Lambda vs Aquanode
AI cloud running its own datacenters, on-demand instances and 1-Click Clusters
Lambda owns and operates its own hardware, which buys consistency you do not get from a broker — the same instance type behaves the same way every time. It is also materially more expensive per GPU-hour than the marketplace floor, and an environment you build there stays there.
Where Lambda wins
- First-party datacenters and a hand-tuned ML image — consistent performance and a support path to people who own the machines.
- 1-Click Clusters for multi-node training with InfiniBand, which is a genuinely different product from single-box rental and one we do not offer.
- A long track record with large training customers, which matters if you are committing to a multi-week run.
Where Aquanode wins
- Your environment survives the box. Pause a deployment, come back tomorrow, resume with packages, model weights and config intact — instead of rebuilding from a fresh image every session.
- A snapshot taken on one provider restores onto another. Supply moved, price moved, or a region ran dry — the environment follows, rather than being stranded in the account that created it.
- One account across every provider we broker, so you are not maintaining separate logins, keys and billing per vendor to chase capacity.
- Automated backups are on by default when you attach storage at deploy time, at a 6-hour interval — you do not have to remember to configure them.
Lambda pricing vs Aquanode
Lambda figures are On-demand instances, price per GPU per hour, read from their own pricing page on 2026-08-05. The Aquanode column is the lowest live per-GPU rate in our marketplace feed and moves on its own — the two columns are not measured the same way, so treat this as a starting point, not a quote.
Source: https://lambda.ai/pricing — On-demand instances, price per GPU per hour. Verified 2026-08-05. Vendors change prices; check theirs before deciding.
What we do not claim
Restoring an environment requires a snapshot that already exists. Stopping a deployment yourself captures it on the way out, so you can bring it back later on any provider. A provider-side termination is different: it is only recoverable if you had already switched automated snapshots on for that deployment, and it costs you the work since the last one. Automated snapshots are opt-in — nothing runs until you start it — and with none running there is nothing to restore.
`aq deploy --snapshot <id>` rents the cheapest matching GPU and restores your snapshot onto it — including onto a different provider than the one it came from. Creating the snapshot is a separate step today: the standalone ogre CLI on the box writes it.
Lambda vs Aquanode: common questions
Is Lambda more reliable than a GPU marketplace?
For consistency of a single instance type, generally yes — they own the hardware. The trade is price and portability: a Lambda instance is a Lambda instance, and if the capacity you want is not there you wait rather than move.
Can I run multi-node training on Aquanode?
No. Lambda's 1-Click Clusters are the right tool for interconnected multi-node training and we do not have an equivalent. Aquanode is for single-box workloads whose environment you want to keep.
