DSSTNE had a clearish reason for doing so: automatic model-parallel training. Twitter wanted something simpler than Torch for most of their devs to use.
But I'm not really seeing why you guys did it.
"DSSTNE currently only supports Fully Connected layers ..."
Kur supports the cutting edge: like CNN/RNN.
"DSSTNE Engine works with data only in NetCDF format."
Kur supports the data that you have on hand. You can see in the tutorial (http://kur.deepgram.com/tutorial.html) how easy it is to send brand new data in the familiar Python pickle object.
These are the kind of GOTCHAs that people doing deep learning run into all the time. And they are a major time suck. Kur relieves you of those duties so you can work on more interesting parts, like trying novel models :).
We're so glad DL tools are coming out. But there's still tons of progress to be made and Kur is one step along that path—making the user experience more efficient and enjoyable.
So the real reason is for internal efficiency for model prototyping, or more direct: results per human-hour. If we have to spend time in troubleshooting land, then we're losing our Startup Competitive Advantage™ in DL (the ability to move fast).
That seems sane to me.