Maximum memory per instance (8GB) is an extreme limit. Disk and CPU limits per container instance are also quite bad. And, laughably, for any workload just a b

Maximum memory per instance (8GB) is an extreme limit. Disk and CPU limits per container instance are also quite bad. And, laughably, for any workload just a bit out of reach for Cloud Run, GCP docs immediately recommend switching to GKE (and even Anthos). Imagine having a high RAM workload that is just a simple RPC service. Many (probably most) machine learning services fit this model. Many routine ML models require more than 8GB RAM just to load the model, bit it’s a good use case for serverless non-lambda infra because it runs out of a very unique Docker image and does nothing but serve stateless model predictions. Needing to bring in all the machinery of GKE or pay out the nose for Anthos just because you need exactly the same operational model as Cloud Run just with high RAM or CPU is a really poor customer experience that feels deliberately set up to push you towards more expensive Kubernetes products.