dstack

dstack

dstack is a container orchestration platform for AI/ML teams, offering a unified control plane to simplify the end-to-end workflow from development and training to deployment, helping teams efficiently manage GPU resources and significantly reduce costs.
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AI container orchestrationGPU resource management platformmachine learning workflow orchestrationmulti-cloud AI infrastructuredstack AI orchestration

Features of dstack

Unified GPU resource provisioning and orchestration across multi-cloud, on-premises, and Kubernetes environments
Configures development environments, job scheduling, model serving, and other settings to cover the full AI workflow
Native compatibility with NVIDIA, AMD, TPU and other accelerators to avoid vendor lock-in
Fleet (resource pool) mechanism enables on-demand resource creation and automatic release of idle resources
Simplifies distributed training setup and enables fast inter-cluster connectivity to optimize node-to-node communication

Use Cases of dstack

When AI engineers need to quickly create interactive development environments (e.g., Jupyter) for model experiments and code debugging
When ML teams orchestrate large-scale model training and fine-tuning on hybrid-cloud or on-prem clusters
When you need to deploy auto-scaling model inference endpoints compatible with OpenAI API
When teams want a unified view of heterogeneous GPU resources across different cloud providers or on-prem data centers
For complex AI tasks like distributed training that require multi-node high-speed interconnects and collaboration

FAQ about dstack

QWhat is dstack? What problem does it solve?

dstack is an open-source container orchestration platform designed for AI/ML workflows. It provides a unified control plane for ML teams to simplify the end-to-end process of developing, training, fine-tuning, and deploying generative AI models, reduce the complexity of managing underlying infrastructure (such as Kubernetes), and optimize GPU resource costs.

QWhat deployment environments and hardware does dstack support?

dstack supports multi-cloud (e.g., AWS, GCP, Azure), on-premise server clusters, and existing Kubernetes environments. On the hardware side, it natively supports NVIDIA, AMD, TPU, Intel Gaudi and other leading AI accelerators.

QWhat prerequisites are required to use dstack?

The basics are to install Git, Docker, and Docker Compose. After deploying the dstack server and CLI tools, you enable resources by configuring them (e.g., Fleet) in a config file. For on-prem clusters, you only need Docker and SSH keys to manage.

QWhat is Fleet in dstack? What does it do?

Fleet (resource pool) is a core concept in dstack that defines and manages a group of compute resources (such as number of nodes, GPU types and quantities). It supports on-demand resource creation and automatic release of idle resources after tasks complete to control costs, and is a key component for efficient GPU orchestration.

QHow does dstack help reduce AI project costs?

dstack achieves cost savings through unified resource orchestration and intelligent scheduling, delivering GPU resources on demand and maximizing utilization to avoid idle capacity. It claims to help teams reduce infrastructure costs by 3x to 7x.

QIs dstack suitable for individual developers or enterprise teams?

dstack is designed for AI/ML teams, whether startups or large enterprises. It offers a range of deployment options from open-source self-hosted to hosted services (dstack Sky), meeting the needs of individual developers or small teams for experimentation as well as enterprise-grade, large-scale production deployments.