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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsKeep configuring Kubernetes’ existing autoscaling mechanisms while they can express your policy safely. Consider a custom controller or broader control plane only when a specific capability gap remains—such as needing domain state that is not available as a metric, coordinating changes across several resources, or acting on resources that do not expose a /scale subresource.
What question are you trying to answer?
Start with the decision the system must make, not with the controller you might write. If the decision is how many replicas a scalable workload needs, Horizontal Pod Autoscaler (HPA) is the natural first choice. If it is how much CPU or memory each pod should request, look at Vertical Pod Autoscaler (VPA). If it is how to react to external events or schedules—including queue depth or stream lag—consider KEDA. Those mechanisms may be combined, provided each field and scaling decision has a clear owner.
A useful boundary is whether your policy can be expressed through supported metrics, VPA recommendations and update modes, KEDA triggers and schedules, or a target resource’s /scale interface. Custom code becomes credible when it must do something those interfaces cannot safely express.
What native autoscaling mechanisms already cover
| Mechanism | Signal and control target | Reaction path | Scale-to-zero and scope |
|---|---|---|---|
| HPA | Resource, custom, container-resource, or multiple metrics; adjusts desired replicas of a scalable target. | A control-plane controller periodically reads metrics and adjusts desired scale. Kubernetes documents a default synchronization period of 15 seconds. | Controls one target’s scale, not coordinated changes to several resources. The documented HPA interface is for scalable objects; a DaemonSet, for example, cannot be scaled by HPA. |
| VPA | Historical and current CPU or memory use, including peaks, variance, OOM events, and cluster capacity; recommends or updates pod resource requests and limits. | A separately installed add-on uses a recommender, updater, and admission controller. Depending on mode and support, changes can be applied to new pods, through eviction and recreation, or in place. | Rightsizes pod resources rather than deciding replica count. Its documented modes are Off, Initial, Recreate, InPlaceOrRecreate, and InPlace. |
| KEDA | External event sources and schedules; can scale supported workloads and Jobs, and can target a custom resource that exposes /scale. |
The KEDA operator handles zero-to-one and one-to-zero transitions. For one-to-N and N-to-one, it manages an HPA that obtains external metrics through KEDA’s metrics API. | Designed to cover zero-to-one event-driven scaling as well as ordinary replica scaling. CPU and memory triggers cannot scale from zero because no running pod exists to provide those metrics. |
| Custom controller or control plane | Potentially domain state, multiple signals, and actions across several resources or beyond a target’s /scale interface. |
Defined by the implementation; the team must design reconciliation, safety, observability, and recovery. | Can coordinate a broader policy, but also makes the team responsible for that policy and its operational lifecycle. |
The HPA details above reflect Kubernetes documentation current August 3, 2026. Kubernetes documents custom and multiple HPA metrics as stable in v1.23, and vertical workload autoscaling with VPA as stable in v1.25. In-place pod vertical scaling is documented as stable in Kubernetes v1.35. These status and version statements describe the Kubernetes documentation; VPA remains a separately installed add-on rather than a built-in Kubernetes control-plane feature.
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When is HPA enough?
Use HPA when the policy is fundamentally “how many replicas should this workload have?” and the signal can be expressed through supported resource or custom metrics. HPA is an API resource and a controller in the Kubernetes control plane. It periodically adjusts the desired scale of a Deployment, StatefulSet, or similar scalable target.
HPA supports resource metrics, custom metrics, container-resource metrics, and multiple metrics. With multiple metrics configured, it uses the largest recommended scale, subject to the configured maximum. This is useful when a workload should not scale down just because one signal is quiet while another still indicates demand. It does not remove the need to choose sensible bounds or ensure that the metrics are available and meaningful.
Kubernetes documents a default --horizontal-pod-autoscaler-sync-period of 15 seconds. That polling interval is not a guarantee that a workload will respond to a burst within 15 seconds: metric collection, metric freshness, control-loop processing, scheduling, and pod startup also affect end-to-end response. For bursty traffic, account for the full path and consider whether the workload needs more capacity before the burst arrives.
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HPA is not an option for every object. It needs a scalable target; a DaemonSet, for example, cannot be autoscaled with HPA. If your target is a custom resource, it can participate when it exposes the Kubernetes /scale subresource.
