Kubernetes capacity planning for OpenStack teams: a practical model for cost and performance

Many OpenStack operators move to Kubernetes expecting better utilization, then discover that resource planning becomes harder, not easier. CPU requests are overestimated, memory limits are copied from old VM templates, and autoscaling rules are added without clear business thresholds.

The result is predictable: strong technical work, weak cost efficiency, and occasional user-facing incidents when spikes do not match assumptions. This guide focuses on a practical planning model for teams that already run mixed OpenStack and Kubernetes estates on OneCloudPlanet.

1) Start from service behavior, not from cluster size

A common planning mistake is choosing a target node pool first and forcing applications to fit it. Reverse that logic. Begin with service behavior: steady load, burst pattern, latency sensitivity, and deployment frequency.

For example, a billing API with predictable daytime traffic needs a different buffer than an e-commerce checkout service during campaigns. Mapping workloads into behavior classes helps you set realistic requests, limits, and scaling windows before any cluster redesign.

2) Build a two-layer baseline: platform and workload

Use at least 60–90 days of telemetry and separate platform overhead from application demand. Platform overhead includes control plane, ingress, observability, CI jobs, and security tooling. Workload demand is what each product service actually consumes.

This separation prevents one of the biggest CTR-and-conversion killers in cloud buying journeys: content that promises savings but hides unavoidable overhead. If you are building migration economics in parallel, align the same assumptions with our OpenStack-to-Kubernetes migration cost model.

3) Set right-sizing rules teams can execute weekly

Capacity planning fails when it depends on quarterly hero projects. Use weekly rules instead: flag workloads with <35% average CPU usage and stable memory for four weeks, then downsize one step with rollback criteria already defined.

Do the opposite for pressure signals: sustained throttling, memory OOM trends, and queue lag growth should trigger controlled upsizing. This gives operators a decision loop that is fast enough for production reality and simple enough for cross-team adoption.

4) Align autoscaling with business windows

Autoscaling is not a strategy by itself. It is a control mechanism that needs commercial context. Define business windows first: campaign hours, reporting cutoffs, regional peaks, and maintenance periods.

Then set scaling policies around those windows. In practice, this reduces both missed demand and unnecessary idle capacity. If your infrastructure is managed through policy and templates, codify these windows with Terraform-based governance so decisions stay consistent across releases.

5) Use a buyer-friendly KPI set for weekly reviews

Technical teams often track too many metrics and miss decision quality. Keep weekly reviews focused on a short set: cost per service transaction, p95 latency under peak, scaling reaction time, and waste ratio (requested versus used resources).

This KPI set helps cloud buyers and operators speak the same language. It also improves title-intent alignment in educational content because the value proposition is explicit: predictable performance at controlled cost, not abstract “optimization”.

Hence

Kubernetes capacity planning for OpenStack teams works best when it is behavior-led, measured weekly, and tied to real business windows. You do not need a dramatic redesign to improve outcomes. You need a repeatable model your team can run every week.

For more practical cloud operations guidance, browse the OneCloudPlanet blog.