# Autoscaling Workloads

Autoscaling WorkloadsWith autoscaling, you can automatically update your workloads in one way or another. This allows your cluster to react to changes in resource demand more elastically and efficiently.In Kubernetes, you can scale a workload depending on the current demand of resources. This allows your cluster to react to changes in resource demand more elastically and efficiently.When you scale a workload, you can either increase or decrease the number of replicas managed by the workload, or adjust the resources available to the replicas in-place.The first approach is referred to as horizontal scaling, while the second is referred to as vertical scaling.There are manual and automatic ways to scale your workloads, depending on your use case.Scaling workloads manuallyKubernetes supports manual scaling of workloads. Horizontal scaling can be done using the kubectl CLI. For vertical scaling, you need to patch the resource definition of your workload.See below for examples of both strategies.Horizontal scaling: Running multiple instances of your appVertical scaling: Resizing CPU and memory resources assigned to containersScaling workloads automaticallyKubernetes also supports automatic scaling of workloads, which is the focus of this page.The concept of Autoscaling in Kubernetes refers to the ability to automatically update an object that manages a set of Pods (for example a Deployment).Scaling workloads horizontallyIn Kubernetes, you can automatically scale a workload horizontally using a HorizontalPodAutoscaler (HPA).It is implemented as a Kubernetes API resource and a controller and periodically adjusts the number of replicas in a workload to match observed resource utilization such as CPU or memory usage.There is a walkthrough tutorial of configuring a HorizontalPodAutoscaler for a Deployment.Scaling workloads verticallyFEATURE STATE: Kubernetes v1.25 [stable]You can automatically scale a workload vertically using a VerticalPodAutoscaler (VPA). Unlike the HPA, the VPA doesn't come with Kubernetes by default, but is a separate project that can be found on GitHub.Once installed, it allows you to create CustomResourceDefinitions (CRDs) for your workloads which define how and when to scale the resources of the managed replicas.Note:You will need to have the Metrics Server installed to your cluster for the VPA to work.At the moment, the VPA can operate in four different modes:Different modes of the VPAModeDescriptionAutoCurrently Recreate. This might change to in-place updates in the future.RecreateThe VPA assigns resource requests on pod creation as well as updates them on existing pods by evicting them when the requested resources differ significantly from the new recommendationInitialThe VPA only assigns resource requests on pod creation and never changes them later.OffThe VPA does not automatically change the resource requirements of the pods. The recommendations are calculated and can be inspected in the VPA object.In-place pod vertical scalingFEATURE STATE: Kubernetes v1.33 [beta] (enabled by default: true)As of Kubernetes 1.34, VPA does not support resizing pods in-place, but this integration is being worked on. For manually resizing pods in-place, see Resize Container Resources In-Place.Autoscaling based on cluster sizeFor workloads that need to be scaled based on the size of the cluster (for example cluster-dns or other system components), you can use the Cluster Proportional Autoscaler. Just like the VPA, it is not part of the Kubernetes core, but hosted as its own project on GitHub.The Cluster Proportional Autoscaler watches the number of schedulable nodes and cores and scales the number of replicas of the target workload accordingly.If the number of replicas should stay the same, you can scale your workloads vertically according to the cluster size using the Cluster Proportional Vertical Autoscaler. The project is currently in beta and can be found on GitHub.While the Cluster Proportional Autoscaler scales the number of replicas of a workload, the Cluster Proportional Vertical Autoscaler adjusts the resource requests for a workload (for example a Deployment or DaemonSet) based on the number of nodes and/or cores in the cluster.Event driven AutoscalingIt is also possible to scale workloads based on events, for example using the Kubernetes Event Driven Autoscaler (KEDA).KEDA is a CNCF-graduated project enabling you to scale your workloads based on the number of events to be processed, for example the amount of messages in a queue. There exists a wide range of adapters for different event sources to choose from.Autoscaling based on schedulesAnother strategy for scaling your workloads is to schedule the scaling operations, for example in order to reduce resource consumption during off-peak hours.Similar to event driven autoscaling, such behavior can be achieved using KEDA in conjunction with its Cron scaler. The Cron scaler allows you to define schedules (and time zones) for scaling your workloads in or out.Scaling cluster infrastructureIf scaling workloads isn't enough to meet your needs, you can also scale your cluster infrastructure itself.Scaling the cluster infrastructure normally means adding or removing nodes. Read Node autoscaling for more information.What's nextLearn more about scaling horizontallyScale a StatefulSetHorizontalPodAutoscaler WalkthroughResize Container Resources In-PlaceAutoscale the DNS Service in a ClusterLearn about Node autoscaling