Running Kubernetes in production requires a significant investment in infrastructure, tooling, and expertise. One of the first decisions you face is whether to use a managed Kubernetes service (EKS, AKS, GKE) or operate your own clusters. This choice has major implications for cost, control, and operational burden — and the right answer depends on your organisation's size, skills, and requirements.
What Managed Kubernetes Provides
Managed Kubernetes services handle the control plane — the API server, etcd, scheduler, and controller manager — so you only need to manage the worker nodes where your applications run. Specifically, managed services typically include:
- Control plane availability — the provider runs the control plane across multiple availability zones with automated failover and a financially backed SLA (typically 99.95% or higher).
- Automated upgrades — managed services simplify Kubernetes version upgrades, often providing one-click or automated upgrades for the control plane and node pools.
- Integration with cloud services — native integration with the provider's identity system (IAM/Azure AD), load balancers, storage, networking, and monitoring services.
- Security patching — the provider handles security patches for the control plane components, reducing your operational burden.
Cost Comparison
The total cost of Kubernetes goes far beyond the sticker price of the control plane:
Managed Kubernetes Costs
- Control plane fee — EKS charges approximately $73/month per cluster. AKS offers a free tier for the control plane (with a paid uptime SLA tier). GKE offers one free zonal cluster and charges for additional or regional clusters.
- Worker node compute — you pay standard cloud compute pricing for your worker nodes. This is typically the largest cost component regardless of whether you use managed or self-managed Kubernetes.
- Networking and load balancing — cloud load balancers, NAT gateways, and data transfer charges apply.
- Add-on services — managed logging, monitoring, service mesh, and container registry services add to the monthly bill.
Self-Managed Kubernetes Costs
- Control plane compute — you provision and pay for the VMs or bare-metal servers running the control plane. A production-grade control plane requires at least three nodes for high availability.
- Engineering time — this is the largest hidden cost. Setting up, securing, upgrading, and troubleshooting a self-managed cluster requires significant ongoing engineering effort. Budget at least one to two full-time engineers for cluster operations.
- Tooling — you need to implement your own solutions for monitoring, logging, certificate management, ingress, and storage that managed services provide out of the box.
- Upgrade overhead — Kubernetes releases a new minor version every four months. Self-managed clusters require careful, hands-on upgrade procedures that can take days to execute safely.
For most organisations, when you factor in engineering time, managed Kubernetes is less expensive than self-managed. The break-even point typically favours self-managed only at very large scale (hundreds of nodes) or when you have specific requirements that managed services cannot meet.
When Managed Kubernetes Is the Right Choice
- Small to medium teams — if your team has fewer than five Kubernetes-experienced engineers, the operational burden of self-managing clusters is disproportionately high.
- Cloud-native workloads — if your applications run entirely in a single cloud provider, managed Kubernetes integrates seamlessly with the provider's ecosystem.
- Fast time to production — managed services let you go from zero to a production-ready cluster in hours rather than weeks.
- Compliance requirements — managed services come with pre-built compliance certifications (SOC 2, ISO 27001) that would take significant effort to achieve with self-managed clusters.
When Self-Managed Kubernetes Makes Sense
- On-premises or edge deployments — when workloads must run on your own hardware, managed cloud services are not an option. Tools like k3s, RKE2, or Tanzu support these scenarios.
- Multi-cloud portability — if you run clusters across multiple cloud providers and want a consistent management experience, self-managed with a tool like Cluster API provides more flexibility.
- Specialised requirements — GPU workloads, custom schedulers, specialised networking, or other requirements that managed services do not fully support.
- Large-scale operations — organisations running many clusters with a dedicated platform team may find the economics favour self-managed, especially when using tools like internal developer platforms to automate cluster lifecycle management.
A Hybrid Approach
Many organisations adopt a pragmatic hybrid approach: use managed Kubernetes for production workloads in the cloud while running lightweight self-managed clusters (k3s, kind) for local development and testing. This combination gives you the operational simplicity of managed services where it matters most while keeping development environments flexible and cost-effective.
How ICTLAB Can Help
ICTLAB helps Belgian organisations evaluate, deploy, and operate Kubernetes as part of our DevOps and cloud services. Whether you choose managed or self-managed, we design the cluster architecture, implement security and monitoring, and build the automation your team needs to operate Kubernetes confidently in production.