Raza here (one of the other co-founders). Good question! I think our visions are quite different even if our starting points look similar.
Scale has always positioned themselves as an API to human labour and their goal is to abstract the labelling task away from the end user as much as possible. So scale works really well when you can easily outsource your annotation task.
Our ultimate goal is to try and give domain experts the ability to teach ML models themselves. We're much more focussed on NLP and on tasks that require domain expertise and are hard to outsource. For people where deep domain expertise matters or their are privacy concerns, Scale isn't really an option and we're building tools for them.
On another point, Scale makes its money by charging per annotation so we think they aren't as incentivised to reduce how much you need to label.
