Metaflow was built to assist in both developing ML models and deploying/managing them in production. AFAIK, TFX is focused on the deployment story of ML pipelines.
It's focused on building ML pipelines (similar to what Cortex aims to be).
In addition, it also conveniently supports integration with Orchestrators like Airflow, Kubeflow, Beam etc. This book "Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow" (https://www.amazon.com/dp/1492053198/ref=cm_sw_em_r_mt_dp_Ig...) goes into great details.
I was curious to see what advantage Metaflow offered over TFX.
https://docs.metaflow.org/introduction/what-is-metaflow#shou...