详细内容
The orchestration layer for modern ML teams
dstack provides ML teams with a unified control plane for GPU provisioning and orchestration
across cloud, Kubernetes, and on-prem. It streamlines development, training, and inference — reducing costs 3–7x and
preventing lock-in.
Install open-source Documentation
One control plane for GPUs across cloud, Kubernetes, and on-prem.
An open platform for GPU orchestration
Managing AI infrastructure requires efficient GPU orchestration, whether workloads run
on a single GPU cloud, across multiple GPU providers, or on-prem clusters.
dstack provides an open stack for GPU orchestration that streamlines development, training,
and inference, and can be used with any hardware, open-source tools, and frameworks.
Native integration with GPU clouds
dstack natively integrates with top GPU clouds—automating cluster provisioning and
workload orchestration to maximize efficiency and minimize overhead.
It can provision and manage VM clusters through native integrations or via Kubernetes.
Easy to use with on-prem clusters
For provisioned Kubernetes clusters, connect them to dstack using the Kubernetes backend.
If you run vanilla bare-metal servers or VMs without Kubernetes, use SSH fleets
instead.
Either way, connecting existing on-prem clusters to dstack takes just minutes.
Dev environments
Before training or deployment, ML engineers explore and debug their code.
dstack's dev environments make it easy to connect your
desktop IDE to powerful cloud or on-prem GPUs—streamlining the entire development loop.
Single-node & distributed tasks
Move from single-instance experiments to multi-node distributed training without friction. dstack lets you define complex jobs with a simple configuration,
handling the scheduling and orchestration for you.
This allows your team to focus on research while ensuring that expensive cluster resources are utilized efficiently.
Scalable service endpoints
With dstack, you can easily deploy any model as a secure,
auto-scaling OpenAI-compatible endpoint, all while using your custom code, Docker image, and
serving framework.
Loved by world-class ML teams
Wah Loon Keng
Sr. AI Engineer @Electronic Arts
With dstack, AI researchers at EA can spin up and scale experiments without touching
infrastructure. It supports everything from quick prototyping to multi-node training on any cloud.
Aleksandr Movchan
ML Engineer @Mobius Labs
Thanks to dstack, my team can quickly tap into affordable
GPUs and streamline our workflows
from testing and development to full-scale application deployment.
Alvaro Bartolome
ML Engineer @Argilla
With dstack it's incredibly easy to define a configuration
within a
repository
and run it without worrying about GPU availability. It lets you focus on
data and your research.
Park Chansung
ML Researcher @ETRI
Thanks to dstack, I can effortlessly access the top GPU
options across
different clouds,
saving me time and money while pushing my AI work forward.
Eckart Burgwedel
CEO @Uberchord
With dstack, running LLMs on a cloud GPU is as
easy as running a local Docker container.
It combines the ease of Docker with the auto-scaling capabilities of K8S.
Peter Hill
Co-Founder @CUDO Compute
dstack simplifies infrastructure provisioning and AI
development.
If your team is on the lookout for an AI platform, I
wholeheartedly recommend dstack.
FAQ
How does dstack differ from Slurm?
dstack fully replaces Slurm. Its
tasks cover job submission, queuing, retries, GPU
health checks, and scheduling for single-node and distributed runs.
Beyond job scheduling, dstack adds
dev environments for interactive work,
services for production endpoints, and
fleets that give fine-grained control over
cluster provisioning and placement.
You get one platform for development, training, and deployment across cloud, Kubernetes, and
on-prem.
How does dstack compare to Kubernetes?
Kubernetes is a general-purpose container orchestrator. dstack also
orchestrates containers, but it provides a lightweight and streamlined interface that is purpose
built for ML.
You declare
dev environments,
tasks,
services, and
fleets
with simple configuration. dstack provisions GPUs, manages clusters via fleets with fine-grained
controls, and optimizes cost and utilization, while keeping a simple UI and CLI.
If you already use Kubernetes, you can run dstack on it via the Kubernetes backend.
Can I use dstack with Kubernetes?
Yes. You can connect existing Kubernetes clusters using the Kubernetes backend and run
dev environments,
tasks, and
services on it.
Choose the Kubernetes backend if your GPUs already run on Kubernetes and your team depends on its
ecosystem and tooling.
See the
Kubernetes guide for setup and best practices.
If your priority is orchestrating cloud GPUs and Kubernetes isn’t a must, VM-based backends are a better fit
thanks to their native cloud integration.
For on-prem GPUs where Kubernetes is optional, SSH fleets provide a simpler and more lightweight alternative.
When should I use dstack?
dstack accelerates ML development with a simple, ML‑native interface.
Spin up dev environments, run
single‑node or distributed tasks, and deploy services without infrastructure overhead.
It radically reduces GPU costs via smart orchestration and fine‑grained fleet controls, including efficient reuse,
right‑sizing, and support for spot, on‑demand, and reserved capacity.
It is 100% interoperable with your stack and works with any open‑source frameworks and tools, as
well as your own Docker images and code, across cloud, Kubernetes, and on‑prem GPUs.
Have questions, or need help?
Get started in minutes
Install dstack on your laptop via uv
and start it using the CLI, or deploy it anywhere with the dstackai/dstack Docker image.
Set up backends or SSH fleets, then add your team.
fast → uv tool install "dstack[all]"Downloading dstack-0.19.2-py3-none-any.whl████████████████████████████████████████ 100%dstack serverApplying ~/.dstack/server/config.yml...The admin token is d7ad7f92-9f35-470d-be0e-c7645ce71a4cThe dstack server 0.19.2 is running at http://127.0.0.1:3000 restart ↻
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