The orchestration layer for modern ML teams
dstack gives your team a single control plane to run development, training, and inference jobs on GPU—whether on hyperscalers, neoclouds, or your on-prem hardware. Avoid vendor lock-in and minimize GPU spend.
One control plane for all your GPUs
Instead of wrestling with complex Helm charts and Kubernetes operators, dstack provides a simple, declarative way to manage clusters, containerized dev environments, training, and inference.
This container-native interface makes your team more productive and your GPU usage more efficient—leading to lower costs and faster iteration.
Run on any cloud. Or all of them.
dstack natively provisions GPU instances and clusters across your preferred cloud providers—maximizing efficiency and minimizing overhead.
Connect GPU clouds directly to dstack natively, or run dstack on top of Kubernetes if needed.
Bring your own compute
Have bare-metal servers or on-prem GPU clusters? dstack makes it easy to integrate them and manage everything alongside your cloud resources.
With SSH fleets, you can connect any existing cluster in minutes. Once added to dstack, it's a first-class resource — available for dev environments, tasks, and services.
Code locally, run remotely
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.
Run complex training with simple config
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.
Deploy scalable, OpenAI-compatible 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
Kubernetes is a widely used container orchestrator designed for general-purpose deployments. To efficiently support GPU workloads, Kubernetes typically requires custom operators, and it may not offer the most intuitive interface for ML engineers.
dstack takes a different approach, focusing on container orchestration specifically for AI workloads, with the goal of making life easier for ML engineers.
Designed to be lightweight, dstack provides a simpler, more intuitive interface for development, training, and inference. It also enables more flexible and cost-effective provisioning and management of clusters.
For optimal flexibility, dstack and Kubernetes can complement each other: dstack can handle development, while Kubernetes manages production deployments.
Slurm excels at job scheduling across pre-configured clusters.
dstack goes beyond scheduling, providing a full suite of features tailored to ML teams, including cluster management, dynamic compute provisioning, development environments, and advanced monitoring. This makes dstack a more comprehensive solution for AI workloads, whether in the cloud or on-prem.
dstack is designed for ML teams aiming to speed up development while reducing GPU costs across top cloud providers or on-prem clusters.
Seamlessly integrated with Git, dstack works with any open-source or proprietary frameworks, making it developer-friendly and vendor-agnostic for training and deploying AI models.
For ML teams seeking a more streamlined, AI-native development platform, dstack provides an alternative to Kubernetes and Slurm, removing the need for MLOps or custom solutions.
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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.
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