And it _really_ is a lot. A company I work with switched from running two servers with failover to AWS, bills went from ~€120/m to ~€2.2k/m for a similar work load. Granted, nobody has to manage those servers any more, but if that price tag continues to rise that way, it's going to be much cheaper to have somebody dedicated to manage those servers vs use AWS.
Also, maybe that's just me, but I prefer to have the knowledge in my team. If everything runs on AWS, I'm at the mercy of Amazon.
Not even getting into the fact that an m5.xlarge has roughly as much muscle as an old laptop.
Get normal disks of a larger size, it comes with 3 IOPS per GB.
"We'd rather not manage the database ourselves" was the answer from the lead dev, and I do understand that: it's not the developers' job to manage servers. In this company, management has removed themselves from anything technical and the devs don't like managing servers, so they say "let's use AWS, we'll write some code once to create our environment and Amazon takes care of the rest" - and by this point they are committed, with months having been spent on getting things running on AWS, changing the code where necessary etc, so reversing it would be a hard thing to sell to investors and the team.
Yes, some places will need a full time team of multiple people, but a lot of places don't, and can get a tailored solution to suit their needs perfectly, rather than just trying to fit into however it seems to work best using the soup of rube-goldberg like interdependent AWS services.
However, often teams will use or attempt to use _a_lot_ more resources than what's needed, or they simply don't optimise for cost when using AWS.
My anecdote is that a company I worked for had an in-house app that run jobs on 8 m4.16xlarge instances costing around $20k/m and they were complaining it took hours to run said jobs. The actual tasks those servers were running were easily parallelizable and servers would run at capacity only for around 10% of the time as those jobs were submitted by users every few-up-to-24 hours. The app was basically a lift-and-shift from on-prem to AWS. The worst way one can use cloud. I created a demo for them where the exact jobs they were running on those m4.16xlarge would run on lambda using their existing code modified slightly. The time a job took to run went from many hours to few minutes with around 2k lambda functions running at the same time. The projected cost went from $20k/m to $1-5k/m depending on workload. I was quite happy with the result, unfortunately they ended up not using lambda and migrating off the app that in its entirety cost around $50k/m for the infrastructure. The point I'm trying to make here is that properly used cloud can save you a lot of money if you have a spikey workload.
Also, for internal apps that are used sparingly one can't beat a mix of client side JS code served from S3, using AWS Cognito tied to AD federation for auth with DynamoDB for data storage and lambda for typical server-side stuff. Such apps are easy to write, cost almost nothing if they are not used a lot and don't come with another server to manage. The only downside is that instead of managing servers, now you have to manage changes in AWS's api..., but nothing's perfect.