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Core 01 · Cluster Operations
Kubernetes cost optimization — see every dollar, then cut it.
Most teams see one line item: "EKS — $82,400/mo." Atmosly turns that into real Kubernetes cost optimization: it breaks cloud spend down to namespace, service, and team, flags the idle resources and oversized workloads, and turns real p95 usage into right-sizing the dev team can apply themselves. Across AWS, GCP, and Azure — not a spreadsheet.
- ✓ Namespace + service granularity
- ✓ Multi-cloud
- ✓ Apply right-sizing in a PR
$18,420 ↓ 31%
across all clusters · from $26,710 last month
AWS$13.3k
GCP$3.6k
Azure$1.5k
WorkloadTypeNowSave / mo
checkout-api
rightsizing
$1,240
↓ $610
analytics-spark
savings_plan
$2,980
↓ $890
ml-batch-pool
reserved_inst
$3,410
↓ $1,180
staging-* (idle)
idle
$740
↓ $740
How it works
See it, attribute it, cut it — without a FinOps team.
Kubernetes Cost Optimization runs continuously on every connected cluster, powered by in-cluster cost telemetry — Kubernetes cost optimization and cloud cost management in one loop. No tagging project, no quarterly spreadsheet, just the cost visibility your engineers can act on.
$257k/ mo
Compute $88.2k
Kubernetes $65.0k
Network $79.8k
Storage $24.0k
compute$88.2
EKS$65.0
data xfer$51.9
NAT gw$27.9
storage$24.0
Reclaimable — idle & oversized workloads
≈ $61k / mo
01 — Attribute
Spend broken down to namespace, service & team
One bill becomes a map. Automatic cost allocation splits spend by account, by cluster, by namespace, and by service — instance type, load balancer, NAT gateway, data transfer, and storage, each on its own line. Then hand each team its own showback and chargeback view.
- Per-account, per-cluster, per-namespace breakdown
- Compute, NAT, LB, data-transfer & storage line items
- Showback dashboards per team — no more shared mystery bill
CPU · p95 over 14d0.18 / 1.0 requested
Memory · p95 over 14d412Mi / 1Gi requested
used p95idle headroom
APPLYRequest 1.0→0.35 CPU · 1Gi→512Mi
↓ $610 / moopens a PR
02 — Recommend
Right-sizing from real usage — not guesses
Atmosly reads each workload's p95 over time and proposes a concrete new request/limit, with the dollar impact attached. Reserved-instance and savings-plan opportunities surface the same way — ranked by monthly savings.
- Right-sizing, reserved instances & savings plans
- Every recommendation carries its savings_monthly
- Apply as a PR the owning team reviews — no central gatekeeper
daily spend · last 14d + forecast$740 avg
budgetDaily spend exceeded $900 cap · #platform
forecastMonth-end estimate $19.1k · within budget
idle3 orphaned volumes flagged · $210/mo
03 — Stay ahead
Budgets, forecasts & idle detection that page you first
Set budgets and get notified the moment daily spend crosses the line. A monthly forecast tells you where you'll land. Idle workloads and orphaned volumes get flagged before they quietly bleed another month of spend.
- Daily-budget & monthly-estimate alerts
- Month-end cost forecast from current run-rate
- Idle-workload & orphaned-volume detection
What's inside
Everything finance and engineering both want
The cost visibility engineers trust because it comes from the cluster — and finance trusts because it reconciles to the cloud bill. Cloud cost management both sides can finally agree on.
Multi-cloud, one view
Amazon EKS, Google GKE, and Azure AKS spend normalized into a single dashboard — EKS, GKE, and AKS cost optimization side by side, with per-cloud and per-account totals reconciled to the bill.
Namespace & service granularity
Powered by in-cluster cost telemetry, so spend maps to the actual workloads — not just the cloud account.
Right-sizing from p95
Concrete request/limit changes from observed usage, each with its dollar impact, applied as a reviewable PR.
Commitment planning
Reserved-instance and savings-plan recommendations sized to your steady-state, ranked by monthly savings.
