If you have a predictable workload (i.e. we ingest 100,000 videos a month with n size) you should be looking at it from that perspective -- how much compute you are going to need, and when. Serverless works well for predictably unpredictable, infrequent workloads that don't need to have perfect output ("good enough").

The big mistake I see people make is trying to be overly clever and predict the future workload needs. It never works out like that. Design for your current workload now, if your architecture can handle 10x of that great! Each time your scale from 10x workload you will likely need to redesign the system anyway, so you shouldn't pay that tax unless you absolutely have to.

There are a lot of limitations of serverless, the big one I experienced was inability to control the host environment and limitations on the slice of memory/CPU you get, such that you must take that into consideration when designing your atomic work units. Also paying the distributed computing tax is real and occurs at development and runtime -- things like job tracking and monitoring are important when you have 10,000 processes running -- you start to get into the territory of things that basically never happen on a single machine or small cluster, but become problems with thousands of disparate machines / processes.