Interactive workbench
Kubernetes Capacity Calculator
Model cluster capacity from requests, limits, replicas, and headroom.
How many worker nodes do we need for this workload once headroom is included?
How to use & What you leave with
Configuration Suite
Surgically adjust parameters for Kubernetes Capacity Calculator
Methodology
This tool automates the core architectural decisions required for high-performance scale. It eliminates spreadsheet errors and gut feeling by using post-2026 optimized algorithms.
Output Precision
- Surgical accuracy for production loads
- Ready for direct export to Terraform/Docs
Total pod replicas across the workload.
Kubernetes CPU request for each pod.
Kubernetes memory request for each pod.
Buffer above actual demand for bursts and scheduling.
vCPU capacity of each worker node.
Memory capacity of each worker node.
Extra node capacity for rolling updates.
PRO TIP: Changes reflect in real-time. Use shortcuts for fast navigation.
Worker nodes
4
Primary constraint
CPU
CPU utilization
98%
Memory utilization
49%
Autoscale range
4–5
Pods per node
6
Key Insights
- Workload needs 15.6 vCPU and 31.2 GB (including 30% headroom). CPU is the binding constraint.
- At 4 nodes: CPU utilization 98%, memory 49%. Utilization is high — consider larger nodes or more headroom.
- Autoscaler range: 4–5 nodes. 6 pods per node average — verify this fits within ENI/IP limits for your CNI plugin.
Actionable Next Steps
- Consider compute-optimized nodes (c-family) to better match the CPU-bound workload.
- Export the HPA config and tune targetUtilization based on your latency SLOs.
- Review the node shape comparison table to find the optimal cost/utilization balance.
Analysis Metrics
Benchmarks & Comparison
| Node shape | Nodes needed | CPU util % | Mem util % | Pods/node |
|---|---|---|---|---|
| 4 vCPU / 8 GB | 4 | 98% | 98% | 6 |
| 8 vCPU / 16 GB | 3 | 65% | 65% | 8 |
| 16 vCPU / 32 GB | 3 | 33% | 33% | 8 |
| 32 vCPU / 64 GB | 3 | 16% | 16% | 8 |
Actionable Exports
Decision Log
This tool automates the core architectural decisions required for high-performance scale.
Surgical Precision
Eliminate spreadsheet errors and gut feeling with post-2026 AI.
Seamless Workflow
Move results straight to Terraform, Jira, or Confluence.
The Lab Result
Post-2026 Audit Complete
This workflow has been surgically optimized by AutonomOps AI for Architecto Power Users.
FAQ
Questions teams ask before they adopt this workflow.
When should teams use Kubernetes Capacity Calculator?
This tool is most useful when the team needs an answer they can carry into diagrams, documentation, and design reviews without rewriting the same context three times.
Who benefits most from Kubernetes Capacity Calculator?
Architects, platform teams, and technical leads benefit most because they need explicit assumptions, clear review cues, and artifacts that survive implementation handoff.
How does Kubernetes Capacity Calculator connect back to Architecto?
Architecto uses the free content surface as the top of a larger workflow. Once the team needs richer diagrams, schema visibility, change comparison, or technical documentation, the matching product module keeps the same decision context alive.
Related workflow paths
Keep moving with the next tool, guide, or product module.
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Guide
What cluster topology means in Kubernetes and Cloud Native
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Guide
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Comparison
Architecto vs dbdiagram
Architecto vs dbdiagram with a workflow-first comparison across diagrams, architecture review, technical documentation, and code-adjacent implementation evidence.
Continue in Architecto
Use the exported artifact from Kubernetes Capacity Calculator as the first review input, then move into Scalability Analyzer when the team needs a deeper design, diagram, or review workflow.
Open matching moduleRelated modules