Kubernetes Cost Optimization

Most overspend is not waste.
It is misplacement.

You can shave 10% off a Kubernetes bill by trimming resource requests. The bigger money is in where the workload runs, what the platform costs to operate, and whether moving is cheap enough that you ever actually do it.

60%
OpEx reduction
11x
Cost efficiency
30 vCPU
Free forever

Where the money actually goes

Six costs that survive every rightsizing exercise, because none of them are about how big your pods are.

Premium infrastructure for invisible workloads

Logging, metrics, backups, batch jobs, and build runners sitting on Tier 1 cloud at Tier 1 prices. None of them are latency-sensitive. All of them are billed as if they were.

Platform overhead multiplied by fleet size

A self-run delivery controller is roughly 500-800 MB of RAM in every cluster. On one big cloud cluster you shrug. Across 50 edge sites it is the difference between node sizes.

Buying compute from a spec sheet

vCPU counts are not comparable across providers. Two nodes with identical specs can differ by more than 2x on real Kubernetes workloads, and the cheaper one is sometimes the faster one.

Paying a list price for falling behind

An EKS cluster that ages out of its 14-month support window is auto-enrolled in extended support at 6x the control plane fee - roughly $4,400 a year extra for the same workloads.

Repricing is somebody else's decision

In June 2026 a Hetzner CCX33 went from EUR 62.49 to EUR 138.49 a month. If your infrastructure story depends on one provider staying cheap, your budget is a snapshot, not a plan.

The glue code is the lock-in

Moving a workload is rarely the hard part. Rebuilding registry auth, deploy keys, secrets wiring, and the pipeline for a new provider is - so the savings never get taken.

The levers that move the number

Ordered by what they return. The first two are worth more than everything a dashboard will ever recommend.

Place workloads by what they actually need

Keep user-facing APIs on premium cloud. Move logging, backups, CI runners, and model training to cost-effective infrastructure. Same Stack definition, different cost profile.

Make the move cheap enough to take

A Stack is one object deployed to many clusters. Cloning production onto a second provider is minutes of work, not a quarter-long migration project - which is the reason the saving actually gets realised.

Delete the delivery control plane

Ankra's native engine drives Helm from the agent that already runs in your cluster. Seven pods per cluster stop existing, on every cluster, forever.

Benchmark before you buy

We publish reproducible Kubernetes benchmarks across six clouds. Price per benchmarked unit of work is the number that matters, and it is rarely the number on the pricing page.

Keep upgrades boring

Staying current is included; staying old is a product you pay for. Stacks make the component-version graph explicit, so an upgrade is a routine instead of an archaeology project.

Pricing you can forecast

Free forever up to 30 vCPU, then per-vCPU pricing. No per-cluster fee, so splitting one cluster into three for isolation does not triple your platform bill.

Workload placement has its own page, with the tier-by-tier breakdown and the before/after bill:multi-cloud and on-prem.

The line items nobody budgets

The costs that are not on the pods

Every one of these is charged per cluster or per fleet, which is exactly why they scale with the thing you keep adding.

Idle control plane fees

An EKS control plane is about $73 a month per cluster, charged whether or not anything is running on it. Environment-per-cluster isolation quietly multiplies that.

Extended support surcharge

$0.10/hour becomes $0.60/hour the moment a cluster ages out of standard support. Same cluster, same workloads, six times the fee, roughly $4,400 a year.

Delivery controller RAM

500-800 MB per cluster for a GitOps control plane you also have to upgrade and be on call for. Multiply by every cluster in the fleet.

Egress on internal traffic

Backups, log shipping, and cross-region replication bill at premium egress rates on hyperscalers, and at zero on several alternatives.

Observability on Tier 1 storage

Prometheus, Loki, and log archives are storage and compute heavy and completely invisible to your users - the single most common workload sitting on the wrong tier.

Engineer-months of platform maintenance

The DIY toolchain is not free because it has no invoice. Upgrades, glue code, and on-call for the platform are a real recurring line item.

Portability is the hedge

A price is a snapshot, not a contract. The teams that shrugged at the last repricing were not on a cheaper provider - they were able to leave.

01

Import what you already run

One Helm command per cluster, on any provider: EKS, GKE, AKS, Hetzner, OVH, UpCloud, bare metal. Ankra does not need a new cluster to start being useful, and there is no firewall change.

02

Turn each environment into a Stack

Standard Helm charts and manifests, versioned in your Git repo, with the dependency order captured explicitly. This is the step that converts a migration project into a clone operation.

03

Move one workload class at a time

Start with the cheapest thing to be wrong about: logging, backups, or build runners. Cascading variables handle the per-cluster differences - domains, storage classes, registry credentials.

04

Keep the option open

Because the stack is portable by construction, the next repricing letter is a pricing decision rather than an engineering emergency. That optionality is the real saving.

What you keep either way: standard Helm charts and Kubernetes manifests in your own Git repository. Nothing about the cost work depends on staying with Ankra, which is the only honest way to sell portability.

Free forever up to 30 vCPU

Find out what your fleet actually costs to run

Import your clusters in five minutes, see the whole fleet in one place, and move the first workload that is on the wrong tier.