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Use VPA when pods are consistently under- or over-provisioned and the needed change is to their CPU or memory requests and limits. VPA’s recommender analyzes current and historical consumption, peaks, variance, OOM events, and available cluster resources. Its updater can evict pods or update resources in place when supported, while its admission controller applies recommendations to newly created pods.
VPA is an add-on and requires a metrics source such as Metrics Server. Its modes determine whether recommendations are merely reported, applied to new pods, or applied to running pods using recreation or in-place updates. Choose the mode with the workload’s disruption tolerance and cluster support in mind; do not assume every cluster or workload can apply every kind of update in place.
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Can KEDA replace a custom autoscaler?
Often, if the policy is to scale in response to an event source KEDA supports. KEDA works alongside HPA rather than replacing it: the operator handles transitions between zero and one replica, and a managed HPA handles scaling between one and the configured upper range. KEDA provides ScaledObject, ScaledJob, and TriggerAuthentication custom resources, supports many event sources, and can target custom resources exposing /scale.
This makes KEDA a strong fit when demand is better represented by queue depth, stream lag, message count, database state, API demand, or a schedule than by CPU or memory utilization alone. Check the scaler and its configuration against your actual signal before building another controller. A stated limitation matters for zero replicas: CPU and memory triggers cannot supply a metric from a pod that does not exist, so they cannot initiate scale-from-zero.
When do HPA and VPA conflict?
They can interfere when both mechanisms influence the same scaling outcome. HPA can use CPU or memory utilization as a signal, while VPA changes the resource requests against which utilization is interpreted. A changing request can therefore change the utilization signal that HPA uses to decide replica count. That interaction may produce behavior different from either controller considered alone.
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Before combining them, define who owns replica count and who owns requests and limits. Make the interaction explicit in the design, choose suitable signals and VPA modes, and observe the combined behavior under representative load. Avoid a custom controller silently writing fields that VPA also owns; two controllers changing the same field without an arbitration policy make outcomes difficult to reason about and recover.
What capability gap justifies custom control-plane code?
A custom controller is a reasonable candidate when the required invariant cannot be expressed safely through the native mechanisms—not merely because a different scaling algorithm sounds attractive. Examples include a policy that must use domain state unavailable through supported metrics, coordinate changes across several resources, sequence actions transactionally, use predictive or policy-heavy decisions, or act on objects without a usable /scale interface. This is an engineering decision based on the boundaries of the documented interfaces, not a universal vendor threshold.
For example, suppose the requirement is to keep queue age below a bound while changing both worker replicas and a companion buffer service. KEDA may be able to scale workers from queue-related signals, but if the invariant requires coordinated changes to both resources in a particular order, the gap is coordination and sequencing—not simply missing replica scaling. First check whether the requirement can be expressed using metrics, KEDA triggers and schedules, VPA modes, or a custom resource exposing /scale. Build additional control logic only for the remaining part of the invariant.
How to decide whether to build
- Write the invariant in domain terms. State the condition that must hold, such as keeping queue age below a bound while two related services change together. Include what must remain true during failure and recovery.
- Map the policy to native interfaces. Check HPA custom or multiple metrics, container-resource metrics, VPA recommendation and update modes, KEDA triggers and schedules, and whether the target exposes
/scale. - Name the specific gap. Identify the information, coordination, prediction, sequencing, or actuation requirement the existing interfaces cannot safely meet. If you cannot name one, keep configuring rather than starting a custom controller.
- Assign ownership before implementation. Decide which component owns replicas, requests, limits, disruption, and rollout. Document any intentional overlap and how it is arbitrated.
- Treat the controller as a product. Design its API or CRD, idempotent reconciliation, bounds and rate limits, stale-data behavior, leader election, RBAC, metrics and events, auditability, rollback, upgrade compatibility, and failure recovery.
- Test against your workload. Compare the configured native option with the proposed custom policy using queue latency, SLO error rate, saturation, stabilization time, scaling churn, and cost. Record workload, methodology, and date so the result is not mistaken for a universal threshold.
There is no universal cost, latency, or reliability break-even point established for custom autoscalers. Whether custom control is worthwhile depends on the workload and on the operational cost of owning another controller. The practical test is whether it satisfies a necessary invariant that native interfaces cannot safely express, and whether the measured benefit justifies that ownership.
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