Kubernetes costs cannot be expressed as a single number, because they come from six separate blocks: nodes, load balancers, storage, egress traffic, your team's operational time, and, if you use managed Kubernetes, a markup the provider charges on top. If you only compare node prices, you almost always underestimate your real costs, because the last block, your own operational time, never shows up on any cloud provider's invoice.
I run production-grade Kubernetes clusters on rented cloud infrastructure and help companies get a realistic read on their cluster costs. You can find all my articles on running Kubernetes clusters collected on the Kubernetes page. I deliberately skip concrete dollar or euro figures here, because they shift with every provider, every region, and every contract, and without a current source they would go stale fast and mislead you. What you get instead is the structure to build your own numbers on.
Kubernetes costs: the six blocks that matter
The first block is the nodes themselves, the virtual or physical servers your Pods run on. The second block is the load balancer, which almost every cluster needs for incoming traffic. The third block is storage, for persistent volumes as well as backups and object storage. The fourth block is egress traffic, data leaving your cluster or your provider's data center. The fifth block is your team's operational time, for updates, monitoring, troubleshooting, and capacity planning. The sixth block is the managed markup a provider charges for taking the control plane and part of the operational load off your hands.
These six blocks carry different weight for every company. A data-heavy service with lots of external traffic feels the egress block far more than an internal tool with hardly any outbound traffic. A small team without prior Kubernetes experience feels the operational time block far more than an established platform team. That is exactly why a single number for "what does a Kubernetes cluster cost" is not very useful, while a breakdown along these blocks is.
Node costs: what actually matters
Node cost is a function of size and count on the surface, but two points get overlooked routinely. First, every node needs a baseline of resources for Kubernetes's own processes, the kubelet, the container runtime, and system services, before any of your applications even get a slice. That baseline eats a bigger percentage of small nodes than large ones, which means many small nodes tend to cost more than a few large ones at the same total capacity. Second, with managed Kubernetes you usually do not pay separately for the control plane nodes, while with a self-operated cluster, say k3s on rented infrastructure, you pay for those control plane nodes out of your own pocket even though they carry no application load themselves.
The most reliable way to see what your applications actually need is your cluster's own metrics, not a guess from your desk. A simple first step:
kubectl top nodes
kubectl describe node <nodename> | grep -A 5 Allocatable
The first command shows the current CPU and memory usage of every node, the second shows the capacity actually available to Pods after the system reserve is subtracted. If usage sits far below allocatable capacity for a long time, you are paying for capacity that never gets used, a common and easily fixable cost driver.
Load balancers and storage: the underestimated line items
A load balancer looks minor at first glance, but quickly becomes its own cost block once you spin up a separate one for every service instead of routing incoming traffic through a shared ingress controller. Most cloud providers charge every load balancer on an ongoing basis, regardless of how much traffic actually flows through it.
For storage, it pays to look at three things separately: the persistent volumes your applications use, the backups of those volumes, and any object storage you use for larger datasets or for storing backups themselves. All three go through different interfaces technically, and most providers bill them separately, by storage used and often additionally by read and write operations. If you only watch your application volumes, you can easily miss that backups end up consuming more space over time than the original data itself.
Egress traffic: the hidden cost trap
Egress traffic, data leaving your cluster toward the internet or toward another data center, is the least transparent cost block at nearly every cloud provider. Incoming traffic is free at most providers, outgoing traffic is not, and the exact pricing differs sharply between providers. For applications with heavy traffic to external users or to services outside the provider's own data center, this block can grow into one of the largest ones over time, even though it is almost always missing from initial cost estimates.
A practical lever against it: traffic that stays inside the same data center or the same region usually incurs much less egress, or none at all, at most providers. Where you place your services and their users therefore has a direct effect on this cost block.
Operational time: the biggest line item nobody prices in
The cost block that shows up in the fewest comparisons is the time your team spends on the cluster: applying updates, checking security advisories, planning capacity, investigating incidents. With managed Kubernetes, the provider takes over part of that, everything around the control plane, while a self-operated cluster leaves that work with you entirely. This time never appears on any cloud provider's invoice, yet it sits inside every cluster you run regardless.
