This is not clear at all - 55k monthly users means nothing, what's the average number of requests per second? 99th percentile? What's the same stats for CPU and memory usage? 3.4k users per day is actually not that large a number, even assuming each user is going to bombard your server with thousands of requests in 20 minutes, it's well within what optimised flask with wsgi can handle with a very mediocre compute instance.
Further,Are you absolutely sure that you have optimised your configuration and code for performance without changing the stack? (Serving static components directly via ngninx, caching responses using flask, caching other resources using memcached, optimizing gunicorn worker counts for the instance type, profiling which endpoints take the most amount of time and trying to optimize them, see if the bottleneck is webserver or the db, considering pypy, and probably 50 other ideas).
Are you also absolutely sure that bluegreen deployments are what you want to use given your cost constraint? Further, if you have bluegreen it should be straightforward to downgrade your instances to smaller ones fairly easily,why not do that first before discussing costs?
We run production webservers on t2.small ec2 instances in elastic beanstalk and (with some assumptions) handle comparable loads during work hours. We have two redundant instances and can easily scale up or down even on schedule with zero extra code. There's not even a need to minimize costs here because our application costs 100x more on data lake costs than the webservers, but it really helps keep the engineers grounded and ensuring they don't write outrageously inefficient code that's covered up by excessive server sizes.
Further,Are you absolutely sure that you have optimised your configuration and code for performance without changing the stack? (Serving static components directly via ngninx, caching responses using flask, caching other resources using memcached, optimizing gunicorn worker counts for the instance type, profiling which endpoints take the most amount of time and trying to optimize them, see if the bottleneck is webserver or the db, considering pypy, and probably 50 other ideas).
Are you also absolutely sure that bluegreen deployments are what you want to use given your cost constraint? Further, if you have bluegreen it should be straightforward to downgrade your instances to smaller ones fairly easily,why not do that first before discussing costs?
We run production webservers on t2.small ec2 instances in elastic beanstalk and (with some assumptions) handle comparable loads during work hours. We have two redundant instances and can easily scale up or down even on schedule with zero extra code. There's not even a need to minimize costs here because our application costs 100x more on data lake costs than the webservers, but it really helps keep the engineers grounded and ensuring they don't write outrageously inefficient code that's covered up by excessive server sizes.