Some "pros" that many find appealing:

* Copying the database around its a file copy in sqlite. Each database is it's own single file. (there's also WAL stuff that you get control of)

* No extra service to deploy, manage, and/or optimize. I don't fully agree with the following, but I had a colleague who used to say "If you don't have multiple app servers writing to the db, postgres is a waste of effort".

* embedded means way lower data latency - if the dataset is in the fs cache even lower, no waiting on network transactions.

I've frequently chosen it over PG in cases where I needed basic relational data operations. In one case we ingested a large dataset (a few gbs of measurements) once an hour. Then we did some initial analytics on those measurements and threw the results in the same db file. After that step was done, the data was read only for several other systems and we just copied the db file to each of the systems that needed the data on-demand. A couple of the systems did additional analytics and effectively imported the db file to a different db (one was PG another was a graph db - neo4j). A couple of the systems just used the db file directly. It worked our really well.

less things to go wrong. This gives you benefits all the way up the dev stack. Changing an integration test from needing its own db server installed to just a couple of files on disk is a big difference in complexity. You can probably run that test with just a local disk almost infinitely entirely deterministically, conversely as soon as you go onto the network all bets are off.

Granted, if you already have the tooling its less of a big deal but if you don't then you need the tooling, e.g. your build and test machines need to have access to some sql installation somewhere and that the process of maintaining that can be a flaky one.

If you consider cost... I would imagine a fair few. From the article:

> We will ensure that D1 costs less and performs better than comparable centralized solutions.