>If using Python instead of what we use, our cloud costs would be more than double.
You won't double your costs and I can safely say that without knowing anything about your service. Because if you use PyPy, the speed up is generally 2x. So right there, your argument is defeated.
But even without PyPy, I am willing to bet that your service has a dominant component of either network calls, or data processing. In the former case, you can use Python with things like uvloop and multiprocessing to basically structure your service so that you are filling the processing time with waiting for network calls, spending minimal time in actual compute, which means you don't get any slower. In the latter case, its pretty straightforward to write a data cruncher in a more optimized language like C and run it from Python - even if you don't wanna deal with native interfaces, an LLM can write you a small udp server that can listen on a unix pipe for messages to process, and from python you just launch it with subprocess and send data to it.
So unless you run a highly specialized service with a very niche use case, you would be absolutely fine with Python.
And even then, Im willing to bet based on average use case that your cloud costs aren't even close to optimal. In the case of AWS, so many companies use managed services like Dynamo DB when they could easily run a reserved small EC2 instance and run their own version of Mongo or something similar. At my current company, the only AWS services we use is S3 and EC2, where things like database software or caching software is all manually installed and ran on EC2. If you know what you are doing, its not really that hard to manage. We even have or own version of AWS Lambda.