Environment-first workflow
Develop in a real cloud dev environment that behaves like your local machine, then scale from the same terminal.
Skip to content
Serverless GPU Cloud
Run training and inference jobs without wasting GPUs on idle time or setup.
Avoid the overhead of containers and cluster management. Pay only for compute when your jobs run, not during development or between workloads.
vrun -P h200-1 python train.pyBuilt for teams shipping models every week
Everything you need to go from experiment to production inference without switching platforms.
1 command
Scale any local command to cloud GPUs with vrun.
H100 / H200
High-end accelerators available on demand for training and evaluation.
Multi-cloud
Run on AWS, GCP, Azure, or your own infrastructure from one workflow.
No manifests
No Kubernetes YAML, no Docker image pipeline for every experiment.
Why ML teams pick Velda
Keep developer velocity high while workloads scale from single-GPU prototyping to distributed training.
Distributed training without cluster plumbing
Launch multi-node jobs with vbatch -N [n] and skip manual NCCL, worker IP, and SSH-key setup.
Portable by design
No proprietary SDK lock-in. Your commands stay framework-native across PyTorch, Ray, JAX, and custom stacks.
Serving and batch in one platform
Run batch pipelines and deploy auto-scaling HTTP services without maintaining a separate serving stack.
Snapshot and reproduce
Snapshot the full environment before job submission so queued and rerun jobs stay reproducible.
Cloud cost control
Use your existing cloud credits, reserved capacity, and enterprise discounts from connected providers.
From code to production, no context switching
A simple path for modern ML teams that need speed and reliability.
01
Start in a template
Clone a team template and begin coding immediately in browser VS Code or your local IDE.
02
Scale with command prefixes
Use vrun for bigger instances and vbatch for distributed jobs.
03
Deploy services instantly
Point Velda to your command and port, then auto-scale the service with built-in routing.
$ vrun -P h200-1 python train.py --dataset imagenet
$ vbatch -N 16 -- python pretrain.py --epochs 90
$ vrun --service --port 8000 python serve.pyHow Velda compares
Different platforms optimize for different workflows. Velda is designed for full-lifecycle ML development.
| Capability | Modal | RunPod | SageMaker | Velda |
|---|---|---|---|---|
| No SDK rewrite required | ❌ | ✅ | ❌ | ✅ |
| Container-free GPU execution | ✅ | ❌ | ❌ | ✅ |
| First-class distributed training primitive | ❌ | ❌ | Limited | ✅ |
| Interactive cloud dev environment | ❌ | ❌ | ❌ | ✅ |
| Multi-cloud scheduling with BYOC | ❌ | ❌ | AWS-only | ✅ |
Velda Cloud
Managed cloud with instant VSCode + GPU access, plus free monthly credit. Perfect for individual and small teams.
Enterprise
Self hosted or dedicated infrastructure, premium support for organizations of any size.