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Platform engineering

Right-size the fleet without giving up change control

KubeOn finds oversized requests, node groups and volumes, prices them against your bill, and opens each fix as a pull request on your repository. Your pipeline ships it, and Automatic mode can open them within your safeguards on the clusters you choose.

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What UnitVane brings to this job

Monthly savings, each with the rule that found it

Savings

Container requests are sized from p95 usage over 7 days, node groups from 14 days of peak requests. Orphaned volumes, abandoned workloads and off-hours schedules are priced per month and recomputed every 30 minutes.

Explore Savings

The problem

Why it is hard today

Right-size clusters through your own pipeline

  • Requests nobody revisits:

    Requests are set at launch and copied forward, so most clusters reserve far more than they use.

  • Automation that bypasses review:

    Tools that resize workloads directly skip the code review and change windows production depends on.

  • Cost questions land on the platform team:

    Every team asks why their bill moved, and the answers live in three different tools.

  • A cost tool is one more thing to run:

    Per-cluster stacks with their own databases add load and on-call to every cluster.

How UnitVane handles it

What you get

Container rightsizing

p95 usage over 7 days, 20% headroom, sensible floors.

Node sizing

Smaller nodes or a better family, as Karpenter or node group changes.

Change proposals

Evidence, risk and a pull request for every saving.

Storage cleanup

Orphaned and released volumes, checked before deletion.

Read-only agent

An hourly CronJob with get, list and watch only.

Self-service answers

Team leads see their own costs and ask Ask KubeOn why they moved.

Outcomes

What changes for your team

Savings that ship through Git

Each change is a reviewed pull request with the evidence attached.

Fewer cost questions to answer by hand

Teams look up their own spend, trends and proposals.

Nothing new to operate per cluster

One small CronJob per cluster; the hub runs once.

Your first weeks

What you see, and when

From the first agent run to findings that need a full usage history.

  1. 01

    Within the first hour

    The cluster, its nodes, namespaces and workloads appear in Diagnostics. Allocated cost per namespace, team and label follows as soon as billing data is in.

  2. 02

    Once billing data is in

    180 days of history are read and allocated, plus 30 days of per-pod split cost history where AWS split cost allocation data exists. A brand-new AWS export can take up to a day to deliver its first file. The allocated history is kept; the raw billing rows leave the hub after 90 days.

  3. 03

    Within 30 minutes of the first allocation

    Savings are computed, starting with findings that need no usage history, such as released volumes that still bill. Change proposals follow at the next daily regeneration.

  4. 04

    After 7 days of usage

    Container requests are right-sized from p95 usage over 7 days of hourly samples.

  5. 05

    After 14 days

    Node group sizing and abandoned workloads, which each need 14 days of data, and over-provisioned volumes, which also need Prometheus volume usage.

UnitVane works with your technology

Every major cloud, any Kubernetes, and the tools and model providers your teams already use.

All integrations

Clouds and Kubernetes: AWS, Microsoft Azure, Google Cloud, Oracle Cloud, Kubernetes, Red Hat OpenShift, Rancher, VMware Tanzu, k3s

Your tools and model providers: Helm, Terraform, Argo CD, Flux, Prometheus, OpenTelemetry, Okta, Slack, Microsoft Teams, OpenAI, Anthropic, Mistral AI

Your questions answered

Keep reading

Expert guides and related solutions

Article

Why cost recommendations don't get shipped

Most cost tools find savings. Few savings reach production. What engineers need before they change a running service, and what changes when a saving arrives as a reviewed patch instead of a dashboard row.

Article

Rightsizing requests with p95, headroom and a floor

Over-sized requests are the most common cause of Kubernetes overspend. Averages break the busiest hour and maximums waste money every other hour. A sizing rule engineers will accept, and what it cannot see.

Solution by role

FinOps teams

Every cloud bill in one ledger, with an owner for every line

Solution by role

Engineering and finance leaders

Cost by business unit and product, with a forecast

Solution by role

AI platform teams

One gateway and one cost model for every model your teams call

All resources

Ready to see it on your own data?

A 30-minute walkthrough on your own billing data, for your clouds, your clusters and your model providers.

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