It would be really really amazing if it allowed a large number of CPU cores. Like 64 cores. That would make it interesting for ML inference workloads.
I understand this is kind of a hard CS problem and is basically rooted in needing to move a lot of data around very quickly, all while making the software low latency as well.
But solving these is kind of the point of a FaaS.
Otherwise we could just run containers on VMs and autoscale ourselves. With terrible cost and cold start times.
Maybe look at how Jelastic does it for a unique take on this. https://jelastic.com/public-cloud-pricing/
This is probably the model that would make users the happiest.
Having poked a bit at how Jelastic does it, it seems they drop a pool of users on a 32-core machine and load balance the users containers with live migration to less utilized machines.
Imagine GCP doing something similar. Drop a big pool of users onto 96 vCPU instances. If an instance starts to get overutilized, live migrate some user containers to a new instance.
Same type of thinking that is probably behind AWS Lambda: how do you satisfy bursty workloads for a lot of users cost effectively? Pool the users. Assume they won't all burst at once.
That has got to be one of the biggest benefits to large public cloud computing.