Your use case allows you to perform batching for writes at the *application layer*, while discord's use case doesn't.
Then it is just a case of structuring your application properly.
Most applications are immediately broken, by design, by having a thread dedicated to the request/response pair. It then becomes difficult to have parts of that processing from different threads be selected and processed together to take benefit of amortizing costs.
The alternative I am using is funneling all requests into a single pipeline and having that pipeline split into stages distributed over CPU cores. So it comes in (by way of Kafka or REST call, etc.), it is queued, it goes to CPU core #1, gets some processing there, then moves to CPU core #2, gets some other processing there, gets published to CPU core #3 and so on.
Now, each of these components can work on huge number of tasks at the same time. For example when the step is to enrich the data, it might be necessary to shoot a message to another REST service and wait for response. During that time the component picks up other items to do the same.
As you see, this architecture practically begs to use batching and amortize costs.
After reading all of you responses, I still don't see how you think your learnings apply to Discord. They would not be able to fit the indexes in memory on MongoDB. They can't batch reads or writes at the application server level (the latency cost for messaging is not acceptable). Millions of queries happen every second, not one-off analytical workloads. It seems these two systems are far enough apart that really there is no meaningful comparison to be made here.