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What is dstack?

dstack is an open-source control plane for provisioning compute and running AI workloads across clouds, Kubernetes, and bare-metal clusters. Built for both engineers and AI agents, it simplifies infrastructure orchestration for training, inference, and sandbox environments. The tool supports NVIDIA, AMD, TPU, and Tenstorrent GPUs, and integrates natively with major cloud providers and open-source frameworks. dstack eliminates the need for Kubernetes or Slurm by providing a lightweight, container-first interface. Users can define fleets, dev environments, tasks, and services using simple YAML configurations. It is designed to reduce GPU costs through smart orchestration, right-sizing, and support for spot and reserved instances. dstack is trusted by over 100 AI-first companies and is ideal for teams that need to scale ML experiments without infrastructure overhead.

AI Tool Review Summary

Performance Score

4.5/5

Content/Output Quality

High, consistent, and on-brand

Interface

Clean and minimal

AI Technology
LLMNLPComputer Vision
Purpose of Tool

Unified control plane for provisioning compute and running AI workloads across diverse GPU infrastructure.

Compatibility

Works with any cloud, Kubernetes, or bare-metal cluster; integrates with VS Code, Cursor, Windsurf, and OpenAI-compatible endpoints.

Pricing

Freemium with paid tiers

Features

Features with the highest value for users are highlighted here.

Multi-GPU vendor support (NVIDIA, AMD, TPU, Tenstorrent)

Cloud, Kubernetes, and bare-metal provisioning

Fleet management with auto-scaling and idle deprovisioning

Dev environments with VS Code, Cursor, Windsurf integration

Distributed training and batch job scheduling

High-performance inference serving with OpenAI-compatible API

Cost optimization via spot and reserved capacity

SSH fleets for on-prem GPU clusters

How It Works

1

Install dstack

Install dstack on your laptop using uv or deploy the Docker image anywhere.

2

Configure backends

Connect your cloud accounts, Kubernetes clusters, or SSH fleets via simple YAML.

3

Define workloads

Create YAML configurations for dev environments, tasks, or services.

4

Run and scale

Apply the configuration to provision compute and run workloads with automatic scaling.

Who Is It For?

AI Engineers

ML Researchers

DevOps Teams

Data Scientists

Startups

Enterprise AI Teams

AI Agents

Academic Researchers

MLE Teams

Infrastructure Managers

Pricing

Open Source

$0/free
  • Self-hosted
  • All core features
  • Community support

Sky Free

$0/monthly
  • Hosted by dstack
  • $5 GPU credits
  • Bring your own clouds
Popular

Sky Pro

$Usage-based/monthly
  • Hosted by dstack
  • GPU marketplace access
  • Priority support

Enterprise

Custom/yearly
  • Self-hosted
  • SSO
  • Air-gapped
  • Dedicated support

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Pros & Cons

Pros

  • Simplifies AI infrastructure orchestration without requiring Kubernetes or Slurm expertise.
  • Supports a wide range of GPU vendors and deployment options, enabling flexibility and cost savings.

Cons

  • May have a learning curve for users unfamiliar with YAML configuration.
  • Advanced features like disaggregated inference may require additional setup.

FAQs

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Rating

5.0

Pros

Cons

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