We are still using both. We haven't done everything I wanted (we're still less than 1yo, so we do not have a lot of capacity, and there is a trade-off between improving our process, and get the many low hanging fruits around us to improve the application). We are checking in CI that our DVC pipelines are up-to-date, but pants play no role in this yet.
With regards to our usage of pants, one thing we improved since my last comment (but is not directly related to DVC) is to use pants to build and publish our docker images. We deploy most of our models/services as docker images.
In previous companies, building docker images which depend on multiple internal packages in the repo, often entailed pushing/pulling the packages to a private pypi or manual handling of the build context (like temporary copying files). Which was a hassle. Pants detecting the dependencies, and including everything you need, is a game changer for me. This means that even our data scientists which don't understand docker are able to add/maintain docker images.
Plus, the dependencies thing made it easy to have a github action doing "List the docker images with the tag 'auto-deploy' whose transitive dependencies (code, data, version of 3rdparty packages) have changed since origin/main; build the images and push them to our docker registry; update the yaml file of the corresponding service in the staging environment so that it now uses that image". We no longer have issues with staging being out-of-date with origin/main.
When thinking about which tools I want to add to our stack, I used to ask "what will integrate well with DVC?". Now, I ask "what will integrate well with Pants?". I still use and like DVC. But I could see myself switching to another tool. But I see Pants as the heart of our repo, and I have a hard time thinking of a scenario where I'd switch to another build tool. (I promise I am not paid to advertise it)