Hi Utkarsh. We have few models (lets say M1, M2, M3, corresponding to flows F1, F2, F3) that does different types of predictions on the same data and has to share the input & intermediate data states between them. (eg. M2 uses user input and M1's output if available). And each of these can be triggered(called) separately as well by the client. (M2 can be called without M1 and M3). We have 20+ models used to process the same input and some are very lightweight models and doesn't need its own dedicated pods, and hence trying to see how we can optimize on pods vs communication overhead between each flow for the same input. Hope this makes sense.