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The resource limits are so severely restricted on Cloud Run that I don’t think it’s fair to compare it to Fargate. The space of use cases solved well with Farga
by mlthoughts2018 6y ago
The resource limits are so severely restricted on Cloud Run that I don’t think it’s fair to compare it to Fargate. The space of use cases solved well with Fargate is far, far larger.
- runeks 6y ago> The resource limits are so severely restricted on Cloud Run [...] Honest question: which of the limits in https://cloud.google.com/run/quotas#cloud_run_limits https://cloud.google.com/run/quotas#cloud_run_limits do you find “severely restricted”?
- mlthoughts2018 6y agoMaximum 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.