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Choose based on what needs to change: use HPA to change the number of pods, KEDA to activate and scale workloads from event sources such as queues, and VPA to adjust the CPU and memory assigned to pods. KEDA commonly uses HPA for scaling from one replica upward, so it complements rather than universally replaces HPA. There is no documented universal performance or cost winner; compare options against the same workload, service objectives, and capacity constraints.
What changes: replicas, event activation, or pod resources?
| Autoscaler | What it changes | Best-fitting signal or need | Important consideration |
|---|---|---|---|
| HPA (Horizontal Pod Autoscaler) | The replica count of a supported scalable workload | CPU, memory, custom, or external metrics that indicate demand or saturation | It needs usable metrics and a scalable target; resource utilization calculations depend on pod resource requests. |
| KEDA (Kubernetes Event-driven Autoscaling) | Event-driven activation and replica scaling, commonly with HPA managing the one-to-many phase | Queue depth, stream activity, or another supported event source | Zero-to-one activation, polling, and thresholds depend on the selected scaler and its configuration. |
| VPA (Vertical Pod Autoscaler) | CPU and memory requests and limits assigned to pods, depending on configuration | Historical resource use and a need to right-size pods rather than add equivalent replicas | It is an add-on that must be installed and supplied with metrics; applying recommendations can disrupt pods depending on update mode. |
These tools address different control dimensions. More replicas help only when work can be distributed across equivalent pods. Changing resource requests is useful when a pod is undersized or oversized, but does not itself make a workload more parallel. A pending-pod problem can also require more nodes, which is a separate capacity layer.
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When should you use HPA?
Use HPA when a workload can handle demand by adding or removing replicas and you can identify a metric that tracks the need for capacity. Kubernetes documentation describes its default control-loop sync period as 15 seconds; this is a controller polling interval, not a guarantee that additional ready capacity will be available within 15 seconds. The period is configurable through --horizontal-pod-autoscaler-sync-period.
Choose a metric that reflects the bottleneck
CPU utilization can be a useful signal for CPU-bound services, while memory, custom, or external metrics may better represent other limits or demand. Resource utilization is calculated relative to pod resource requests. If a relevant request is missing, CPU utilization may be undefined, and HPA cannot make a scaling decision from that metric.
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With multiple metrics, HPA calculates a desired replica count for each and selects the largest recommendation. If one metric cannot be evaluated, the controller may still scale up based on another valid metric, but an error can prevent a downscale. This matters when adding custom or external signals: a metric pipeline failure is part of the control system’s behavior, not just an observability issue.
Allow for control-loop smoothing
Kubernetes documentation gives a default 10% tolerance around the target ratio: when the measured ratio is sufficiently close to 1.0, HPA skips scaling. The documented default downscale stabilization window is five minutes; within that window, the controller considers recent recommendations and uses the highest to smooth scale-down. Both settings are configurable. These defaults affect replica decisions, but do not establish how quickly a particular application meets its latency objective.
When should you use KEDA?
Use KEDA when an event source is a better indicator of unfinished work than CPU or request utilization alone—especially for workers that should be idle when no work is waiting. For example, a queue-backed worker can be scaled to zero while the queue is empty, activated when messages arrive, and then scaled from one replica upward using metrics exposed to HPA.
KEDA monitors event sources and exposes metrics to Kubernetes HPA; its usual role is to handle event-driven activation and the zero-to-one transition while HPA controls one-to-many scaling. It is therefore often more useful to compare KEDA with a native HPA metrics setup than to treat them as mutually exclusive replica controllers.
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Account for the chosen scaler’s behavior
Polling interval, activation threshold, event-source semantics, and availability of a scaler depend on the KEDA scaler and configuration. A metrics cache can reduce load on scalers, according to KEDA documentation, but is not supported by CPU, memory, or cron scalers. Validate the selected scaler’s behavior and failure modes against the event source you actually use.
When should you use VPA?
Use VPA when changing pod resource allocation is more appropriate than adding replicas—for example, when a workload is not usefully parallelizable or its resource requests are poorly sized. Kubernetes documentation describes VPA as stable since Kubernetes v1.25, but VPA is a separately installed add-on, not a default built-in controller, and it requires a metrics source such as Metrics Server.
The VPA recommender considers historical utilization, cluster resource availability, and runtime events such as out-of-memory conditions. Start by examining recommendations against observed resource use and scheduling outcomes. Whether to apply them automatically depends on the workload’s availability requirements and the consequences of replacing or resizing pods.
