dstack provides a common way to configure and run AI workloads, while Vultr supplies the cloud compute those workloads use. Vultr is a documented dstack backend, so developers can use dstack to apply configurations for inference services, batch or training tasks, and interactive development environments without treating those workloads as the same deployment type.
What dstack and Vultr each do
dstack describes itself as a unified control plane for provisioning and orchestrating GPU workloads across GPU clouds, Kubernetes, and on-premises infrastructure. It provides workload concepts such as fleets, development environments, tasks, and services. Vultr provides the underlying cloud infrastructure in the documented Vultr workflows.
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The separation matters: dstack gives you a workload configuration and orchestration workflow, but it does not decide which model, GPU, memory capacity, ports, or application settings your project needs. You still need to configure provider credentials and choose resources that suit the workload.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsdstack’s backend model distinguishes VM-based provisioning and cluster management from container-based operation, where provisioning is delegated to a cloud provider or Kubernetes. Check the current dstack backend documentation for the relevant backend behavior and configuration before applying a workload.
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What you need before deploying
- A dstack server installation or a dstack Sky account, plus the dstack CLI. The dstack quickstart says to install the server or sign up for dstack Sky before using dstack.
- A Vultr account and API key for configuring the Vultr backend. Treat the key as a secret: keep it out of public repositories and shared configuration files.
- A workload configuration matched to your application, model, and intended GPU resources.
- A fleet created before submitting runs, following the dstack quickstart workflow.
Provider versions, GPU inventory, regional availability, and prices can change. Consult current dstack and Vultr documentation and check the resources available in your chosen region before applying a configuration.
Choose the dstack workload type
The right configuration depends on whether you need an interactive workspace, a finite job, or a service that stays available to handle requests.
| Workload type | Best fit | Endpoint or interaction | What to plan |
|---|---|---|---|
| Development environment | Interactive coding, experimentation, and IDE-based work | Interactive environment rather than a persistent public inference endpoint | GPU and memory needs, development setup, and how you will access the environment |
| Task | Training, fine-tuning, batch processing, or other bounded work | Usually runs as a job rather than serving a persistent endpoint | Workload duration, compute requirements, and whether the job needs one or multiple nodes |
| Service | Persistent model inference or a web application | Publishes an application or model endpoint | Model-serving software, GPU memory, ports, and service configuration |
dstack’s overview and quickstart describe these as distinct workflow concepts; Vultr also publishes walkthroughs for development environments and tasks, in addition to inference services.
How to deploy a model service on Vultr
Vultr’s service guide, updated 15 April 2026, demonstrates deploying a DeepSeek model with dstack on L40S GPU compute. Its example prepares a Python virtual environment, configures the Vultr API-key backend, defines a service, and applies the configuration with dstack apply. It uses SGLang and requests l40s:48GB; those are example selections, not requirements for every model or deployment.
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- Set up dstack. Install the server or use dstack Sky, install and configure the CLI, and create a fleet as described in the quickstart.
- Prepare your environment. Follow the Vultr example to create a Python virtual environment and install the dependencies required by your chosen serving stack.
- Configure the Vultr backend. Provide the Vultr API key using the current backend instructions. Do not commit the credential to a public repository.
- Define the service. Specify the model-serving application, resource request, and service settings. Choose a GPU and memory capacity compatible with the model rather than copying the guide’s example without checking fit.
- Apply the configuration. Run
dstack applywith your service configuration, following the guide’s current CLI workflow. - Monitor the run. Check provisioning and workload status in dstack, then verify that the application endpoint is available and behaving as expected.
The Vultr guide says its example may take up to 10 minutes. That is a guide-specific estimate, not a guaranteed provisioning time; actual completion depends on current resource availability and the workload.
For the full example and its configuration details, follow Vultr’s dstack service deployment guide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How backend choice affects operations
dstack supports different backend models, so the key question is where infrastructure provisioning and cluster management should happen. A VM-based path involves provider VM provisioning and management; a container-oriented path can rely on infrastructure already provisioned by a cloud provider or Kubernetes. These are operational distinctions, not a performance ranking. The cited documentation does not provide a controlled benchmark comparing them.
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- Use the backend mode that matches how your team manages infrastructure and credentials.
- Confirm what dstack provisions versus what your existing platform must provide.
- Review the current backend reference rather than assuming every provider exposes identical resource behavior.
What dstack does not remove from the deployment
Orchestration simplifies how you express and apply a workload; it does not eliminate deployment decisions. You remain responsible for choosing a model and serving or training software, sizing GPU memory and other resources, setting ports and application options, and protecting API credentials. Cost and capacity also depend on Vultr’s current offerings and region. The documented guides establish workflows, not independent evidence about performance, reliability, cost, or production suitability.
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