dstack

license

MPL-2.0(OSI)

source

public(dstackai/dstack is the control plane you run)

core features withheld

no — a 2,640-entry recursive tree carries a single LICENSE.md and no path matching enterprise, ee, commercial or proprietary

dstack is licensed under the Mozilla Public License 2.0, read from the LICENSE.md body; MPL-2.0 sits beside Apache and MIT as an OSI license. The repository tree carries that one license file and no enterprise, ee or commercial path, and dstack's deployment guide lists the self-hosted control plane and its commercial offerings as separate products.

• https://api.github.com/repos/dstackai/dstack/git/trees/master?recursive=1 recorded 2026-09-15

  Full untruncated recursive tree of the default branch, 2,640 entries. One LICENSE.md. No path matches enterprise, ee, commercial or proprietary. A tree lists paths; it is cited for that and for nothing about the vendor's offerings.

• https://dstack.ai/docs/guides/server-deployment/ recorded 2026-09-16

  dstack's server-deployment guide, naming the products separately: 'Self-hosted dstack - the open-source control plane for AI-native orchestration', beside 'dstack Factory' and the hosted 'dstack Sky'. Establishes commercial offerings beside the open control plane rather than inside it.

• https://github.com/dstackai/dstack recorded 2026-09-15

  Repository page for dstackai/dstack: MPL-2.0 license, public and unarchived, described as 'Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent'. Establishes the license and that the source is public.

Adoption is measured by PyPI downloads of the dstack package, its declared distribution channel.

• https://pypistats.org/api/packages/dstack/recent recorded 2026-09-15

  Trailing-window download counts for the declared PyPI artifact: 27,109 downloads in the trailing month.

The resource request, including GPUs across NVIDIA, AMD, Google TPU and Tenstorrent, is dstack's own schema, and dstack provisions against it. dstack creates the fleet and places a multi-node task across those nodes, so it both chooses the machines and holds the set together, level with SkyPilot, which does the same across a different set of backends.

• https://dstack.ai/docs/concepts/fleets/ recorded 2026-09-15

  dstack's fleets documentation, describing fleets of instances provisioned by dstack across clouds, Kubernetes and on-premise, and used to place runs.

• https://github.com/dstackai/dstack recorded 2026-09-15

  Repository page and README for dstackai/dstack describing the control plane and its accelerator support.