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Disclaimer: I work for Polars Inc, but my opinions are my own.

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.



they came for the speed but stayed for the API

This is exactly how I would describe my experience. When I talk to others about polars now I usually quickly mention its fast up front, but then mostly talk about the API, its composability, small surface area, etc. are really what make it great to work with. Having these same semantics backed by eager execution, query optimized lazy API, streaming engine, GPU engine, and now distributed auto-magical ephemeral boxes in the sky engine just make it that much better of a tool.


Being both eager and lazy does make it sound magical.


I think being able to run the same code locally and on the "cloud" is a great selling point. Developing on Spark feels hillariously ineffective.




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