I disagree with this, I have used spark standalone/single node both locally and in the cloud for numerous use cases because you can easily cook up spark sql or pyspark cleaning and ETL scripts for small data (a couple TB) and reach high throughput (>260mb/s). I am also someone who reaches for dask (and prefect) first for small data but in cases where the data is medium sized (100gb-10tb) I always reach for spark first, its simply easier to write fast code with spark (especially spark sql) without needing to think hard about optimizations. I also have not run into any operational issues running a spark standalone instance for a few hours or days to process small data I would use dask for.
I reach for dask first for most small data (<100gb) because its better integrated with python libraries like prefect that improve the dev experience.