If you have a very beefy desktop machine and no giant datasets, there isn't a strong reason to use Polars Cloud.
Are you a data scientist running a Polars data pipeline against a subsampled dataset in a notebook on your laptop? With just changing a couple lines of code you can run that same pipeline against your full dataset on a beefy cloud machine which is automatically spun up and spun down for you. If you have so much data that one machine doesn't cut it, you can start running distributed.
In a nutshell, the pitch is very similar to Dask/Ray/Spark, except that it's Polars. A lot of our users say that they came for the speed but stayed for the API, and with Polars Cloud they can use our API and semantics on the cloud. No need to translate it to Dask/Ray/Spark.