Should you run Kubernetes on-premises or in the cloud?
Run Kubernetes on-premises when workloads are steady, data-heavy or regulated and you can keep owned servers busy. Run it in the cloud when load is spiky, growth is uncertain or your applications depend on managed cloud services. The deciding factor is usually utilization: an owned server costs the same whether it is idle or full, while cloud capacity costs money only while it runs.
Kubernetes itself behaves the same in both places. What differs is who supplies the infrastructure around it, how fast capacity arrives and how you pay for it.
How on-premises and cloud Kubernetes differ
| Factor | On-premises | Cloud |
|---|---|---|
| New capacity | Weeks to months to order, rack and install servers | Minutes through an API |
| Cost model | Up-front hardware, plus power, space, cooling and maintenance | Per hour or per second of use, plus storage and egress fees |
| Idle capacity | Already paid for, so scaling down saves almost nothing | Scaling down or to zero saves money |
| Control plane | You run it, or you connect your nodes to a managed control plane | The provider runs it in managed services such as EKS, GKE and AKS |
| Load balancers and storage | You install and operate them | Created on demand through the cloud’s APIs |
| GPUs | Bought once, used without hourly cost | Rented by the hour, subject to regional availability |
| Data location | Your building or colocation cage | The provider’s region and contract terms |
On-premises clusters need several components that a cloud adds automatically. Running Kubernetes on-premises lists them: a highly available control plane, Service load balancers, persistent storage, a registry and a backup plan.
When on-premises wins
- Steady base load. Services that run around the clock at predictable levels keep owned hardware busy, which is where it costs least per core.
- Data gravity. Large datasets that many workloads read are cheaper to process where they live than to move into a cloud and pay egress to read back.
- Latency to physical equipment. Factories, labs, hospitals and retail sites often need compute next to machines and sensors.
- Regulation and control. Some contracts and sector rules are easier to meet when you know exactly which building holds the disks.
- Existing investment. A data center with spare power and hardware that still has years of life changes the math.
A frequently cited example is 37signals. After moving seven applications from AWS to about $700,000 of Dell servers in 2023, the company reported that its annual cloud bill fell from about $3.2 million to about $1.3 million, with the same operations team (The Register, October 2024). Results like this depend on steady load and a team that already runs hardware, and published figures rarely include every cost of colocation and hardware refresh.
When the cloud wins
- Spiky or seasonal load. Paying only for peak hours beats owning hardware sized for the peak.
- New products with unknown demand. Capacity can follow growth without a purchase order.
- Global reach. Regions on several continents are available without building sites.
- Managed services. Databases, queues and AI services from the provider save operating effort but tie you to that provider.
- Short-term GPU work. Renting GPUs for a few weeks of training avoids buying cards that sit idle afterwards, when capacity is available. See why cloud GPU capacity is hard to get.
How to compare the cost
Compare cost per unit of work actually used, not list prices. For on-premises, add the hardware price spread over its expected life, power, cooling, rack space or colocation fees, network and support contracts, then divide by the capacity you expect to use, not the capacity you buy. For the cloud, add compute for the hours you run, storage, load balancers and data transfer. The lower your average utilization and the larger the gap between peak and average, the more the cloud’s elasticity is worth. The higher and flatter your utilization, the more owned hardware pays off.
Lead time counts too. On-premises capacity has to be bought ahead of demand, so clusters usually carry headroom that is paid for but unused.
Using both
Many organizations do not choose. They run the steady base load on owned servers and add cloud nodes for peaks, GPU jobs or regions they do not serve from their own sites. This is hybrid cloud Kubernetes. A middle option for teams that want owned hardware without operating Kubernetes itself is a managed control plane with on-premises worker nodes. Moving workloads back from the cloud is covered in cloud repatriation.
How Cloudfleet fits
Cloudfleet Kubernetes Engine (CFKE) runs a managed control plane, and one cluster can combine your own Linux servers with nodes in AWS, Google Cloud and Hetzner Cloud that node auto-provisioning adds and removes as Pods need them. Your base load stays on owned hardware and peaks spill into the cloud without a second cluster. See on-premises and hybrid cloud Kubernetes.