1. Deployment Environment: Gradio is primarily focused on simplifying the deployment of machine learning models by providing a user-friendly interface for building and sharing web-based applications. On the other hand, Kubeflow is designed as an open-source Kubernetes-native platform for deploying, scaling, and managing machine learning workloads in a production environment.

2. User Interface: Gradio emphasizes the ease of use for non-technical users, offering a drag-and-drop interface for quickly creating interactive demos and prototypes. In contrast, Kubeflow targets data scientists and ML engineers, providing a set of robust tools and APIs for building and deploying complex machine learning pipelines at scale.

3. Scope of Functionality: Gradio focuses on streamlining the process of building and sharing machine learning applications, with a strong emphasis on real-time model inference. In comparison, Kubeflow offers a comprehensive ecosystem of tools for end-to-end machine learning lifecycle management, including data processing, training, and serving models in a distributed manner.

4. Community Support: Gradio has a growing community of users and contributors who actively engage in sharing models, providing feedback, and improving the platform. Meanwhile, Kubeflow benefits from the strong backing of the Kubernetes community, ensuring continuous development, support, and integration with various cloud services and tools.

5. Scalability: Gradio is well-suited for small to medium-scale machine learning projects that require quick prototyping and deployment. Kubeflow, on the other hand, is designed to handle large-scale production workloads with built-in capabilities for managing distributed training, serving models at scale, and monitoring performance metrics in real-time.

6. Integration with Kubernetes: While Gradio can be deployed on Kubernetes clusters, its primary focus is on simplifying the machine learning deployment process without requiring extensive Kubernetes expertise. In contrast, Kubeflow is tightly integrated with Kubernetes, leveraging its robust orchestration capabilities for managing complex ML workflows efficiently.

In Summary, Gradio and Kubeflow cater to different segments of the machine learning ecosystem, with Gradio offering easy deployment for demos and prototypes, while Kubeflow provides a comprehensive platform for scalable production-ready machine learning workflows.