In the field of Automatic Speech Recognition (ASR), custom models are rarely more accurate than general models due to their extensive training on diverse datasets. General models can handle most audio data, and custom models may only be necessary for unique characteristics like children's speech. Adding custom vocabulary to a general model is often sufficient for improving accuracy in specific use cases. Maintaining custom models is expensive and time-consuming compared to updating general models with the latest research. Companies should consider their unique data needs, budget, and ability to maintain models before investing in custom ASR solutions.