samples for different objects with unique labels Lets say i have a samples taken from veterinary hospital , one of my feature will be the type of  the animal and some other features such as fever , size,symptom etc.. , my labels are the medicine given to that animal. 

if each medicine is unique to that type of animal (Medicine A should be given to animal A , and there are no Animal B that taken Medicine A) . What will be the cons and pros to building one classifier for the whole data set vs split classifier for each animal (since there wont be valid generalization between animals)
Have run into this problem, when we wanted to run with patient-based deep learning models or individual observations (the same patient could have come several times) based deep learning models.

In your case, they could be analogous to Animal-based model vs general-one-for-all model which has all the animals. 

Pros of animal-based:


If you are going to build an animal-level classifier, it certainly is going to generalize better when compared to one-model for every animal.


Cons of animal-based:


If you don't have enough data for one particular animal, you can't help out much, for that case. This is slightly advantageous as well, I can't say for veterinarian examples, but if you are going to use a general-one-for-all model it might misclassify the prediction as well (because of lack of data for that particular example).


I would suggest you try both. There is nothing certain in this world. Only hunches and guesses from past experiences.

Hope this helps.