Deploy GPUs.
Not provider accounts.

One account to deploy GPU instances across RunPod, Lambda, Vast.ai, CoreWeave, and 26 more providers. Unified API, unified billing, automatic failover.

No credit card required

Free $50 credits

deploygpu-cli

◡ Checking availability across 30+ providers...

Lambda Labs 4x H100 SXM us-east-1 ✓ Available

RunPod 4x H100 SXM us-east-1 ✓ Available

Vast.ai 4x H100 SXM us-east-1 ✓ Available

CoreWeave 4x H100 SXM us-east-1 ✗ No capacity

Hyperstack 4x H100 SXM us-east-1 ✓ Available

✓ Provisioned on Lambda Labs (us-east-1)

Instance: dpg-8f3a2b1c

SSH: ssh [email protected]

Cost: 4x H100 SXM — $11.56/hr

Ready in 38 seconds.

How It Works

From command to SSH-ready in under 60 seconds

Describe Your Workload

Specify your GPU type, count, region, and image. Set optional constraints like max price or preferred providers.

We Find Availability

Our engine queries real-time inventory across 30+ providers and selects the best match based on availability, price, and reliability.

Deploy in Seconds

Your instance is provisioned automatically. SSH in immediately or connect via API. No provider account needed.

Everything You Need

Built for developers who want to focus on their models, not infrastructure management

Always Available

We check real-time inventory across 30+ providers simultaneously. When one provider is out of capacity, we route to the next. You always get GPUs when you need them.

One API / One SDK

Single REST API and Python SDK. One integration replaces ten. deploygpu deploy and we handle auth, provisioning, and networking across every provider.

Unified Billing

One invoice, one payment method, one dashboard. Stop tracking credits across RunPod, Lambda, Vast.ai, Paperspace, and others. See all spend in one place.

Never Lose a Job

Provider goes down mid-training? We automatically migrate your workload to another provider. Your jobs survive outages you'd never even know about.

Zero Lock-in

Works across RunPod, Lambda, Vast.ai, CoreWeave, Vultr, Hyperstack, and 24 more. Switch providers anytime. Export everything. You're never stuck.

Powered by gpuperhour.com

Built by the team behind the GPU pricing engine tracking 1,155+ GPUs across 30+ providers in real-time. We didn't just build the comparison — we built the deployment layer on top of it.

One Integration. Every Provider.

Deploy, manage, and monitor GPU instances with a few lines of code

CLI

Bash

# Deploy 4x H100s — best available provider

deploygpu deploy \

--gpu h100 \

--count 4 \

--region us \

--image pytorch/pytorch:2.1.0-cuda12.1

# ✓ Provisioned on Lambda Labs (us-east-1)

# ✓ SSH ready: ssh [email protected]

# ✓ 4x H100 SXM — $11.56/hr | Ready in 38s

Python SDK

Python

from deploygpu import DeployGPU

client = DeployGPU(api_key="your-key")

# Deploy with automatic provider selection

instance = await client.deploy(

gpu="h100",

count=4,

region="us",

image="pytorch/pytorch:2.1.0-cuda12.1",

auto_failover=True

)

print(f"Live: {instance.ssh_url}")

# → Live: ssh [email protected]

View full documentation

Pay for what you use

No subscriptions. No minimums. No surprises.

$0 to get started
Sign up and get $50 in free credits. No credit card until you're ready.
Pure usage-based
Pay per GPU-hour. Platform fee is baked into the price you see at deploy time. No hidden charges.
No minimums
Deploy one GPU for an hour or a hundred for a month. Scale up, scale down, stop anytime.
One invoice
All providers, all instances, one monthly bill. No more juggling credits across RunPod, Lambda, and Vast.ai.
Volume discounts
Routing $10K+/mo? We'll reduce your platform fees. The more you deploy, the less you pay.

Deploying at scale? Talk to us

Deploy GPUs. Not provider accounts.