Okay so the last piece was just when does it cut off in pure times perspective?
Shows the various daily peaks and having done a bit more detailed testing the longest has been 9.27 seconds with several going above the 9 seconds mark. These are all massively over the CPU limit but it appears that the only real element that gets culled is the raw time. Doing some more work around the database code at the moment and it appears that long queries there are also a pretty big issue, especially when using an iterator. The best bet is to do a fetch on the results and then use those results to form the next query rather than moving along the offsets, in other words if you are ordering by date (newest to oldest) then do a fetch(20) and take the time of the last element in the results and on the next query say "date>last.date". Fetch is certainly your friend in these scenarios.
So what does this mean? Well Google aren't culling at the CPU limit straight away but are consistent around the time limit, the performance doesn't have peaks and troughs through the day and there doesn't seem to be any swapping out of CPU intensive tasks. All in all its a solid base.
Finally however I just had to lob on something from Google Spreadsheets that brings how the sort of thing that people can do when they have access to real time data and decent programming frameworks.
This just shows the progression of the "default" render over time, if you go by "average" then it will show you the stability that occurs and if you go by count then it shows the amount of calculations that all of these stats have been gleaned form and will help you think whether its a decent enough set of data to draw conclusions from.