Kubernetes Autoscaling

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Datadog Kubernetes Autoscaling continuously monitors your Kubernetes resources to provide immediate scaling recommendations and multidimensional autoscaling of your Kubernetes workloads. You can deploy autoscaling through the Datadog web interface, or with a DatadogPodAutoscaler custom resource.

How it works

Datadog uses real-time and historical utilization metrics and event signals to make recommendations. You can then examine these recommendations and choose to deploy them.

Use Kubernetes Autoscaling alongside Cloud Cost Management to get impact estimates based on your underlying instance type 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 1000 workloads optimized with Datadog Kubernetes Autoscaler.

Compatibility

Distributions: This feature is compatible with all of Datadog’s supported Kubernetes distributions.
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 (required for Remote Configuration)
- API Keys Write (required for Remote Configuration)
- Workload Scaling Read
- Workload Scaling Write
- Autoscaling Manage

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

By default, Datadog Kubernetes Autoscaling shows idle cost and savings estimates using fixed values for CPU and memory costs.

When Cloud Cost Management is enabled within an org, Datadog Kubernetes Autoscaling shows idle cost and savings estimates based on your exact bill cost of underlying monitored instances.

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.