Kubernetes Cost Calculator (node sizing from requests)
Kubernetes costs are mostly compute. A practical first estimate is: (1) size nodes from CPU/memory requests (including max pods per node), then (2) multiply by a per-node hourly rate. Model baseline vs peak to avoid under-budgeting.
1) Size nodes from requests
Use representative requests (not peak limits). Then pick an allocatable % to leave room for kube-system and headroom.
Set a max pods per node value if your CNI enforces pod caps, and compare baseline vs peak to understand scale risk.
This Kubernetes cost calculator focuses on the biggest driver: node spend. Treat the result as a baseline, then add managed control plane, storage, and observability line items.
Inputs
Pods
CPU request (mCPU / pod)
~0.25 cores per pod.
Memory request (MiB / pod)
~0.5 GiB per pod.
CPU limit (mCPU / pod)
Memory limit (MiB / pod)
Node CPU (cores)
Node memory (GiB)
Allocatable (%)
Reserve capacity for kubelet/daemonsets/overhead.
~7.2 cores, 28.8 GiB allocatable.
Max pods per node
Set to 0 to ignore pod limits.
Peak pods multiplier (%)
Model a peak month (traffic spikes, reprocessing, incidents).
Scenario presets
Results
Total CPU requests
15 cores
Total memory requests
30 GiB
Nodes needed (requests)
3
Bottleneck
CPU requests
Allocatable per node
7.2 cores / 28.8 GiB (90%)
Max pods per node
110
Baseline vs peak
| Scenario | Pods | Nodes | CPU req (cores) | Mem req (GiB) |
|---|---|---|---|---|
| Baseline | 60 | 3 | 15 | 30 |
| Peak | 75 | 3 | 18.75 | 37.5 |
| Delta | 15 | 0 | 3.75 | 7.5 |
Limits (burst risk)
| Metric | Total |
|---|---|
| CPU limits | 30 cores |
| Memory limits | 60 GiB |
2) Apply pricing
Multiply node count by an hourly price (or blended on-demand/commitment rate) and expected uptime.
If you don't have a node $/hour yet, start with a representative instance type in your target region, then refine with your real bill.
Inputs
Nodes
Avg $233.47 per node-month.
Price per node ($ / hour)
Utilization (%)
Use <100% if nodes aren't running 24/7.
Hours/day
Days/month
Use 30.4 for an average month.
Monthly hours: 730
Scenario presets
Results
Estimated monthly node cost
$2,801.66
Billable hours (per node)
730 hr (100%)
Cost per node
$233.47
3) Don't forget these common add-ons
- Logs: Log ingestion cost.
- Metrics: Metrics series cost.
- Storage: Storage pricing calculator.
- Egress: Data egress cost.
How to get your inputs
- Inputs: use billing exports, metrics, or logs to get real counts/GB where possible.
- Units: convert throughput (Mbps) or rates (RPS) into monthly units when needed.
- Scenarios: build a baseline and a high-usage scenario to avoid under-budgeting.
Result interpretation
- If node count is driven by pod limits, adjust max pods per node or CNI settings.
- If CPU or memory requests drive nodes, refine request sizing and overprovisioning.
Common mistakes
- Using a single average and ignoring peak/incident scenarios.
- Double-counting or missing adjacent line items (transfer, logs, retries).
Advanced inputs to capture
- Node hours by instance type and utilization drive baseline.
- Add headroom for autoscaling and over-provisioning.
- Persistent volumes and backups add storage costs.
- Network transfer and load balancers add non-node costs.
Scenario planning
| Scenario | Pods | Nodes | Notes |
|---|---|---|---|
| Baseline | Expected | Average | Normal traffic |
| Peak | High | High | Launch or incident |
Validate after changes
- Compare your estimate to the first real bill and adjust assumptions.
- Track the primary driver metric (requests/GB/count) over time.
Next steps
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Example scenario
- Start from pod requests to estimate node count; then multiply by $/hour to get a monthly estimate.
- Compare baseline vs peak pods to see how autoscaling changes node count and cost.
Included
- Requests/limits sizing into cluster totals and node estimate (including max pods per node).
- Baseline vs peak sizing summary and bottleneck highlight.
- Node cost estimate from node count, $/hour, and expected uptime.
Not included
- Control plane fees, load balancers, storage, observability, and egress (add separately).
- Scheduling constraints like affinities, taints, daemonsets, and topology spread that can increase node count.
How we calculate
- Step 1: Convert per-pod requests into total CPU/memory and an estimated node count with pod limits.
- Step 2: Estimate monthly node cost from node count and $/hour pricing.
- Step 3: Compare baseline vs peak scenarios to stress-test the estimate.
- Add separate line items for managed control plane, storage, load balancers, logs/metrics, and egress.
FAQ
Why estimate from requests, not limits?
Scheduling uses requests. Limits matter for throttling/OOM risk but aren't a good baseline for capacity planning.
Why does max pods per node matter?
Kubernetes enforces a per-node pod limit (often 110). Even if CPU/memory is available, pod limits can force more nodes.
What else should I include in a full Kubernetes cost model?
At minimum: control plane, load balancers, persistent volumes, container registry, logs/metrics, and data egress.
Do managed control plane fees matter?
Often yes. Many managed Kubernetes offerings charge a fixed hourly control plane fee. Add it as a separate line item.
How should I think about autoscaling?
Use a baseline (typical) node count for budgeting, then compare a peak scenario to understand risk and headroom.
Should I use on-demand, spot, or commitments?
Use the effective blended $/hour you actually expect to pay. If you mix on-demand + commitments + spot, compute a blended rate and test sensitivity.
Related tools
Related guides
Kubernetes requests vs limits: why requests drive node count (and cost)
A practical explanation of Kubernetes requests vs limits for capacity planning and cost estimation, with common mistakes, a worked sizing workflow, and links to calculators.
EKS node sizing: requests, overhead, and why packing is never perfect
A practical EKS node sizing guide: size from requests, reserve headroom, account for DaemonSets and max-pods limits, and understand why real scheduling often needs more nodes than the math minimum.
Kubernetes requests & limits: practical sizing (and cost impact)
How to size clusters from requests, choose allocatable headroom, and use limits to reason about burst risk - with a calculator, a worked template, and common pitfalls.
Azure Kubernetes Service (AKS) pricing: what to include
AKS cost is more than node VMs: include node pools, networking/egress, storage, and observability. Use this checklist-style model to estimate safely.
EKS vs GKE vs AKS cost: a practical comparison checklist (beyond node price)
Compare managed Kubernetes costs across EKS, GKE, and AKS by modeling the same line items: nodes, control plane, load balancers, storage, observability, and egress. Includes a worksheet template and validation steps for baseline vs peak.
Google Kubernetes Engine (GKE) pricing: nodes, networking, storage, and observability
GKE cost is not just nodes: include node pools, autoscaling, requests/limits (bin packing), load balancing/egress, storage, and logs/metrics. Includes a worked estimate template, pitfalls, and validation steps to keep clusters right-sized.
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Disclaimer
Educational use only. Not legal, financial, or professional advice. Results are estimates based on the inputs and assumptions shown on this page. Verify pricing and limits with your providers and documentation.
Last updated: 2026-02-23