Use Kubernetes’ Horizontal Pod Autoscaler (HPA) for straightforward scaling from CPU, memory, or metrics already exposed to Kubernetes. Add KEDA when demand is best measured by an event source—such as a queue backlog—or when a supported workload should activate from zero. KEDA commonly works with HPA rather than replacing it: KEDA connects event-source demand to Kubernetes metrics and activation, while HPA typically manages replica growth once the workload is active.
How HPA and KEDA differ
HPA is a Kubernetes API resource and control-plane controller that adjusts a workload’s replica count using configured metrics. For resource metrics, clusters commonly use Metrics Server; custom and external metrics require the corresponding Kubernetes APIs and providers or adapters. HPA’s documented default controller sync interval is 15 seconds, a polling interval rather than a guarantee of end-to-end scaling time. Kubernetes HPA documentation
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KEDA adds event-source integrations, called scalers, that inspect demand in systems such as queues or streams and make that information available for Kubernetes scaling. With common Deployment and StatefulSet workloads, KEDA usually works alongside HPA: KEDA supports event-driven activation, and HPA handles scaling across the active range. The exact source support and behavior depend on the scaler and KEDA version. KEDA concepts · KEDA deployment scaling
Choose based on the workload’s demand signal
| Decision | HPA alone | KEDA, usually with HPA |
|---|---|---|
| Good fit | Always-on services whose load tracks CPU, memory, or metrics already available through Kubernetes. | Workloads whose demand is represented by a supported event source, such as queue or stream backlog, especially when they may sit idle. |
| Metrics and integration | Resource metrics commonly use Metrics Server; custom and external metrics need the corresponding API provider or adapter. | Requires KEDA components, a scaler for the chosen source, source connectivity, and credentials where applicable. |
| Scaling to zero | Restricted to object or external metrics, with `minReplicas: 0` and the `HPAScaleToZero` feature gate enabled on both the API server and controller manager. | Can activate supported workloads from zero when event activity appears; actual behavior depends on the scaler and configuration. |
| Operational footprint | Lower when the cluster already has the required metrics infrastructure. | More components and source-specific configuration, in exchange for broader event-source integration. |
When HPA alone is the better choice
Start with HPA if the service should remain available and its load is adequately represented by resource metrics or metrics already integrated into Kubernetes. Avoid introducing event-source infrastructure when it does not solve a real scaling requirement.
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Check the metric path
For CPU utilization, the relevant containers need CPU resource requests: utilization is calculated relative to those requests, and missing requests can leave the metric undefined. HPA can evaluate multiple configured metrics and uses the highest desired-replica recommendation, subject to the configured bounds. It also needs a scalable target with a Kubernetes `scale` subresource.
Account for control behavior
The documented default HPA downscale stabilization window is five minutes. That setting helps avoid rapid scale-down after a temporary drop; tune scale behavior and replica bounds for the service rather than treating the default as a response-time promise. Kubernetes HPA documentation
When KEDA is worth adding
Choose KEDA when an event source is a better indicator of work than CPU or memory alone—for example, pending queue messages—or when an idle workload should start in response to new events. The KEDA scaler catalog lists integrations, but it is a changing catalog, not a guarantee that every source, authentication method, or feature works with every KEDA release. Check the documentation for the exact scaler and version you plan to deploy. KEDA scaler catalog
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchKEDA can also be used with ScaledJobs and custom resources that expose a scale subresource; it is not limited to the common Deployment and StatefulSet pattern. Confirm that the target type and scaler combination meet the workload’s needs in the relevant KEDA documentation. KEDA deployment scaling
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What scale-to-zero does—and does not—mean
HPA’s documented scale-to-zero path is narrowly conditional: it supports object or external metrics, requires `minReplicas: 0`, and requires the `HPAScaleToZero` feature gate in both the API server and controller manager. Check the cluster’s Kubernetes version and configuration before relying on it. KEDA provides event-driven activation for supported workloads from zero, but zero replicas do not mean instant service: event detection, scheduling, image availability, and application startup all affect when a pod is ready. Kubernetes HPA documentation · KEDA deployment scaling
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Operational checks before deploying
- Verify the metric or scaler: confirm the metric API provider for HPA, or the exact KEDA scaler’s supported source, version, and authentication model.
- Set replica bounds and behavior: define appropriate minimum and maximum replicas and scaling behavior for the application.
- Keep one replica authority: when HPA manages a Deployment or StatefulSet, Kubernetes recommends removing the fixed `spec.replicas` value from its manifest; a later apply can otherwise reset the count. Kubernetes HPA walkthrough
- Test the full activation path: measure source-to-ready-pod delay under realistic conditions, including cold starts; do not infer readiness time from a controller interval or polling setting.
- Monitor failure signals: observe metric freshness, scaler and API errors, queue age or backlog, pod readiness, and application latency.
KEDA polling, activation thresholds, metric caching, and scaler capabilities vary by configuration and integration. Validate the exact combination instead of assuming all scalers behave alike.
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