The simplest way to put it is that Kubeflow (whose team we have a ton of respect for) is a tool for helping devops engineers build their ML deployment platform on kubernetes, whereas Cortex is an ML deployment platform. Kubeflow plugs into an existing k8s cluster, whereas Cortex abstracts k8s (and automates AWS-layer devops too).

With Cortex, we wanted to build something so that developers can take a trained model—regardless of if it's trained by their DS team or if it is a pre-trained model—and deploy it as a production API without needing to understand k8s. Because Cortex manages the k8s cluster, we can do the legwork for features like spot instances, request-based cluster autoscaling, GPU support, etc, and expose them as simple yaml configuration.