[cheptsov]

I guess it depends on the use case. For example with dstack, we focus on AI.

Our abstractions include:

1. Dev environments - you need them often and need an easy way to get one with tight GOU resources - either using already provisioned resources or provision on-demand

2. Tasks. For example, in AI you may want to run distributed tasks over a cluster using your favorite framework like pytroch

3. Services - very close to Docker Compose. And you can use it with dstack. But for you may want to also manage GPU requirements; and of course auto-scaling

4. Managing clusters. As an AI user you may want to provision them on-demand. This is what we call fleets with dstack.

5. Ingress for public endpoints. Dstack also handles authorization and OpenAI endpoint mapping - as it’s important for AI.

6. Finally you need to manage tenancies - isolate resources across projects or teams. With dstack, we call it projects.