Kubernetes Autoscaling

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Datadog Kubernetes Autoscaling automates the scaling of your Kubernetes environments based on utilization metrics. This feature enables you to make changes to your Kubernetes environments from within Datadog.

How it works

Datadog Kubernetes Autoscaling provides cluster scaling observability and workload scaling recommendations and automation. Datadog uses real-time and historical utilization metrics to make recommendations. With data from Cloud Cost Management, Datadog can also make recommendations based on costs.

Automated workload scaling is powered by a DatadogPodAutoscaler custom resource that defines scaling behavior on a per-workload level.

Each cluster can have a maximum of 100 workloads optimized with Datadog Kubernetes Autoscaler.

Compatibility

Distributions: This feature is compatible with all of Datadog’s supported Kubernetes distributions.
Cluster autoscaling: This feature works alongside cluster autoscaling solutions, such as Karpenter and Cluster Autoscaler.
Workload autoscaling: This feature is an alternative to Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA). Datadog recommends that you remove any HPAs or VPAs from a workload before you use Datadog Kubernetes Autoscaling to optimize it.

Requirements

Remote Configuration must be enabled for your organization. See Enabling Remote Configuration.
Helm, for updating your Datadog Agent
(For Datadog Operator users) kubectl CLI, for updating the Datadog Agent
The following user permissions:
- Org Management (org_management)
- API Keys Write (api_keys_write)
- Workload Scaling Write (orchestration_workload_scaling_write)

Setup

Ensure you are using Datadog Operator v1.8.0+. To upgrade your Datadog Operator:

helm upgrade datadog-operator datadog/datadog-operator

Add the following to your datadog-agent.yaml configuration file:

spec:
  features:
    orchestratorExplorer:
      customResources:
      - datadoghq.com/v1alpha1/datadogpodautoscalers
    autoscaling:
      workload:
        enabled: true
    eventCollection:
      unbundleEvents: true
  override:
    clusterAgent:
      image:
        tag: 7.58.1
    nodeAgent:
      image:
        tag: 7.58.1 # or 7.58.1-jmx
    clusterChecksRunner
      image:
        tag: 7.58.1 # or 7.58.1-jmx

Admission Controller is enabled by default with the Datadog Operator. If you disabled it, re-enable it by adding the following highlighted lines to datadog-agent.yaml:

...
spec:
  features:
    admissionController:
      enabled: true
...

Apply the updated datadog-agent.yaml configuration:

kubectl apply -n $DD_NAMESPACE -f datadog-agent.yaml

Add the following to your datadog-values.yaml configuration file:

datadog:
  orchestratorExplorer:
    customResources:
    - datadoghq.com/v1alpha1/datadogpodautoscalers
  autoscaling:
    workload:
      enabled: true
  kubernetesEvents:
    unbundleEvents: true
clusterAgent:
  image:
    tag: 7.58.1
agents:
  image:
    tag: 7.58.1 # or 7.58.1-jmx
clusterChecksRunner:
  image:
    tag: 7.58.1 # or 7.58.1-jmx

Admission Controller is enabled by default in the Datadog Helm chart. If you disabled it, re-enable it by adding the following highlighted lines to datadog-values.yaml:

...
clusterAgent:
  image:
    tag: 7.58.1
  admissionController:
    enabled: true
...

Update your Helm version:

helm repo update

Redeploy the Datadog Agent with your updated datadog-values.yaml:

helm upgrade -f datadog-values.yaml <RELEASE_NAME> datadog/datadog

Ingest cost data with Cloud Cost Management

Datadog’s Kubernetes Autoscaling works with Cloud Cost Management to make workload scaling recommendations based on cost data.

See Cloud Cost setup instructions for AWS, Azure, or Google Cloud.

Cost data enhances Kubernetes Autoscaling, but it is not required. All of Datadog’s workload recommendations and autoscaling decisions are valid and functional without cost data.

Usage

Identify resources to scale

Use your Kubernetes Autoscaling page to better understand the resource efficiency of your Kubernetes deployments. The Summary view displays information about optimization opportunities and estimated costs across your clusters and workloads. The Cluster Scaling view provides per-cluster information about total idle CPU, total idle memory, and costs. Click on a cluster for detailed information and a table of the cluster’s workloads. You can also use the Workload Scaling view to see a filterable list of all workloads across all clusters.

Click Optimize on any workload to see its scaling recommendation.

Deploy recommendations with Autoscaling

After you identify a workload to optimize, examine its Scaling Recommendation. You can also click Configure Recommendation to make changes to the recommendation before you apply it.

You can use Kubernetes Autoscaling to deploy a scaling recommendation in one of two ways:

Click Enable Autoscaling. Datadog automatically applies the scaling recommendation to your workload.
Deploy a DatadogPodAutoscaler custom resource. Click Export Recommendation to see values for your CRD.

Deploy recommendations manually

As an alternative to Autoscaling, you can also deploy Datadog’s scaling recommendations manually. When you configure resources for your Kubernetes deployments, use the values suggested in the scaling recommendations. You can also click Export Recommendation to see a generated kubectl patch command.