Free Trial Cluster

During your free trial period, we can provide you with a free cluster to use. If you wish to start a free trial, follow the Getting Started Guide.

You are welcome to provide your own cluster to use during the free trial period, or even combine your own cluster with the free trial cluster in order to try out the multi-cluster capabilities of Pipekit.

Cluster Details

Installed Software

In order to facilitate your trial of Pipekit, the free trial cluster comes with a number of software packages pre-installed and pre-configured. These include:

  • Argo Workflows

  • Minio (s3-compatible object storage)

  • Pipekit Agent

Software Versions

The free trial clusters are constantly maintained, meaning that software versions can change frequently. We aim to keep the cluster and its contents on the latest stable versions.

If you wish to know the exact versions of software installed in the cluster at a given time, you can run the get-versions example from the Pipekit Examples repository.

Cluster Security

Your provisioned cluster is not shared with any other users. Your free trial organization will be the only organization with access to the cluster. Access to the cluster is restricted to the Pipekit team and your organization's users. However, we recommend you do not use the cluster for any sensitive data or workflows. We recommend you fork our Pipekit Examples Github repository and use the workflows therein.

You have no access the cluster itself. You can trigger Pipekit workflows and the Pipekit Agent will execute them on your behalf inside the cluster.

Cluster Availability

While we will make every effort to ensure you have a smooth free trial, we make no guarantees as to the availability of the free trial cluster.


Your workflows should be executed in the argo namespace.

Service Account

You should use the argo-workflow Service Account Name to run your workflows.

Docker Registry Credentials (imagePullSecrets)

To use private container registries, or to avoid rate limits from public registries. We recommend you add your docker registry credentials to your org in pipekit. Any workflows run from Pipekit will then automatically inherit these secrets.

Cluster Limitations

The cluster is intended for trial purposes only. As such, there are a number of limitations in place to ensure the cluster is not abused.

Resource Limits

The cluster is limited in CPU, Memory and Ephemeral Storage. If you breach this limit, the workflows will fail or time out. The limits cannot be increased.

You cannot use persistent storage in the cluster.

In order to facilitate Kubernetes scheduling, if you do not set a resource limit on a resource, we set a default limit of 200m CPU, 200Mi Memory and 100Mi Ephemeral Storage. You can override this by setting a limit in your resource requests in your Workflows.

If you see an error similar to Invalid value: "1Gi": must be less than or equal to memory limit of 200Mi, this indicates that you set a memory resource request above the default limit, but you did not set a higher memory resource limit. In this case you should set a memory limit on the resource that matches or exceeds your memory request.

The workflow examples contained in our Pipekit Examples Github repository will all work within the resource limits of the free trial cluster.


Argo Workflows is configured to use minio as an artifactRepository. The minio endpoint can be reached at http://minio.pipekit.svc.cluster.local:9000. The minio access key and secret key are pipekit and sup3rs3cr3tp4ssw0rd1 respectively.

Minio does not have persistent storage. All artifacts are stored in memory and may be lost at any time. You should not rely on minio for long-term storage of artifacts. It is recommended to keep the total size of artifacts below 1GB.

Argo Workflows

Argo Workflows is pre-configured with the following workflowDefaults:

    activeDeadlineSeconds: 3600
      serviceAccountName: argo-workflow
      strategy: OnWorkflowCompletion
      strategy: OnPodSuccess
      secondsAfterCompletion: 120
      strategy: OnWorkflowCompletion

These cannot be changed in the free trial cluster, however you can overwrite them in each individual workflow.

Deleting the Free Trial Cluster

If you no longer wish to use the free trial cluster, you can delete it by navigating to the cluster settings menu and deleting it as documented here.

Once a free trial cluster has been deleted, it cannot be recreated.

Last updated