RunPod Alternative

GPU cloud without the pod management

RunPod gives you raw GPU access. Velar gives you a serverless GPU platform where you write Python and forget the infrastructure. No pods, no Docker, no manual scaling.

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A higher-level GPU experience

RunPod is powerful, but it requires significant DevOps knowledge. Velar abstracts the infrastructure so your team can focus on building AI products, not managing GPU servers.

No pod management

With RunPod you provision pods, manage their lifecycle, and handle failures manually. With Velar, you write Python — the infrastructure is invisible.

Deploy in under 60 seconds

Decorate a function with @app.function(gpu='A100') and run app.deploy(). No Dockerfile, no docker build, no push to registry — unless you want to.

Scale to zero automatically

RunPod pods run 24/7 until you stop them. Velar's serverless functions scale to zero when idle and spin up on demand, so you only pay for actual GPU time.

Velar vs RunPod

Feature	Velar	RunPod
Python-native SDKRunPod requires manual API calls or third-party libraries
Scale to zeroRunPod pods keep running until manually stopped
Sub-minute deploys
Per-second billing
No Docker/Kubernetes knowledge
Serverless (auto-scale)
Built-in logsRunPod has no pod logs API
Free starter credits

From Python to GPU in 60 seconds

With RunPod, you manually provision a pod, SSH in or call the API, manage the container lifecycle, and poll for results. With Velar, you write a Python function and call it remotely. That is the entire workflow.

Read the quickstart

import velar

app = velar.App("my-model")

# Velar handles the container build automatically
image = velar.Image.from_registry(
    "pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime"
).pip_install("diffusers", "transformers")

@app.function(gpu="A100", image=image)
def run_inference(prompt: str):
    # Your code runs on GPU, returns to caller
    from diffusers import StableDiffusionPipeline
    import torch
    pipe = StableDiffusionPipeline.from_pretrained(
        "runwayml/stable-diffusion-v1-5",
        torch_dtype=torch.float16
    ).to("cuda")
    return pipe(prompt).images[0]

# Call it from anywhere
app.deploy()
image = run_inference.remote("a sunset over mountains")

Less infrastructure. More building.

Velar Cloud is paused. Join the waitlist for the relaunch. Your first deployment takes under 5 minutes.

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