Hi CynicalBee90
Always great to have people joining the conversation, especially if they are the decision makers a.k.a can amend mistakes 🙂
If I can summarize a few points here (and feel free to fill in / edit any mistake or leftovers)
Open-Source license: This is basically the mongodb license, which is as open as possible with the ability to, at the end, offer some protection against Amazon giants stealing APIs (like they did for both mongodb and elastic search) Platform & language agnostic: ClearML is definitely python oriented, that said it is totally platform agnostic, Linux, Windows, and MacOS are supported. For that matter I'm pretty sure TB/MlFlow etc. are the same. (or I might have misunderstood the definition of "platform agnostic") Experiment Data storage: local storage (i.e. shared folders), on-prem object storage (e.g. minio / ceph) and cloud object storage (s3 / gs/ azure), are all supported Easy-to-setup: Considering the fact that docker-compose is the de facto standard to spin a server (with both Windows and MacOS supported, on to top of the standard linux distros) and that we also provide a pre-built AMI and GCP images, I'd say that it is very easy to install (for comparison, just try to setup Kubeflow in a usable way, now this is challenging) Scalable for large number of users: The plain vanilla install scales to a few millions, and the cloud ready helm chart is unlimited (basically clusters of all the databases). We have experienced with millions of experiments, hundreds of running agents and a few dozen UI users, all running smoothly.
wdyt? could we fix the comparison chart?