By Mateus P. Mateus P.
on GPU & Compute Platforms

dstack

The orchestration stack for heterogeneous AI compute

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What Is dstack?

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dstack is an AI‑native orchestration stack designed to standardize how AI workloads run across heterogeneous compute, including GPU clouds, Kubernetes clusters, and on‑prem hardware. It provides a unified control plane with first‑class primitives for provisioning accelerators, scheduling workloads, and monitoring performance, so teams can run development environments, training jobs, and inference services through a single interface.

The platform is vendor‑agnostic and supports hardware from providers like NVIDIA, AMD, and Tensorfront, as well as TPU accelerators. Teams can bring their own infrastructure by connecting GPU cloud accounts, Kubernetes clusters, or bare‑metal servers over SSH.

For organizations without existing GPU capacity, dstack Sky offers access to a hosted GPU marketplace that can be combined with private or on‑prem resources. With its CLI, UI, and API, dstack removes much of the overhead of managing Kubernetes or Slurm directly, enabling scalable, secure orchestration of modern AI workloads.

Quick Snapshot

dstack gives AI teams a single, vendor‑agnostic control plane to standardize how they provision, schedule, and manage workloads across GPU clouds, Kubernetes, and on‑prem clusters. This simplifies infrastructure operations, improves accelerator utilization, and lets teams focus more on development, training, and inference.

Works on
  • Web
  • Linux
  • Mac
  • API
Pricing Model
Cannot determine the price.
Fits on
Affiliate Program
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API Availability
dstack has an API available.
Key Features
  1. Unify AI workloads across clouds and on‑prem
  2. Vendor‑agnostic support for diverse accelerators
  3. Simplified orchestration via CLI, UI, and API
Audience
  • ML engineers
  • AI researchers
  • MLOps teams
  • infrastructure engineers
  • startups building AI products
  • AI platform teams
URL
https://dstack.ai

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dstack

Key Features of dstack

Unified control plane

Provides a single orchestration layer for managing AI workloads across GPU clouds, Kubernetes clusters, and on‑prem environments.

Heterogeneous accelerator support

Supports a range of accelerator hardware, including GPUs from NVIDIA, AMD, Tensorfront, and TPU accelerators, through vendor‑agnostic primitives.

Workload scheduling

Offers first‑class workload scheduling capabilities tailored to AI tasks, covering development environments, training jobs, and inference services.

Fleet and resource management

Unifies fleets, volumes, gateways, and other infrastructure components, making it easier to provision and manage compute resources for AI workloads.

CLI, UI, and API access

Lets teams interact with the platform through a simple command‑line interface, web UI, or programmatic API to integrate into existing workflows.

dstack Sky marketplace

Provides an optional hosted GPU marketplace that can be used alongside private clouds and on‑prem clusters to extend available compute capacity.

Use Cases for dstack

Unified AI infrastructure

Centralize management of GPU clouds, Kubernetes, and on‑prem clusters into a single control plane, reducing the complexity of running AI workloads across multiple environments.

Model training orchestration

Schedule and run training jobs on the most suitable accelerators, improving GPU utilization while avoiding manual coordination across different clusters and providers.

AI development environments

Provision consistent, accelerator‑backed development environments for ML engineers, so they can iterate on models without dealing with low‑level infrastructure setup.

Inference and services deployment

Deploy inference services onto heterogeneous hardware fleets with standardized workflows, making it easier to scale production AI systems reliably.

Hybrid and multi‑cloud AI

Combine private clouds, on‑prem hardware, and dstack Sky’s GPU marketplace to build a flexible, hybrid AI infrastructure that can scale with workload demands.

Frequently Asked Questions

What is dstack and who is it for?

dstack is an open‑source orchestration stack for heterogeneous AI compute, designed for ML engineers, AI researchers, MLOps teams, infrastructure engineers, and AI platform teams that need to manage workloads across GPU clouds, Kubernetes, and on‑prem clusters.

How does dstack handle heterogeneous AI compute?

dstack provides a unified control plane with vendor‑agnostic primitives for provisioning accelerators and scheduling workloads, so you can run AI development, training, and inference across GPUs and other accelerators from different providers without changing your workflows.

Can I use my existing cloud and on‑prem infrastructure with dstack?

Yes. You can connect major GPU cloud accounts, Kubernetes clusters, and bare‑metal or on‑prem servers via SSH, allowing dstack to orchestrate workloads across your current infrastructure.

What is dstack Sky?

dstack Sky is a hosted GPU marketplace that integrates with the dstack control plane, giving teams access to additional GPU resources that can be combined with private clouds and on‑prem clusters.

Is dstack open‑source?

Yes. dstack is offered as an open‑source orchestration stack that you can deploy and run on your own infrastructure.

Does dstack replace Kubernetes or Slurm?

dstack does not replace Kubernetes or Slurm but abstracts their complexity by providing an AI‑focused orchestration layer on top, reducing the need to manage these systems directly for day‑to‑day AI workloads.

Does dstack offer an API for integration?

Yes. dstack includes an API, along with a CLI and UI, so you can integrate orchestration capabilities into your existing tools and pipelines.

dstack · Our Verdict

dstack addresses a real pain point for AI teams by consolidating fragmented GPU, Kubernetes, and on‑prem resources into a single, AI‑first control plane. Its open‑source model and vendor‑agnostic design make it particularly attractive for organizations that want flexibility without committing to a single cloud or hardware provider.

For teams scaling AI workloads, the combination of unified scheduling, accelerator provisioning, and optional access to dstack Sky’s GPU marketplace is a strong operational advantage.

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