dstack is joining the Vultr Cloud Alliance, bringing its open-source AI orchestration platform to Vultr's high-performance cloud infrastructure. This collaboration simplifies AI training and deployment, allowing developers and enterprises to run workloads efficiently, scale seamlessly, and reduce costs while maintaining complete control over their AI stack.
By combining dstack’s streamlined alternative to Kubernetes and Slurm with Vultr’s global cloud and diverse GPU offerings, AI teams can now access a powerful and flexible environment tailored to their needs.
A cost-effective, high-performance solution for AI teams
AI training and deployment have traditionally been complex, requiring teams to navigate orchestration tools like Kubernetes and Slurm, optimize for high cloud costs, and struggle with limited GPU options. These obstacles slow down development and create unnecessary inefficiencies. dstack and Vultr are changing that by providing a seamless and scalable solution for AI teams of any size.
With dstack’s intuitive orchestration capabilities and Vultr’s predictable pricing, developers can now focus on building and refining AI models rather than dealing with infrastructure headaches. This partnership ensures AI teams have access to an open, flexible, and cost-effective environment for model training, inference, and experimentation.
Solving the complexity of AI orchestration
Managing AI workloads can be daunting, with intricate configurations and high-performance demands. The integration of dstack with Vultr simplifies this by offering:
- Streamlined AI orchestration: dstack eliminates Kubernetes and Slurm complexity, allowing teams to focus on building AI models without extensive infrastructure management.
- Cost-effective performance: Vultr’s predictable pricing and support for NVIDIA and AMD GPUs enable teams to optimize budgets without compromising on power.
- Flexibility for every AI stack: dstack’s open-source approach, combined with Vultr’s composable infrastructure, gives developers the freedom to choose the best tools for their workflows.
- Scalability and global reach: With 32 cloud data center regions, Vultr’s network reaches 90% of the global population within 2-40 ms latency, enabling AI teams to deploy models where needed with minimal latency.
Practical strategies for AI developers
- Select the right GPU: Choose top-of-the-range AMD and NVIDIA GPUs via Vultr Cloud GPU to power your AI workloads. NVIDIA GPUs are ideal for deep learning models that require high memory bandwidth, while AMD GPUs offer cost-efficient training and inference solutions.
- Simplify your AI training deployment with dstack: Automate workload scaling without the complexity of Kubernetes or Slurm, using dstack’s CLI/API for rapid AI model training and deployment on Vultr’s cloud infrastructure.
- Maximize cost savings and performance: Leverage Vultr’s predictable pricing to plan AI budgets effectively while testing different GPU configurations to find the best mix of efficiency and affordability.
- Scale globally with ease: Deploy AI models closer to end users with Vultr’s extensive cloud network, minimizing latency by strategically placing workloads in optimized locations worldwide.
Explore our joint resources to learn more
- Tech talk: Deploy LLMs with dstack on Vultr
- Help documentation: How to deploy services with dstack on Vultr
- Help documentation: How to run tasks with dstack on Vultr
- Help documentation: How to deploy dev environments with dstack on Vultr
Register for the upcoming Linux Foundation webinar (February 25, 2025, 10:00 AM PST): Join us for an exclusive tech talk on Simplifying AI Container Orchestration on Vultr with dstack. Learn how dstack streamlines AI workload management without Kubernetes, enabling seamless model fine-tuning and deployment with NVIDIA and AMD GPUs on Vultr.
With dstack and Vultr, AI teams can deploy, scale, and optimize workloads efficiently while keeping infrastructure costs under control. Learn more about dstack and Vultr today.