How heavily this block weighs depends almost entirely on your team's experience. A team already running Kubernetes in production needs noticeably less time for the same tasks than a team building that knowledge from scratch. That is exactly why an honest look at your team's existing knowledge is worth doing before you pick an operating model, more on that in Kubernetes On-Premises or Cloud?.
Managed markup: what you pay for less operational work
The managed markup is the price a provider charges for taking operational time off your plate, usually a small base fee per cluster, sometimes folded into slightly higher node prices instead. Economically, you are trading part of your operational-time block for a fixed, predictable markup. Whether that pays off depends on how expensive your own operational time is compared to the markup, and how much you value predictability over control.
A cost worksheet, as structure only
Even without dollar or euro figures, there is a structure you can use to build your own numbers. Sum your node costs over the billing period, add the ongoing cost of your load balancers, add storage by volume used for volumes, backups, and object storage separately, estimate your egress traffic and add it at your provider's rate, multiply your team's estimated operational hours by their internal hourly rate, and, with managed Kubernetes, add the provider's markup on top. The result is not an exact number, it is a structure that shows you which block actually weighs the most in your case, and that is exactly where your first optimization pays off.
As a formula for your own calculation, with your numbers from the provider price list:
Monthly cost =
application nodes (count x price)
+ control plane nodes or managed base fee
+ load balancers (count x price)
+ storage (used GB x price, volumes plus backups)
+ egress (GB above the included allowance x price)
+ operating time (hours per month x internal hourly rate)
The last item is the one missing from every quote and the heaviest one in reality. Put an honest number on it, even though nobody invoices you for it.
The table below shows who carries which block under each operating model:
| Cost block |
Managed Kubernetes |
Self-operated (e.g. k3s on rented cloud) |
| Nodes for applications |
you |
you |
| Nodes for the control plane |
usually included in provider pricing |
you, additionally |
| Load balancers |
you |
you |
| Storage (volumes, backups, object storage) |
you |
you |
| Egress traffic |
you |
you |
| Operational time for the control plane |
mostly the provider |
fully you |
| Operational time for the application layer |
you |
you |
| Additional markup |
yes, usually a base fee |
no |
Frequently asked questions
Is Kubernetes free?
The software itself is open source and carries no license fee. Running it is still never free, because you need nodes, networking, storage, and operational time regardless, whether at a cloud provider or on your own hardware. "Is Kubernetes free" only ever applies to the software, never to running it.
How much does a rented Kubernetes cluster cost per month?
That depends too heavily on the provider, region, node size, and traffic to answer responsibly as a single number. It is more realistic to work through the six cost blocks from this article for your own scenario, using your provider's current price list, rather than relying on generic figures.
When does managed Kubernetes pay off despite the markup?
Whenever your own operational time would cost more than the markup, or when your team simply does not want to or cannot build its own Kubernetes expertise. For teams that already have that expertise, the math often flips, because the markup then pays for work the team could already do efficiently on its own.
How do I keep an eye on my Kubernetes costs over time?
Most reliably with the same tools you already use for monitoring: check resource usage per node and per namespace regularly, hunt down unused load balancers and orphaned volumes, and watch egress traffic per service, instead of only looking at it once the provider's invoice arrives.
Where to go next
Before you build your cost model, it is worth answering the bigger question of which operating model fits you in the first place, covered in Kubernetes On-Premises or Cloud? and in Kubernetes for SMEs: An Operating Model. If you want to cut node costs without giving up capacity, Kubernetes Autoscaling with HPA covers the right lever for matching Pods to actual load automatically.
My suggestion: go through the six cost blocks from this article for your own cluster and mark which one currently gets the least attention. In my experience, it is almost always either egress traffic or your own operational time.
My Kubernetes Practical Guide (Rheinwerk Computing) covers the business case for and against Kubernetes in chapter 2, but I do not work through concrete cost blocks like the ones in this article there, since prices change faster than a book can stay current. You can find all the information about the book on my Kubernetes page. The Kubernetes documentation on managing resources covers how to set resource requests properly, and you can find the source code of Kubernetes itself in the Kubernetes project on GitHub.
