Well, I have to say thank you very, very much. Thanks to your advice we 
have our lowest latency in 3 years! Sub 300ms average.  As expected though, 
we are now sitting on 21 billed f4 instances, which will potentially cost 
us in the order of 3x our current ($30-40 -> $100+), but we will tweak that 
from tomorrow onwards. Peak hour is about to hit so we are going to see if 
the system can keep sub-300ms at the current "automatic" setting for 
scaling. But yes, once again, thank you for solving in 5 minutes what I 
have been working on doing for 2 weeks (my tears are from joy and sadness 
all at once)

<https://lh3.googleusercontent.com/-eEUuw3hSLYU/V3Tox-bhe6I/AAAAAAAAQWM/zPzgBRJkRHcoBSPmVrP2xsmN2FDK6Yl_wCLcB/s1600/Screen%2BShot%2B2016-06-30%2Bat%2B18.37.20.png>

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On Thursday, June 30, 2016 at 6:03:23 PM UTC+9, troberti wrote:
>
> Right, you should definitely test and see what the results are. My first 
> inclination was also to increase max_concurrent_requests, but because then 
> all those requests have increased latency, the actual QPS per instance 
> decreased! Lowering max_concurrent_requests decreased request latency, so 
> each instance could process more requests/second.
>
> We use F1 instances, because we do not need the additional memory, and our 
> requests perform mostly RPCs. In our testing, faster instance classes do 
> process requests faster, but also cost significantly more.  F1s provide the 
> best performance/cost ratio for us. This could be a Python thing, not sure. 
> Again, you should really test and figure out what is the best for your 
> application+runtime.
>

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