Simplified AI workload orchestration
dstack is an open-source alternative to Kubernetes and Slurm, designed to simplify AI development for ML engineers. It streamlines AI workloads and GPU orchestration across top clouds and on-prem clusters.
AI-native container orchestration
Designed for ML engineers, it simplifies development, training, cluster management, and inference.
dstack optimizes GPU provisioning, container orchestration, job scheduling, metrics monitoring, and cost efficiency.
dstack integrates natively with top GPU clouds and runs seamlessly on private clouds and data centers.
IDEs & notebooks
Dev environments allow you to provision a remote machine, set up with your code and favorite IDE, with just one command.
Dev environments are perfect for interactively running code using your favorite IDE or notebook before scheduling a task or deploying a service.
Training & fine-tuning
Tasks allow you to schedule jobs or run web apps. Tasks can run on single nodes or be distributed across clusters. You can configure dependencies, resources, ports, and more.
Tasks are ideal for training and fine-tuning jobs, running apps, or executing batch jobs, including those using Spark and Ray.
Scalable inference
Services let you deploy models or web apps as private or public auto-scalable endpoints. You can configure dependencies, resources, authorization, auto-scaling rules, and more.
Once deployed, the endpoint can be accessed by anyone on the team.
Managing clusters
Fleets streamline provisioning and management of cloud and on-prem clusters, ensuring optimal performance for AI workloads.
Once created, a Fleet enable teams to run Dev environments, Tasks, and Services.
ML engineers dstack
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.
Andrew Spott
ML Engineer @Stealth
Thanks to dstack, I get the convenience of having a personal Slurm cluster and using budget-friendly cloud GPUs, without paying the super-high premiums charged by the big three.
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.
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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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