If you use a pre-trained model, you are more or less bound to the architecture, which is fine in most use cases.
Sometimes you would like to define a specific model architecture and cannot use pre-trained weights, since they are not matching.
It really depends on the use case. Often you can replace a part of your overall model with a pre-trained model, which might help the training as @Nicolo_Savioli explained.
On the other hand, there are not many pre-trained models working with e.g. accelerometer data for activity recognition.