Driver 01
Idle capacity
Instances running around the clock at low average CPU to cover occasional peaks.
Migration playbook: EC2 / virtual machines → Amazon EKS
EC2 to Amazon EKS Kubernetes migration
Containerize EC2 server fleets and move them to an autoscaling Amazon EKS cluster, so you stop paying for idle capacity sized for peaks.
Migration drivers
Why teams make this move
Driver 02
Manual patching and drift
Configuration drifting between servers that are maintained over SSH and ad-hoc scripts.
Driver 03
Slow scaling
Auto Scaling groups that take minutes to bring new instances into service.
Execution sequence
How the migration runs
Each phase ends with a check you can verify — data parity, error rates, latency — and the rollback path is agreed before any traffic moves.
- 01Phase
Containerization and hardening
Packaging applications as minimal, standardized container images.
- 02Phase
EKS cluster and VPC in Terraform
Provisioning a multi-AZ Amazon EKS cluster with Karpenter for node autoscaling.
- 03Phase
Helm charts and GitOps
Defining deployments declaratively and syncing them with Argo CD.
- 04Phase
Traffic cutover
Shifting traffic from the EC2 load balancers to ingresses managed by the AWS Load Balancer Controller.
Risk prevention
Pitfalls that derail this migration
Risk 01
Missing resource requests and limits
Not measuring CPU and memory needs, which leads to node memory pressure and OOM kills.
Risk 02
Local disk dependencies
Applications writing uploads to local VM disks instead of object storage (S3) or EFS.
Risk 03
No readiness or liveness probes
Traffic reaching new pods before their database connection pools are ready.
Before and after
What we measure
We take a baseline before any change and report the same numbers after cutover, from your own tools. They are the evidence of whether the migration worked — not figures promised in advance.
- Compute cost
- Monthly EC2 spend vs cluster spend for the same traffic
- Utilization
- CPU and memory requested vs used, per node
- Scale-up time
- Pending pod to ready, measured under load
Questions
Frequently asked migration questions
It packs several services onto fewer, right-sized nodes and lets you run interruptible workloads on spot capacity. The saving depends on how idle your current instances are, which we measure first.
Kubernetes marks the node unreachable and, after a timeout (five minutes by default, and tunable), reschedules its pods onto healthy nodes. Running several replicas across Availability Zones keeps the service up while that happens.
Rehearse the cutover before the real one
Tell us about your data volume, traffic and timeline. An engineer will reply within one business day to set up a call about the migration plan and its rollback path.