You need to upload your time series (loss, performance metrics) to the cloud. Not your weights or models.

Reliance on cloud services is a legitimate worry though for privacy, IP, process control, reliability, etc.

The comparison between Optuna and WandB was not apples to apples. Optuna is completely self-hosted and local. It also focuses on hyperopt narrowly with flexible design unlike WandB that now assumes to be capture a large part of cloud-based MLOPS workflow.

It would be more fair to compare Optuna to Hyperopt. And I think Optuna was the better choice there, but I did simple PoCing and have no strong opinions.

What is the "it" you're referring to? You don't need to upload your model or the weights. You do need to upload the hyperparameters you're optimizing and your target values, but those seem unlikely to be sensitive. (Although I'm sure there are still some legitimate reasons why someone might not want to do so.)