Not quite sure if I understand the question correctly. In terms of calculation time, batches are much faster that feeding the training data one by one. Technically, you want to maximize the batch size for the best performance, but memory size will stop you at some point.
The larger the batch size, the more you average out the different gradients if they are (very) different for the items within one batch. So in general, You can often use higher learning rates for larger batches. You might also have a look at the paper “Don’t Decay the Learning Rate, Increase the Batch Size” 1.
Many Seq2Seq examples are batches of size 1 since the decider predicts the next word step by step and the number of steps might be different for the input sequences in a batch, even if the input sequences themselves have the same lengths. So yeah, in such encoder-decoder setups, feeding the data one by one might be the only/best/straightforward solution. But for a classification task there’s in my opinion no reason not to go for batches.