We have production monitors on every host that show basic metrics like memory, disk, and CPU utilization. Atop that, we added a tracker for the number of suspended Haskell threads. (that is, threads which are not blocked on I/O, but are also not running)
We found that the machines are usually able to handle requests as soon as they come in, so if the number of Haskell threads goes above 0 for any length of time, the machine is about an hour away from melting down.
We can restart the process without losing any connections, so this leaves us a very comfortable margin of error.
Once we know we have a problem, it's usually pretty simple to run the heap profiler on the process and look at recent commits. We continuously deploy, so there's only about a 10 minute delay before a particular commit is running in front of customers. This makes tracking regressions down really fast.
Even in cases where we can't figure out why a bit of code is leaking, we can almost always identify it and revert it until we understand what's going on.