The cost of Databricks is really related to the size of the clusters you are running (1 worker, 1 driver or 1 driver 32 workers?), the spec of the machines in the cluster (low RAM and CPU or high RAM and CPU), and how long you leave them running (always running or short time to live, aka "Terminate after x minutes of inactivity". I am also assuming you are not running the always on High Concurrency cluster mode.
Some general recommendations would be:
- work with smaller datasets in dev, eg representative samples which would enable you to...
- work with smaller clusters in dev, eg instead of working with large 32 node clusters, work with 2 node small clusters
- set time to live as short eg 15 mins
- which together would reduce your cost
Obviously there is a trade-off in assembling representative samples and making sure your outputs are still accurate and useful but that's up to you.