What deployment options and getting-started paths does Velda offer?

Velda offers two deployment options: Velda Cloud, a managed cloud with instant VS Code and GPU access plus a free monthly credit, suited to individuals and small teams; and Enterprise, which runs on self-hosted or dedicated infrastructure with premium support for organizations of any size. You can start on Cloud by signing up, or talk to the team for Enterprise. Both paths use the same core workflow — prefixing a command with vrun to run it on cloud or cluster GPUs.

Velda Cloud

The managed option. According to Velda, it includes instant VS Code + GPU access and a free monthly credit, and is positioned for individual users and small teams.

Getting started:

1. Sign up from the site (a Sign-up link sits in the main navigation).
2. Launch VS Code in your browser, or connect with your preferred IDE.
3. Start from a prebuilt template — for example the PyTorch ML template with torch==1.12.1, pandas==1.4.2, and matplotlib==3.5.2 — then tailor dependencies to your workflow.
4. Run a job by prefixing your command with vrun, e.g. vrun -P h200-1 python train.py.

Expected result: your command runs on Velda's compute rather than your local machine, without image builds, manifests, or cluster setup.

Enterprise

Self-hosted or dedicated infrastructure with premium support, for organizations of any size. The stated entry path is to book a demo rather than self-serve sign-up. Use this route if you need your own infrastructure or organizational support terms.

Choosing between them

The workflow both options share

Velda's core idea is the vrun prefix: put it in front of any command to run that command from your cloud or cluster. The page's example is:

vrun -P h200-1 python train.py

The -P h200-1 flag selects the compute profile in that example. No image builds, no manifests, no dependency drift — your local development experience is preserved while the compute runs remotely. The same prefix is described as working for training, serving, or data pipelines, and for scaling from a CPU head node to H200 worker nodes.

Cost model to be aware of

Velda states you pay only for compute while your jobs run — not during development or between workloads. The page contrasts this with a traditional dev machine, where compute sits idle during coding and breaks, and with running workloads on Kubernetes or a cluster. This is the mechanism behind the "save on compute" claim: provisioning happens when jobs run, not while you code.

Where to go next

• Pricing — the site links a Pricing page for plan details.
• Docs — for setup and command reference.
• Blog — for background and comparisons.
• Talk to the team / Schedule a call — for Enterprise or questions before signing up.

If you want to try the workflow immediately, sign up for Velda Cloud and run a vrun command from your IDE. If you need your own infrastructure or organizational support, book a demo instead.