Hey AgitatedDove14 , thanks for the detailed response. I wanted to make time to respond to your points appropriately:

1. Apologies, I made a mistake, and the license name is SSPL, not SPSS (which is a tool for statistics by IBM). In any case, SSPL is a new license, which is considered problematic by many. There are a lot of examples of people complaining about the change in Elastic’s licensing, considering it moving from Open Source to Source-Available (in some people’s minds this is closed source, but I don’t have a horse in that race). The definition of SSPL ( https://en.wikipedia.org/wiki/Server_Side_Public_License ) is Source-Available, but to be fair I think we’ll leave the checkmark and the warning and just write SSPL below , so people can make their own judgement. If no-one cares about the license, then that info won’t deter anyone.

2. So since we write Platform and language agnostic, we agree? I explained above regarding language agnosticism, so hopefully that settles it.

3. A DB might be as good as JSON, or might not be. The criterion (or job to be done) is I want to crack open a notebook with pandas to do smart analysis on my logged data easily, without resorting to reading API docs or using custom libraries. In DVC, that requires I parse yaml files and csv s. That’s easy. With MLflow, It’s a bit harder since each param and metric is save to a separate file with an intuitive format to understand at a glance. Admittedly, I couldn’t find any information on doing something equivalent with ClearML in your docs, so if you can point me to where it explains these things, we can evaluate whether it meets the criterion. Otherwise, I feel that the fact that not being able to find this is a good indicator that ClearML doesn’t meet this criterion.

4. ClearML SaaS is not open source, so that would also not be an apples-to-apples comparison. You raise a good point which is team support out of the box – It might make sense to add it as a criteria as well, since I think this is an important consideration for, well, teams. To be sure (I couldn’t find it in your documentation) – does the Open Source ClearML come with RBAC? In any case it seems that we agree that TB and MLflow are easier to setup in the absolute sense, but might offer less capabilities compared to ClearML/DAGsHub.

5. I’m attaching a screen capture of your column. Not sure where you see question marks for CLearML?