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What is dstack?

dstack is a powerful orchestration platform that unifies GPU management for machine learning workflows across cloud, Kubernetes, and on-premise environments. Instead of requiring teams to manage complex Helm charts, Kubernetes operators, or manual infrastructure setups, dstack offers a simple declarative interface to handle clusters, tasks, and environments. It natively integrates with top GPU cloud providers for automated provisioning, while also supporting hybrid setups through Kubernetes and SSH fleets. Developers can easily spin up containerized dev environments that connect to local IDEs, allowing them to test, debug, and iterate faster. Scaling from small single-node experiments to large distributed training jobs is effortless, with dstack handling orchestration and ensuring optimal resource efficiency. Beyond training, it enables production deployment by turning any model into a secure, auto-scaling endpoint compatible with OpenAI APIs. The proprietary design ensures lower GPU costs and avoids vendor lock-in, making it attractive for teams balancing flexibility and scalability. Real-world users highlight how dstack accelerates workflows, reduces operational burdens, and improves access to affordable GPUs across multiple providers. Teams benefit from faster iteration cycles, improved collaboration, and simplified governance, especially in enterprise setups. With open-source availability, enterprise support, and quick setup, dstack empowers ML teams to focus on research and innovation rather than infrastructure complexity.

What is Submariner?

With the growing adoption of Kubernetes, businesses are increasingly recognizing the importance of managing and deploying multiple clusters to ensure vital functionalities like geo-redundancy, scalability, and fault isolation for their applications. Submariner allows applications and services to function effortlessly across different cloud providers, data centers, and geographic areas. To kick off this process, a Broker must be established on a single Kubernetes cluster, with the API server of this cluster being accessible to all other clusters linked via Submariner. This Broker can be hosted on a dedicated cluster or any of the existing connected ones. After Submariner is installed on a cluster with the necessary Broker credentials, it enables the sharing of Cluster and Endpoint objects between clusters by utilizing methods such as push, pull, and watching, thus creating connections and pathways to other clusters. It's vital that the worker node IP addresses in all linked clusters are outside the Pod and Service CIDR ranges to avoid conflicts. By adhering to these requirements, teams can fully leverage the advantages offered by multi-cluster configurations. This strategic approach not only enhances resilience but also optimizes resource allocation across diverse environments.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Kubernetes
Microsoft Azure
Python

Integrations Supported

Amazon Web Services (AWS)
Google Cloud Platform
Kubernetes
Microsoft Azure
Python

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

dstack

Date Founded

2022

Company Location

Germany

Company Website

dstack.ai/

Company Facts

Organization Name

Submariner

Company Website

submariner.io

Categories and Features

Categories and Features

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