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| VPA mode | Behavior | Operational implication |
|---|---|---|
| Off | Records recommendations without applying them | Useful for evaluating suggested requests before enabling updates. |
| Recreate | Applies changes by evicting pods so their owning workload can create replacements | Plan for pod disruption, workload availability, PodDisruptionBudgets, and enough cluster capacity to schedule replacements. |
| InPlaceOrRecreate | Attempts an in-place resize and falls back to eviction when needed | In-place resizing is not assured; account for the fallback behavior. |
| InPlace | Uses in-place resizing without the stated recreate fallback | VPA 1.7.0 documentation labels this mode alpha and requires Kubernetes 1.33 or later plus specific feature gates. |
VPA’s Auto update mode has been deprecated since VPA 1.4.0 and currently aliases Recreate, according to VPA documentation. Do not assume that a mode name or platform feature means a given VPA release can resize a pod in place: Kubernetes documentation identifies in-place pod vertical scaling as stable since Kubernetes v1.35, while the VPA documentation states that VPA does not support resizing pods in place as of Kubernetes 1.37. Check the documentation for the exact Kubernetes and VPA versions you deploy.
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Can you use HPA and VPA together?
They can be combined, but their signals and control actions must not work against each other. Microsoft’s AKS guidance advises against using HPA and VPA against the same CPU or memory metrics. If both are needed, that guidance suggests using VPA in recommendation-only mode or choosing distinct custom metrics for HPA. Treat this as provider guidance and verify the interaction for your Kubernetes distribution and versions.
KEDA already uses HPA for its one-to-many scaling phase, so adding VPA creates a separate resource-sizing loop rather than replacing KEDA’s or HPA’s replica control. Test the combined behavior, including the effect of changing pod resource requests on any utilization-based HPA target.
How to compare autoscaling strategies quantitatively
No apples-to-apples benchmark across HPA, KEDA, and VPA is established by the cited Kubernetes, KEDA, and AKS documentation. A defensible comparison requires a workload-specific test: hold the workload trace, SLO, cluster capacity, and cost method constant, and record the outcomes for each candidate. The following are evaluation measures, not reported benchmark results.
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- Use the same demand or event trace, including quiet periods and bursts, for every candidate.
- Record the Kubernetes, VPA, and KEDA versions, relevant feature gates, metric sources, scaling settings, and node capacity.
- Define the service objective and the conditions that count as a violation before running the comparison.
- Disclose the pricing model and resource accounting used to calculate cost; requested resources, actual use, and billed capacity are not interchangeable measures.
2. Record outcomes that distinguish the strategies
| Measure | What to record | Why it helps |
|---|---|---|
| Service performance | Latency or SLO violation rate over the same test window | Shows whether scaling kept service outcomes within the defined objective. |
| Capacity response | Time from a demand change to adequate ready capacity | Separates controller polling from provisioning, scheduling, and application readiness. |
| Unfinished work | Queue depth, backlog age, or work completion delay | Shows whether event-driven scaling is keeping up with incoming work. |
| Resource allocation | CPU and memory requested and used | Shows whether VPA recommendations or replica changes align allocation with demand. |
| Cost | Cost calculated under the same disclosed pricing and accounting method | Allows a workload-specific cost comparison rather than assuming one controller is cheaper. |
| Control churn | Frequency and magnitude of replica and resource changes | Reveals oscillation or frequent adjustments that a simple average-capacity figure can hide. |
| Disruption and scheduling | Evictions, restarts, pending pods, and scheduling failures | Captures availability and capacity effects, especially when updates replace pods or nodes cannot fit new pods. |
3. Apply workload-specific decision rules
- Favor HPA when replica count is the useful lever and a reliable metric tracks the need for more or fewer replicas.
- Favor KEDA when event backlog or another supported trigger should activate workers, particularly when removing idle replicas matters.
- Favor VPA when pod sizing is the primary issue, and assess recommendations before choosing an update mode that can evict pods.
- If pods remain pending because nodes cannot fit them, evaluate node autoscaling separately. AKS documentation describes Cluster Autoscaler as adjusting node count in response to pending-pod demand and commonly pairing it with HPA.
What these autoscalers do not guarantee
An HPA sync interval is not an end-to-end scaling-time guarantee; readiness also depends on metrics, scheduling, image availability, and application startup. KEDA’s zero-to-one behavior depends on the configured event source and scaler. VPA recommendations do not by themselves guarantee safe scheduling or uninterrupted updates. Pod autoscaling also cannot create node capacity when the cluster has nowhere to schedule additional pods. Set expectations from the measured behavior of the target workload and cluster rather than translating controller defaults into promised savings or response times.
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