Hi @Debayan Mukherjee​  Thanks for answer. Yes, I am familiar with the classical approach. I'm more interested if there is any work around. For two model Im able to transfer one model to production stage and second model to staging. Both of them have their own containers and have their own endpoints.  it does not matter if one is designed in tensorflow and second one in pytorch. But I would like to find way how to deploy more models on one cluster. I know it goes against mlflow concept but the aim is to save costs.