The open gridfor machinescale.
Tap a worldwide pool of GPU hardware at a fraction of what legacy clouds charge. Same containers. Same tooling. Radically different economics.
Three ways
onto the grid
Serve models on ArdenML
Hit leading open-weight models through a drop-in, [OI]-compatible endpoint. Sub-second time-to-first-token without provisioning a single machine yourself.
Launch ArdenMLDeploy on the console
Point the console at your container image, set your ceiling price, and let independent providers bid for the workload. You keep the savings.
Deploy NowBecome a compute provider
Idle GPUs are lost revenue. List your hardware on the grid and earn every time a workload lands on your silicon.
Become a ProviderMarket pricing,
not margin
protection
One H200, one hour. Same silicon, four providers. The spread is not a discount. It is the markup you stop paying.
Why the gap?
Legacy clouds set static rates to protect corporate margins, then leave hardware idle. On Arden every lease clears through a global reverse auction, so prices track real utilization instead of a rate card. You pay for the silicon, not the provider's overhead.
Config to running
workload in seconds
Configure your deployment
Pick a template or bring your own container image. Declare the resources you need: GPU model, region preference, memory, and the maximum price you will pay.
Automated bidding
Your spec broadcasts to the grid. Independent providers bid against each other in a reverse auction that typically closes in seconds.
Authorize and execute
Accept the winning lease and your workload launches on the provider's hardware. Monitor logs, shell in, and tear down from the same console.
Stop renting
gatekeepers
Deploy on a global marketplace of high-density GPUs and stretch your engineering runway instead of your cloud bill.
Docker native
No refactoring, no proprietary SDK. If it runs in a container, it runs on Arden. Migrate from a legacy cloud with zero changes to your application code.
Train three times longer
H100s at $1.33 an hour against AWS at $3.93 buys you nearly three hours of compute for the price of one. Transparent pricing, no hidden line items.
One-click templates
Pre-configured environments for Llama, DeepSeek, and Stable Diffusion. Configuration to running container in under sixty seconds.
Global silicon supply
Enterprise H100s and A100s alongside high-performance consumer RTX 5090s. Scale from single-node inference to interconnected training clusters.
Distributed clusters
Provision multi-node Ray clusters natively and fine-tune large models without hand-managing cluster orchestration.
Operational autonomy
Keep absolute control over network routing and data residency. Leaving closed ecosystems protects your stack from single-provider lock-in.
Meridian scaled a viral image engine on Arden
When Meridian's avatar campaign went from pilot to front-page in a weekend, their managed GPU quota did not. The team pointed their inference service at the Arden grid, pooled distributed high-performance GPUs behind one endpoint, and absorbed the surge with no manual infrastructure work.
"The next phase of AI is not just better models. It is efficient infrastructure. Some of our workloads already run locally, and the open grid lets us extend that same efficiency into the cloud when demand spikes."
No off switch
Centralized clouds concentrate risk in single points of failure. Arden spreads workloads across a distributed provider protocol, so your deployment stays independent and resilient.
Start building on the grid
Launch containerized AI workloads from the self-serve console, or coordinate a custom enterprise configuration with our team.