Together AI vs Aquanode
Inference and fine-tuning APIs plus on-demand GPU clusters
Together AI is primarily a model platform (inference endpoints and fine-tuning on open models) with GPU clusters alongside. If you want to call a model rather than administer a machine, they are the better answer and we are not competing for that.
Where Together AI wins
- Managed inference and fine-tuning APIs for open models, with no machine to run at all.
- Large on-demand clusters with modern interconnect for serious training runs.
Where Aquanode wins
- Your setup outlives the GPU you rented it on. Save the environment (custom nodes, model weights, packages, the config you spent an evening getting right) and bring it back up later on a completely different provider, instead of reinstalling it from scratch every session.
- Supply moved, the price moved, or the region ran dry. Your environment follows you out. It is not stranded in the account that happened to create it, which is the part that makes leaving any single vendor cheap.
- Pause and resume in place on every provider we support: it's a snapshot-and-terminate, then a fresh box restored from that snapshot, the same mechanism everywhere. (Voltage Park's adapter supports it too, it just has no live GPUs to rent right now.) On any provider, the same environment also comes back by restoring your saved setup onto a fresh box, a little slower, same result.
- Turn on automatic snapshots yourself and pick the interval (as often as every 15 minutes, 30 by default) instead of remembering to snapshot by hand or wiring up your own cron job.
- One account, one bill and one set of keys across every provider we support, rather than a separate login and invoice per vendor every time you chase capacity.
- Run a saved version of your setup as a job. If a provider takes the box back mid-run, we detect the loss and queue the run to retry on a different provider, excluding the one that just lost it, and if the job checkpoints the next attempt picks up from the last one. Jobs scale to zero when the queue empties, so an idle one is not sitting on a rented GPU, and every job is bounded in the unit we bill: its time limit times its attempts times its machines is the most a single run can cost.
Together AI pricing vs Aquanode
Together AI figures are On-demand GPU clusters, 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://www.together.ai/pricing, On-demand GPU clusters, 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.
Together AI vs Aquanode: common questions
Do I need a GPU box if Together AI hosts the model?
If a hosted endpoint covers your use case, no. Use the endpoint. You need a box when the environment itself is the work: custom nodes, patched libraries, your own tooling on top.
Do I have to reserve GPUs on Together AI?
No. Together's pricing page lists on-demand pay-as-you-go rates per GPU per hour alongside preemptible rates, and its cheaper reserved tiers are priced at terms of 7 to 30, 31 to 90, 91 to 180, and 181 or more days.
Source: https://www.together.ai/pricing, read 2026-09-02.
