Kubeflow vs Vertex AI Pipelines

Question

I was exploring kubeflow pipelines and Vertex AI pipelines. From what I understand, Vertex AI pipelines is a managed version of kubeflow pipelines so one doesn't need to deploy a full fledged kubeflow instance. In that respect, pricing aside, Vertex AI pipelines is a better choice. But then, in kubeflow, one can create experiments, an equivalent for which I have not found in Vertex AI pipelines. The only kubeflow features that Vertex AI does not support that I have been able to spot in the documentation are "Cache expiration" and "Recursion" but they do not mention anything about experiments. Makes me wonder if there are other differences that are worth considering when deciding between the two.

Answer 1

The team I work with has been investigating Vertex AI and comparing with KubeFlow for the past few months. As you pointed out, Vertex AI experiments are not the same as KubeFlow's. Vertex's experiments are just an interface for Tensorboard instances and Vizier hyperparameter tuning.

There seems to be no equivalent in Vertex AI for grouping pipeline runs into experiments. However, as even the authors of KubeFlow for Machine Learning point out, KubeFlow's own experiment tracking features are pretty limited, which is why they favor using KubeFlow alongside MLflow instead.

Some other differences I have noticed:

• Low level access: Vertex, being a managed service, does not expose the instances where the pipeline steps run, whereas in KubeFlow they are plain old Pods inside of Kubernetes, so in theory you should have more low level control.
• Pipeline registry/pipeline versions: in KubeFlow, you can store pipelines and track versions of compiled pipelines within the tool itself, along with descriptions for the changes. In Vertex, there is no equivalent and you would have to store pipeline objects in GCS (but I would argue you would be better off tracking pipeline code in a version control system anyways).
• Scheduled/recurring pipeline runs are not yet supported on Vertex.

Other than these and what you already mentioned, Vertex seems to be at feature parity with KubeFlow in pipeline execution features, with the great advantage of not having to manage a Kubernetes cluster.