Perhaps there is a problem where people are splitting a perfectly good monolith into microservices, but I do wonder how do you deal with large scale machine learning without microservices? I am a rather small operation and I still have models taking several gigabytes worth of memory and ANN indexes of about the same size, which clearly couldn't operate in a monolith unless it was a massive machine, and even if it could not every request would necessitate such power. How does a dogmatic monolith approach solve these problems?