Getting Started

Learn how to use the Keptn Lifecycle Toolkit.

kubectl create -f deployment.yaml will “blindly” deploy workloads, but who needs to be notified that this deployment is about to happen? Is your infrastructure ready? Do your downstream services meet their SLOs? Can your infrastructure handle the deployment?

After the deployment, beyond the standard k8s probes, how can you integrate with other tooling to automatically test the deployment? How do you know the deployment is meeting its SLOs? Has the deployment caused any issues downstream? Who needs to know that the deployment was successful (or unsuccessful)?

The Keptn Lifecycle Toolkit (KLT) “wraps” a standard Kubernetes deployment and provides both workload (single service) tests and SLO evaluations. Multiple workloads can also be logically grouped (and evaluated) as a single cohesive unit: a Keptn Application. In other words, an application is a collection of multiple workloads.

The Keptn Lifecycle Toolkit is a tool and vendor-neutral mechanism - it does not depend on particular GitOps tooling - ArgoCD, Flux, Gitlab or others - KLT works with them all.

The Keptn Lifecycle Toolkit emits signals at every stage (k8s events, OpenTelemetry metrics and traces) to ensure your deployments are observable.

Available steps (applicable to both workload and application entities):

  • Pre-Deployment Tasks: e.g. checking for dependant services, checking if the cluster is ready for the deployment, etc.
  • Pre-Deployment Evaluations: e.g. evaluate metrics before your application gets deployed (e.g. layout of the cluster)
  • Post-Deployment Tasks: e.g. trigger a test, trigger a deployment to another cluster, etc.
  • Post-Deployment Evaluations: e.g. evaluate the deployment, evaluate the test results, etc.

What you will learn here

  • Use the Keptn Lifecycle Toolkit to control the deployment of your application
  • Connect the lifecycle-toolkit to Prometheus
  • Use pre-deployment tasks to check if a dependency is met before deploying a workload
  • Use post-deployment tasks on an application level to send a notification


Check Kubernetes Version

Run the following and ensure both client and server versions are greater than or equal to v1.24.

kubectl version --short

The output should look like this. In this example, both client and server are at v1.24.0 so the Keptn Lifecycle Toolkit will work.

$ kubectl version --short
Flag --short has been deprecated, and will be removed in the future. The --short output will become the default.
Client Version: v1.24.0
Kustomize Version: v4.5.4
Server Version: v1.24.0

Install the Keptn Lifecycle Toolkit

Installation Instructions

Install version 0.6.0 and above

In version 0.6.0 and later, you can install the Lifecycle Toolkit using the current release manifest:

kubectl apply -f
kubectl wait --for=condition=Available deployment/klc-controller-manager -n keptn-lifecycle-toolkit-system --timeout=120s

The Lifecycle Toolkit and its dependencies are now installed and ready to use.

Install version 0.5.0 and earlier

You must first install cert-manager with the following commands:

kubectl apply -f
kubectl wait --for=condition=Available deployment/cert-manager-webhook -n cert-manager --timeout=60s

After that, you can install the Lifecycle Toolkit <oldversion> with:

kubectl apply -f<oldversion>/manifest.yaml
kubectl wait --for=condition=Available deployment/klc-controller-manager -n keptn-lifecycle-toolkit-system --timeout=120s

Check out the Getting Started Repository

For the further progress of this guide, we need a sample application as well as some helpers which make it easier for your to set up your environment. These things can be found in our Getting Started repository which can be checked out as follows:

git clone
cd lifecycle-toolkit-examples

Install the required observability features

The Keptn Lifecycle Toolkit emits OpenTelemetry data as standard but the toolkit does not come pre-bundled with Observability backend tooling. This is deliberate as it provides flexibility for you to bring your own Observability backend which consumes this emitted data.

In order to use the observability features of the lifecycle toolkit, we need a monitoring and tracing backend.

In this guide, we will use Prometheus for Metrics, Jaeger for Traces and Grafana for Dashboarding.

make install-observability
make restart-lifecycle-toolkit

The Demo Application

For this demonstration, we use a slightly modified version of the PodTatoHead.


Over time, we will evolve this application from a simple manifest to a Keptn-managed application. We will install it first with kubectl and add pre- as well as post-deployment tasks. For this, we will check if the entry service is available before the other ones get scheduled. Afterward, we will add evaluations to ensure that our infrastructure is in a good shape before we deploy the application. Finally, we will evolve to a GitOps driven deployment and will notify an external webhook service when the deployment has finished.

Install the Demo Application (Version 1)

In the first version of the Demo application, the Keptn Lifecycle Toolkit evaluates metrics provided by prometheus and checks if a specified amount of CPUs is available before deploying the application

To install it, simply apply the manifest:

make deploy-version-1

You can watch the progress of the deployment as follows:

Watch workload state

When the Lifecycle Toolkit detects workload labels (“” and “”) on a resource, a KeptnWorkloadInstance (kwi) resource will be created. Using this resource you can watch the progress of the deployment.

kubectl get keptnworkloadinstances -n podtato-kubectl

This will show the current status of the Workloads and in which phase they are at the moment. You can get more detailed information about the workloads by describing one of the resources:

kubectl describe keptnworkloadinstances podtato-head-podtato-head-entry -n podtato-kubectl

Note that there are more detailed information in the event stream of the object.

Watch application state

Although you didn’t specify an application in your manifest, the Lifecycle Toolkit assumed that this is a single-service application and created an ApplicationVersion (kav) resource for you.

Using kubectl get keptnappversions -n podtato-kubectl you can see state of these resources.

Watch pods

Obviously, you should see that the pods are starting normally. You can watch the state of the pods using:

kubectl get pods -n podtato-kubectl

Furthermore, you can port-forward the podtato-head service to your local machine and access the application via your browser:

make port-forward-grafana

In your browser (http://localhost:3000, Log in with the user ‘admin’ and the password ‘admin’), you can open the Dashboard Keptn Applications and see the current state of the application which should be similar to the following:


In this screen you get the following information:

  • Successful/Failed Deployments
  • Time between Deployments
  • Deployment Time per Version
  • The link to the Trace of the deployment

After some time (~60 seconds), you should see one more failed deployment in your dashboard. You can click on the link to the trace and see the reason for the failure:


In this case, we see the name of the failed pre-deployment evaluation and the reason for the failure. In this case, the minimum amount of CPUs is not met. This is a problem we can solve by changing the treshold in the evaluation file.

Install the Demo Application (Version 2)

To achieve this, we changed the operator in the evaluation file (sample-app/version-2/app-pre-deploy-eval) from < to > and applied the new manifest:

kubectl apply -f sample-app/version-2

After this, you can inspect the new state of the application using the same commands as before. You should see that the deployment is now successful and that the trace is also updated. You should also see in the Grafana Dashboards that the deployment was successful.

Congratulations! You successfully deployed the first application using the Keptn Lifecycle Toolkit!