Showback by team
Give every team its own cost view so spend has an owner — without standing up a FinOps function.
Budgets & forecasts
Daily-budget and monthly-estimate alerts, plus a month-end forecast from your current run-rate.
Get started
How to get started
Connect one cluster read-only and you'll have a full cost breakdown — idle workloads, oversized requests, and right-sizing recommendations — on your dashboard in about five minutes. No tagging project, no sales call.
1
Connect a cluster, read-only
Install the agent with one Helm command, or connect through the direct cloud integration. It needs read-only access — no write permissions to assess your spend, on AWS, GCP or Azure.
≈ 2 minutes · read-only2
See your full breakdown
Spend maps to namespace, service and team automatically, using metrics from the Prometheus already running in your cluster — idle workloads, oversized requests and orphaned volumes flagged, reconciled to the cloud bill.
≈ 5 minutes · no tagging project3
Apply savings as a PR
Right-sizing, unused-resource cleanup, right-node selection and Spot recommendations open as pull requests the owning team reviews, merges and can revert.
reviewable · reversible
AWS · EKS
GCP · GKE
Azure · AKS
Prerequisite: a Prometheus instance running in the cluster — the agent connects to your existing Prometheus for usage metrics (kube-prometheus-stack works out of the box).
Run in your cluster
Copy# add the Atmosly chart repo
helm repo add atmosly https://charts.atmosly.com
# install the read-only cost agent
helm install atmosly-agent atmosly/agent \
--set token=sk_live_•••••••• \
--set mode=readonly
prod-cluster connected · 142 nodes · 9 namespaces
breakdown in 4m 38s →
The payoff
What teams find in the first month
Real Kubernetes cost reduction, measured — here's what resource optimization from actual usage delivers once the recommendations ship.
−31%
average cluster spend after right-sizing*
5 min
to a full breakdown — no tagging project first
$0
FinOps headcount required to run it
3 clouds
AWS, GCP & Azure in one normalized view
*Representative of customer-reported outcomes. Your results depend on workload mix and current utilization.
Same control plane
Kubernetes Cost Optimization is one of three cores
A deploy, an incident, a security finding, and a cost change are the same story — surfaced in one UI, with one audit trail.
Questions
What teams ask before connecting a cluster
How accurate are the numbers?
Spend is measured from in-cluster cost telemetry and reconciled to your actual cloud bill — per-account and per-cloud totals tie back to what AWS, GCP, and Azure invoice you. Engineers trust it because it maps to real workloads; finance trusts it because it reconciles.
Do I need to tag everything first?
No. Spend maps to namespaces and services automatically, so you get a full breakdown in about five minutes — no tagging project, no cost-allocation spreadsheet to maintain first.
Does it change my infrastructure?
No. Right-sizing and commitment recommendations apply as pull requests the owning team reviews. Nothing about your workloads or cloud account changes without an explicit, reversible approval.
Does it work across multiple clouds?
Yes. AWS, GCP, and Azure spend is normalized into one dashboard, with per-cloud and per-account totals — so a multi-cloud estate reads as a single, comparable view.
How does Atmosly compare to Kubecost?
Teams evaluating Kubecost pick Atmosly when they want cost allocation and cost optimization in one place — spend broken down to namespace, service, and team, plus concrete right-sizing, reserved-instance, and savings-plan actions applied as a pull request, across AWS, GCP, and Azure. See the full Atmosly vs Kubecost comparison.
Does it handle chargeback and showback?
Yes. Every team gets its own showback dashboard, and because spend maps to namespaces and services automatically, you can run chargeback without a tagging project — the cost allocation is done for you. It's cloud cost management engineers actually use, not a spreadsheet finance maintains alone.
Does it cover EKS, GKE, and AKS?
Yes — Atmosly covers EKS cost optimization, plus GKE and AKS, from one dashboard: Amazon EKS, Google GKE, and Azure AKS spend normalized and reconciled to each cloud bill, so container cost management across a multi-cloud estate reads as a single view.
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