Seems like something new to learn, an added layer on top of existing workflows, with no obvious benefit.
It may look redundant on the surface, but those cloud services are infrastructure primitives (compute, storage, orchestration). Metaflow sits one layer higher, giving you a data/model centric API that orchestrates and versions the entire workflow (code, data, environment, and lineage) while delegating the low‑level plumbing to whatever cloud provider you choose. That higher‑level abstraction is what lets the same Python flow run untouched on a laptop today and a K8s GPU cluster tomorrow.
> Adds an extra layer to learn
I would argue that it removes layers: you write plain Python functions, tag them as steps, and Metaflow handles scheduling, data movement, retry logic, versioning, and caching. You no longer glue together five different SDKs (batch + orchestration + storage + secrets + lineage).
> lacks concrete examples for implementation flows
there are examples in the tutorials: https://docs.outerbounds.com/intro-tutorial-season-3-overvie...
> with no obvious benefit
There are benefits, but perhaps they're not immediately obvious:
1) Separation of what vs. how: declare the workflow once; toggle @resources(cpu=4,gpu=1) to move from dev to a GPU cluster—no YAML rewrites.
2) Reproducibility & lineage: every run immutably stores code, data hashes, and parameters so you can reproduce any past model or report with flow resume --run-id.
3) Built‑in data artifacts: pass or version GB‑scale objects between steps without manually wiring S3 paths or serialization logic.
They call it a DREAM stack (Daft, Ray Engine or Ray and Poetry, Argo and Metaflow)