Simplified AI workload orchestration
dstack is an open-source alternative to Kubernetes and Slurm, designed to simplify GPU allocation and AI workload orchestration for ML teams across top clouds, on-prem clusters, and accelerators.
Efficient cloud GPU provisioning and utilization
dstack natively integrates with top GPU clouds, streamlining the provisioning, allocation, and utilization of cloud GPUs and high-performance interconnected clusters.
dstack provides a unified interface on top of GPU clouds, simplifying development, training, and deployment for ML teams.
Orchestrating workloads on existing clusters
Whether you have an on-prem cluster of GPU-equipped bare-metal machines or a pre-provisioned cluster of GPU-enabled VMs, you just need to list the hostnames and SSH credentials of the hosts to add the cluster as a fleet for running any AI workload.
Launching containerized dev environments
Before running training jobs or deploying model endpoints, ML engineers often experiment with their code in a desktop IDE while using cloud or on-prem GPU machines. Dev environments simplify and streamline this process.
Scheduling jobs on clusters and single instances
Tasks simplify the process of scheduling jobs on either optimized clusters or individual instances. They can be used for pre-training or fine-tuning models, as well as for running any AI or data workloads that require efficient GPU utilization.
Deploying auto-scaling model 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.
Trusted 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.
Get started in under a minute
Install dstack on your laptop via pip and start it using the CLI, or deploy it anywhere with the dstackai/dstack Docker image.
Configure the dstack server with cloud accounts or connect it to on-prem clusters once it's running.
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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