Migrating from IaaS to CaaS: A Practical Guide

Over the past decade, many organizations adopted IaaS (Infrastructure as a Service) — virtual machines and resource pools managed by cloud providers or private data centers. This model gave teams flexibility compared to bare-metal, but it also introduced challenges: provisioning delays, environment drift, manual scaling, and heavy reliance on configuration management tools.

Today, CaaS (Container as a Service) platforms like Kubernetes, Amazon EKS, or OpenShift provide a more modern abstraction. Containers enable fast deployments, consistent environments, auto-scaling, and easier service orchestration. Moving from IaaS to CaaS can unlock agility and operational efficiency, but the journey requires careful planning.

In this post, we’ll look at the overall flow of migrating workloads from IaaS to CaaS, along with the key considerations at each step.

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Understanding Stateless vs Stateful Applications

Before migrating, it’s crucial to understand your applications:

Stateless Applications

• Definition: Do not store unique, instance-local state. Any container/pod can be replaced without losing important data.
• Characteristics:
  • Easy to scale horizontally.
  • Resilient to restarts.
  • Configuration and session data are externalized (e.g., environment variables, Redis, databases).
• Examples:
  • REST APIs, web frontends serving static files.
  • Workers processing tasks from a queue.
  • Authentication gateways using JWTs or tokens stored on the client.
• Subtleties:
  • Apps reading from NFS or object storage are still considered stateless, because the state is externalized.
  • Copying object storage files to local disk at startup (read-only) is also stateless — the data is reproducible.

Stateful Applications

• Definition: Store persistent state locally, tied to the instance.
• Characteristics:
  • Harder to scale horizontally.
  • Restarting or rescheduling risks data loss unless persistence or replication is configured.
• Examples:
  • Databases (MySQL, PostgreSQL, MongoDB).
  • Message queues (Kafka, RabbitMQ).
  • Apps writing unique user uploads to local disk.

Rule of Thumb:

Stateless = container/pod can be destroyed and replaced safely.
Stateful = instance has unique data that must be preserved externally.

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1. Assess Current Workloads

Before touching containers, perform an inventory of applications and dependencies:

• Which workloads are stateful vs. stateless?
• Which apps already run well in isolated VMs and can be containerized easily?
• Are there legacy workloads tied to OS-level dependencies?
• How are configurations, secrets, and networking handled today?

👉 The outcome of this phase is a migration plan: what to migrate first (usually stateless services), what needs refactoring, and what may stay on IaaS for now.

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2. Containerize Applications

For each candidate workload:

1. Create a Dockerfile (or OCI-compatible image) that defines how to build and run the application.
2. Externalize configuration using environment variables or config files.
3. Set up CI/CD pipelines to build and publish container images to a registry.

Best practice: start with one pilot service, validate the process, and then apply lessons learned to the broader portfolio.

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3. Prepare the CaaS Platform

Set up or choose a CaaS platform. This could be:

• Managed Kubernetes (e.g., AWS EKS, GCP GKE, Azure AKS)
• Self-hosted Kubernetes/OpenShift on-premises
• Provider-native CaaS (e.g., ECS, Nomad)

Key considerations:

• Networking model (service discovery, ingress, DNS)
• Storage integration for stateful workloads
• Security policies (RBAC, pod security, image scanning)
• Monitoring/observability (Prometheus, Grafana, Datadog, etc.)

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4. Migrate Workloads Incrementally

With the platform ready, begin moving applications:

1. Deploy pilot workloads to CaaS using manifests/Helm charts.
2. Validate functionality, performance, and reliability.
3. Gradually shift traffic using load balancers, service mesh, or DNS cutovers.
4. Roll back quickly if needed (keep IaaS workloads running in parallel until confident).

This phase often follows a canary or blue-green deployment pattern to minimize downtime and risk.

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5. Handle Stateful Services

Databases, message queues, and persistent workloads require extra care:

• Option A: Keep stateful services on IaaS or managed DBaaS, and connect CaaS workloads remotely.
• Option B: Run them inside CaaS with StatefulSets and persistent volumes.
• Option C: Adopt cloud-native managed alternatives (e.g., RDS, Cloud SQL, MSK).

👉 Best practice: start with Option A (leave data where it is), and only migrate stateful services once the team has confidence in container operations.

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6. Optimize and Standardize

Once workloads are running in CaaS:

• Implement autoscaling (HPA, VPA, cluster autoscaler).
• Standardize deployment patterns (Helm, Kustomize, GitOps).
• Improve observability with logs, metrics, and tracing.
• Apply cost optimization strategies (right-sizing pods, spot nodes).

The goal is not just to “lift-and-shift” workloads, but to embrace container-native best practices.

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7. Decommission IaaS Resources

After validating that workloads are stable on CaaS:

1. Retire the old IaaS VMs or scale them down.
2. Update documentation, runbooks, and monitoring to reflect the new platform.
3. Educate teams on day-2 operations in CaaS: scaling, troubleshooting, incident response.

This step closes the migration loop and reduces operational overhead.

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Migration Flow at a Glance

Here’s the high-level flow:

[Assess Workloads]
│
▼
[Containerize Apps] → [Set up CI/CD + Registry]
│
▼
[Prepare CaaS Platform]
│
▼
[Migrate Workloads Incrementally]
│
▼
[Handle Stateful Services Carefully]
│
▼
[Optimize + Standardize]
│
▼
[Decommission RIaaS Resources]

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My Experience Migrating Stateless Apps

During a recent migration from RIaaS to a private CaaS platform, most of our applications were stateless, built with Spring and Java. Some apps had additional complexities:

• Required OS-level dependencies.
• Needed access to NFS for reading shared files.

Issues Encountered

1. Base image complexity:
   • We wanted a common base image to support as many apps as possible and lower maintenance cost.
2. OS dependencies:
   • Some apps required libraries not present in the base image.
3. Private CaaS limitations:
   • NFS was not accessible from the private cluster.
   • Network policies were needed to control access, but the platform lacked UI, making management tedious.
4. Deployment reliability:
   • We needed to ensure the error rate was near 0% during migration.

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Solutions Implemented

1. Common base image with tailored layers:
   • Built a standard Java/Spring base image.
   • Added only the required OS dependencies per app as a layered approach.
   • This reduced overall maintenance while keeping flexibility.
2. Handling NFS apps:
   • Apps that required NFS were left running on RIaaS for the time being.
   • Stateless apps without NFS dependency were fully migrated to CaaS.
3. Network policy management:
   • Used labeling conventions to enforce access rules.
   • On a private platform without UI, careful naming and documentation were essential.
4. Deployment strategy:
   • Instead of rolling deployments, we adopted blue/green deployments.
   • Built subset destination rules to route traffic gradually and safely.
   • This ensured that new versions could be fully tested before switching traffic, keeping error rates minimal.

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Final Thoughts

Migrating from RIaaS to CaaS is not a one-time event, but an iterative journey. Start small, build confidence, and expand. Along the way, focus on:

• Developer experience (self-service deployments, faster feedback)
• Operational maturity (monitoring, scaling, security)
• Business continuity (safe cutovers, rollback plans)

Done right, this migration will give your teams faster deployments, more resilient systems, and greater agility in responding to business needs.