"It works on my machine"
Production runs a different runtime version, staging runs a different OS, and local dev needs a 14-step setup guide. A new hire loses a week before they can even run the app.
Docker & Kubernetes Migration
Containerize at the tier that fits, not the one in fashion.
Most teams don't need Kubernetes on day one. Some teams can't function without it by day 100. You get the tier that matches where you actually are right now, and a path that scales once you outgrow it. Plain Docker keeps things simple. Kubernetes comes in when it has earned its place.
- Simple tier: Dockerize legacy apps and ship them with Compose or managed runtimes
- Advanced tier: production Kubernetes on EKS, GKE, AKS, or bare metal
- Helm charts, GitOps workflows, autoscaling, secrets, RBAC
- Stateful workloads handled properly, including databases, queues, and AI inference
You get container tooling you can actually hire engineers for.
Why
Why containers, and why now.
Containerization isn't a belief system. It's a practical fix for environment drift, low resource utilization, and the gap between how staging and production behave. Done well, it makes your team faster and your infrastructure cheaper. Done badly, it stacks a fresh category of pain on top of the one you started with.
Servers sitting at 12 percent utilization
One service per VM, each one sized for its peak. So most of the fleet is idle most of the time, and the cloud bill or the data center power bill shows it.
Manual deploys with hand-edited configs
Every environment has its own slightly different settings. Nobody can say for sure what's running in prod. A rollback comes down to SSH and a bit of hope.
Kubernetes adopted with no way back out
Someone brought in Kubernetes a year ago. Now you have one engineer who understands it, three Helm charts that half work, and an EKS bill that has doubled. Ripping it out would hurt more than fixing it.
The Process
How we run this engagement.
Each step produces something concrete, comes with a written hand-off, and has to clear a checkpoint before we move to the next one.
01
Tier selection
We start with the blunt question: do you actually need Kubernetes? We look at your services, team size, how often you deploy, and your reliability requirements, then recommend Docker, managed containers, or full Kubernetes based on what genuinely fits.
02
Dockerize the application
We write multi-stage Dockerfiles tuned for small image size and good cache hits, set up local Compose so dev matches production, and wire up a registry with vulnerability scanning and image signing.
03
Orchestration setup
For the simple tier, that's a managed runtime such as ECS, Cloud Run, or App Runner, or single-host Compose with proper logging and restarts. For the advanced tier, it's production Kubernetes with autoscaling, ingress, cert-manager, and external-secrets.
04
Stateful workloads
Databases, queues, caches, and AI inference services each need their own pattern. We design the statefulset layouts, persistent storage classes, backup strategies, and operators. Where it's genuinely safer, we keep a stateful workload outside the cluster altogether.
05
GitOps and hand-off
Deployments become declarative. Git holds the source of truth, and ArgoCD or Flux applies the changes. We document the runbooks, train your team on day-two operations, and stay reachable afterwards.
The Result
What you walk away with.
These are outcomes you can measure, not a slide deck. Here's the change you should expect to see.
3–5xinfra utilization
Higher density, lower spend
Containerized workloads usually run at three to five times the resource utilization of a one-service-per-VM setup, and the saving shows up on the very next bill.
<1 daydeveloper onboarding
New engineers running the stack within hours
With Compose driving local dev, a new hire can clone the repo and have the whole stack running in under a day.
Self-healingproduction
Production that scales itself
A crashed process restarts on its own. A traffic spike triggers horizontal scaling. Your on-call engineers stop being human watchdogs.
An honest tier match
We'll tell you when plain Docker is enough, even though that's the smaller engagement for us. And we'll tell you when Kubernetes will genuinely earn back the operational overhead it brings.
FAQ
Common questions.
How do we know which tier is right for us?
If you have fewer than 10 services and a small ops team, Docker with Compose or a managed runtime almost always wins on both simplicity and cost. Kubernetes starts to earn its keep once you need multi-tenancy, multiple regions, advanced autoscaling, GPU scheduling, or you're past 50 services. We'll give you a straight recommendation, and sometimes that recommendation is to wait.
Can you containerize legacy or stateful apps?
Yes, though we go carefully. Stateful apps and legacy monoliths often call for a hybrid approach, where the app itself runs in containers but the data layer stays outside the cluster. We'll work out with you what's safe to containerize and what should stay where it is.
Do you support self-hosted Kubernetes?
Yes. We use kubeadm, k3s, RKE2, or Talos depending on your hardware and what you need. We've delivered managed Kubernetes on EKS, GKE, and AKS, and bare-metal Kubernetes for clients with strict residency rules or heavy AI workloads.
When is Kubernetes genuinely overkill?
If you run a handful of services, deploy a few times a week, and have a small ops team, Kubernetes usually adds more operational burden than it removes. In those cases plain Docker with Compose or a managed runtime gives you most of the benefit at a fraction of the complexity.
Docker or Kubernetes: how do we actually choose?
Start with Docker and a managed runtime, then move to Kubernetes only when you hit a real driver: multi-region, advanced autoscaling, GPU scheduling, multi-tenancy, or more than roughly 50 services. The trigger should be a concrete need, not a sense that Kubernetes is what serious teams use.
How does autoscaling work once we are containerized?
On Kubernetes we set up the horizontal pod autoscaler against CPU, memory, or custom metrics, and cluster autoscaling to add nodes under load. On managed runtimes the platform scales container instances for you. Either way a traffic spike triggers more capacity without anyone paged.
Can stateful workloads like databases run in containers?
They can, but we are deliberate about it. Databases, queues, and caches need statefulsets, persistent storage classes, backup strategies, and often an operator. Where managed services or staying outside the cluster is genuinely safer, we recommend that instead of forcing everything in.
How do you handle the container registry and image security?
We set up a registry such as ECR, GCR, GitHub Container Registry, or a self-hosted Harbor, with vulnerability scanning on every push and image signing so you can verify provenance. Only signed, scanned images get promoted toward production.
How much ongoing ops burden does Kubernetes add?
Real but manageable when it is set up well. You take on cluster upgrades, node patching, and watching add-ons like ingress and cert-manager. We automate most of it with GitOps and document the runbooks, and we are honest up front about whether that burden is worth it for you.
How do containers improve our resource utilization and cost?
A one-service-per-VM setup leaves most machines idle most of the time. Packing multiple containers onto shared nodes typically lifts utilization three to five times, so you run the same workloads on far less hardware and the saving shows up on the next bill.
Contact UsEmail
[email protected]
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Office
Surabaya, Indonesia
Starting price
From USD 4,000
Typical projects: USD 4,000–25,000