Data Imbalance: what would be an ideal number(ratio) of newly added class's data? Assume that I have 10 classes with 100 samples for each class—same # of samples, perfect balanced dataset.
I want to add 3 new classes, and which of the following is the best option for the number of samples for each newly added class?

*

*100, 100, 100

*200, 200, 200

*1000, 1000, 1000

*100, 1000, 1000

I am creating a data analysis product for my clients, and I have to make a threshold for the minimum (or maximum) number of samples they have to add.
It depends on the datasets we are using, but balanced data is almost always better than imbalanced.
However, I am pretty not sure how I can set the threshold for the newly added class' data sample number if I can choose it.

You have a trade-off between wanting the data to be balanced and preferring more data. As you said, it always depends on the data. Moreover, some metrics are more robust towards imbalance than others. Without any further information, I would choose option 1 or 2.
You could also try to augment your dataset by oversampling, or use metrics that are more robust towards imbalance.
