Microscopic cost optimization is frequently not a problem, but it's great if you can get it for low effort with Go. It's highly contextual whether it'll matter that much in both the short and long-term. You're situation as a solo dev is special and can't be easily extrapolated to efforts involving many people. The things that make solo dev great are hinderances to collaborative work efforts (by design in many cases.)

As a frame of reference, ASP.NET services in C# can exceed the throughput and performance of an equivalent Django app, but Django (and Python) may be the best choice for some people. What's not considered here is the experience of the developer and whether they can avail of the platform capabilities fast. Do they need to rewrite libraries? How much work is needed to be as proficient as you are with Go? The language and platform choice is rarely a differentiator here.

Over the long-term you can see some patterns. Python companies are able to scale up their teams more quickly and cheaply in some markets than Go ones. You choose to optimize hosting cost while they optimize other costs. They may need to avail of libs like Pandas and these are readily available. I don't know what the situation is with Go today, but similar libs weren't available originally and the Go community didn't have a big foothold in data science.

I gave Go a try recently and it was a quite pleasant experience. I don't see why I wouldn't use it for greenfield work over Python.