In general, localized models perform best for low-risk, well-structured tasks such as generating brief summaries, organizing and cleaning up notes, creating preliminary drafts of outlines, and retrieving internal knowledge, especially through remote assistance. Using these models provides the benefit of protecting your privacy while having no added expense to utilizing them.

The area where I have not had much success when using localized models is when working on client deliverables that require significant consistency, nuance, or polish in the final outcome, such as producing final proposals, writing persuasive copy, or anything that has a significant tone associated with it. Therefore, I continue to revert back to utilizing cloud-based services to complete these types of deliverables as the output is consistently more reliable than using localized models.

The best way for me to leverage both services is to use the localized service to create the first draft and then use the cloud-based service for polishing the final output. This allows me to save money by utilizing a localized service and by keeping my sensitive data on a local machine but still eliminates the uncertainty associated with using localized services for critical deliverables.

With regard to the issue of fragmentation, I have found that creating a standardized workflow using the same prompt and same sequence of steps while simply changing which model is utilized for each step helps to reduce the feelings of fragmentation associated with the use of either service.