Vast.ai is a marketplace: you rent GPUs supplied by many providers, often at very low prices, with reliability that varies by host. Lanes Compute offers curated GPUs from A100 to H200 and multi-node clusters with fast interconnect, aimed at consistent hardware for training and serving. Lanes Compute is in early access.
Concretely, Lanes Compute is on-demand GPU infrastructure with a deliberately short menu: A100, H100, and H200 SXM, plus L40S and other options for lighter jobs, from one card to multi-node clusters, billed per second, running your own containers. This page is about the trade the two models make: price against predictability.
At a glance
| Dimension | Lanes Compute | Vast.ai |
|---|---|---|
| Model | Curated on-demand GPUs and clusters | Marketplace of provider-supplied GPUs |
| Consistency | Curated, consistent hardware | Varies by host |
| Multi-node | Yes, InfiniBand and NVLink | Depends on the offer |
| GPUs | A100, H100, H200 SXM, plus L40S | Whatever hosts supply |
| Storage | Fast persistent network volumes | Depends on the offer |
| Billing | Per second | Market priced |
| Status | Early access | Generally available |
| Docs | Lanes Compute | vast.ai |
Where Lanes Compute fits
Lanes Compute is for when you want predictable hardware and real multi-node clusters, with fast interconnect for distributed runs, and per-second billing. Consistency and cluster quality are the point.
In practice that means the card you asked for, provisioned the same way every time, with InfiniBand between nodes and NVLink within each node when a job spans machines. Persistent network volumes keep datasets and checkpoints in place between runs, you choose the region, and reserved capacity covers longer runs. The compute FAQ covers GPUs, billing, and data handling.
Where Vast.ai fits
Vast.ai is hard to beat on price and breadth. If your priority is the cheapest available GPU and you can work around variable hosts, the marketplace model is a genuine advantage.
A marketplace matches buyers to whatever hardware providers list. Supply is broad and prices float with it, which is exactly what cost-driven work wants. The flip side is that the machine, the network, and the host are properties of each offer rather than constants, which is the variability the table above describes.
How Lanes does it
- One consistent supply. A100, H100, and H200 SXM today, with L40S and other options for lighter workloads, taken on demand or reserved for longer runs. There is no marketplace layer: you request hardware and get the same thing each time.
- Clusters as a product. Multi-node capacity comes wired with InfiniBand between nodes and NVLink within a node, sized from a single card upward, for training runs that outgrow one box.
- Per-second billing with persistent storage. You pay while the environment is live and nothing after teardown, and fast network volumes hold datasets and checkpoints between runs. See right-size and pay per second.
- Your stack and your data. Bring your own containers and frameworks, PyTorch, JAX, and vLLM among them. You choose the region, and Lanes does not train on your data or share it.
Choose Lanes Compute if
- You want curated, consistent hardware and multi-node clusters.
- A lost or slowed run costs you more than the gap between offers.
- Distributed jobs need interconnect that behaves the same run after run.
- You want per-second billing with volumes that persist between runs.
Choose Vast.ai if
- Lowest price and marketplace flexibility matter most.
- Your workloads tolerate host variability and you can retry when needed.
- You are running short, cost-driven jobs where any capable card will do.
Both positions are coherent. The marketplace is built for price discovery, Lanes Compute for repeatability, and workloads tend to sort cleanly into one or the other.
Further reading
- Right-size every job and pay per second: pick the card per job and stop paying at teardown.
- Train models on on-demand GPUs: rent exactly what a training run needs, when it needs it.
- Batch processing and experiments: spin up per task and pay only for the window it runs.