Discussions about Anyscale

Mixed workloads on [Anyscale](https://saaskart.co/software/anyscale) need a smart node mix. How are you splitting GPU and CPU node pools to avoid paying for idl

[Anyscale](https://saaskart.co/software/anyscale) supports Ray Serve for online inference. How does it compare to dedicated inference platforms for latency and

If you already run Ray locally, how much work is it to move to [Anyscale](https://saaskart.co/software/anyscale)? Any config or dependency surprises?

Some teams run ML on Kubernetes directly. How does [Anyscale](https://saaskart.co/software/anyscale) compare for developer experience and scaling versus a K8s-b

When a Ray job fails across dozens of nodes, root-causing is painful. What tools and dashboards in [Anyscale](https://saaskart.co/software/anyscale) actually he

Distributed compute bills add up fast. What quotas, autoscaling limits, and monitoring do you set on [Anyscale](https://saaskart.co/software/anyscale) to keep s

Data residency often requires compute in your own VPC. How smooth is the [Anyscale](https://saaskart.co/software/anyscale) setup for running clusters inside you

Fine-tuning large models across many GPUs is hard to orchestrate. How has [Anyscale](https://saaskart.co/software/anyscale) with Ray Train worked for your multi

Batch scoring millions of records is a classic Ray use case. How are people structuring Ray Data pipelines on [Anyscale](https://saaskart.co/software/anyscale)

Open-source Ray is free, so when is [Anyscale](https://saaskart.co/software/anyscale) worth paying for? For teams that upgraded, what enterprise features tipped