Kubeflow and Metaflow are two prominent machine learning operations (MLOps) platforms designed to streamline the deployment and management of machine learning models, with Kubeflow leveraging Kubernetes for scalability and Metaflow offering a Python-based approach focused on orchestrated pipelines. Kubeflow, developed by Google, provides an end-to-end machine learning stack that facilitates the orchestration of complex workflows on Kubernetes, making it suitable for large-scale systems requiring extensive cloud deployment capabilities. In contrast, Metaflow, created by Netflix, is a Python library aimed at enhancing the productivity of data science teams by simplifying the management of data science projects, specifically focusing on the orchestration of production pipelines. While both platforms share similarities such as being open-source and supporting Python, they differ in scope and approach, with Kubeflow offering broader functionalities for the entire ML development process and Metaflow being more specialized in handling production pipelines. Teams may choose between these platforms based on their existing tools and specific needs, with Kubeflow being ideal for those seeking an integrated workspace for model experimentation and deployment, and Metaflow suiting those focused on production pipeline building. Additionally, JFrog ML is presented as an alternative MLOps platform providing a managed service environment similar to Kubeflow, designed to reduce setup and maintenance burdens while offering scalable and customizable infrastructure.