setting the foundation for the upcoming Staging environment in Level Three..
Key Configuration
Size cluster based on Level Zero requirements
Create namespaces per team (logical isolation)
Implement RBAC (integrate with IAM via OIDC)
Define Resource Quotas per namespace
Set Limit Ranges for pods
Configure Requests and Limits for all workloads
Dev Cluster Sizing Example
Based on Level Zero measurements totaling 40 CPU cores and 60GB RAM needed:
| Node Type |
Count |
CPU per Node |
Memory per Node |
Total CPU |
Total Memory |
| Worker Nodes |
3 |
16 cores |
24GB |
48 cores |
72GB |
Namespace Strategy
payments
Payment team services
Resource Quota: 8 CPU, 16GB RAM
transactions
Order processing team
Resource Quota: 6 CPU, 12GB RAM
ui
Frontend team
Resource Quota: 4 CPU, 8GB RAM
monitoring
Observability tools
Resource Quota: 4 CPU, 8GB RAM
Ready for Next Level
With your Dev cluster stable and teams successfully working in their namespaces, proceed to Level Three: Staging/QA Environment to establish a production-like environment for testing.
A production-like environment for thorough testing and issue reproduction, with two possible implementation approaches.
Implementation Options
Option 1: Shared Cluster
Use the same Dev cluster with additional resources, separating environments via namespaces (e.g., dev-payments, stage-payments).
Pros: Simpler, less overhead
Cons: Requires strict RBAC, potential instability
Option 2: Separate Cluster
Create a dedicated staging cluster with similar configuration to what production will use.
Pros: Complete isolation, more stable
Cons: More resource intensive
Recommended: Separate Staging Cluster
Staging Cluster Architecture
Staging Best Practices
Size closer to production requirements
Implement same RBAC policies you'll use in production
Mirror production monitoring and alerting
Test deployment procedures
Validate backup/restore processes
Perform load testing
Ready for Next Level
With a stable staging environment that successfully mirrors your production needs, you're ready to proceed to Level Four: Production Kubernetes Environment with confidence.
The critical production deployment with high availability requirements and production-grade configurations.
Key Production Requirements
Multi-AZ deployment (mandatory)
Pod distribution across AZs using topology spread constraints
Production-grade observability (Prometheus, Grafana)
Proper liveness and readiness probes
Resource management with potential autoscaling
Disaster recovery planning
Multi-AZ Configuration
Multi-AZ Production Cluster
| Node Pool |
AZ |
Node Count |
Instance Type |
Purpose |
| worker-pool-1 |
us-east-1a |
3 |
m5.xlarge |
General workloads |
| worker-pool-2 |
us-east-1b |
3 |
m5.xlarge |
General workloads |
| worker-pool-3 |
us-east-1c |
3 |
m5.xlarge |
General workloads |
| db-pool-1 |
us-east-1a |
2 |
r5.large |
Stateful services |
Topology Spread Constraints Example
topologySpreadConstraints:
- maxSkew: 1
topologyKey: topology.kubernetes.io/zone
whenUnsatisfiable: DoNotSchedule
labelSelector:
matchLabels:
app: payment-service
This ensures your payment-service pods are evenly distributed across availability zones.
Ready for Next Level
With your production environment stable and handling traffic successfully, consider Level Five: Scaling Production for global high availability and multi-region deployment.
Advanced configurations for global scale, high availability, and multi-region deployments.
Global Deployment Strategy
Multiple distinct Kubernetes clusters per region
Global Load Balancer fronting regional clusters
DNS-based routing (e.g., Route53 geolocation)
Data replication between regions
Regional failover testing
Multi-Region Architecture
Global Kubernetes Deployment
| Region |
Cluster Name |
Node Count |
Primary AZs |
Traffic Weight |
| us-east-1 |
prod-useast |
12 |
1a, 1b, 1c |
60% (Americas) |
| eu-west-1 |
prod-euwest |
9 |
1a, 1b |
30% (EMEA) |
| ap-southeast-1 |
prod-apsoutheast |
6 |
1a, 1b |
10% (APAC) |
Beyond Level Five
Kubernetes implementation is an ongoing journey. Additional considerations include:
Service mesh implementation (Istio, Linkerd)
Policy enforcement (Kyverno, OPA Gatekeeper)
GitOps workflows (ArgoCD, Flux)
Cost optimization strategies
Security hardening