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Stable Diffusion, ComfyUI graphs, video diffusion and classic render: the work is short and intense by nature. This is exactly where per-second billing pays off most.
You pay per second and stop whenever you want. No commitment.
Your work looks like this
Image and video work does not draw a flat load curve: an idea arrives, the card boils for a while, then you look at the results. The card does not need to keep running while you look.
A batch takes 40 seconds, a video clip 12 minutes. A meter that keeps running through the review breaks in between costs more, by the end of the month, than the work itself.
Checkpoints, LoRAs, VAEs and ControlNet weights run to tens of gigabytes. Pulling them again on every pod burns both time and traffic.
A ComfyUI session left open at night stays open until morning. Instead of leaving that to good intentions, we tie it to a cap: when the limit is reached the job pauses.
Why the second matters
The calculation below is made up and illustrative: eight separate generation sessions in one day, each averaging 12 minutes. Total real usage is 1 hour 36 minutes. The left column is what we measure; the right one answers the question of what would happen in a hypothetical model that rounds up to the hour.
| Example day (made up) | Per-second billing | If hours were rounded up (hypothetical) |
|---|---|---|
| 8 sessions × 12 minutes | 96 minutes | 8 × 1 hour = 480 minutes |
| 1× RTX 4090 · $0.42/hr (example) | $0.67 | $3.36 |
| 20 working days a month | $13.44 | $67.20 |
An example and illustrative calculation; it points at no one. The right-hand column is not a competitor price but a hypothetical billing model that rounds up to the hour. Our measurement is per second: if it ran for 12 minutes 40 seconds, you pay for 12 minutes 40 seconds.
The flow
Put checkpoints, LoRAs and ControlNet weights on a persistent volume. The volume lives independently of the pod; on the next start there is no download, only an attach.
24 GB is usually enough for SDXL at 1024 px; for video diffusion, long queues and ComfyUI graphs that want 48 GB you step up a tier.
ComfyUI, A1111 or your own image. You expose the port through the pod connection; Jupyter and the terminal stay on the same screen.
Results stay on the volume. When you stop the pod your output does not go with it; you download it with a signed link whenever you want.
The compute charge stops at shutdown. The only line that keeps running is storage, and it appears as a separate row on the invoice — we do not hide that row.
Whatever your daily working habits are, the hard cap stands behind you: when it fills, the pod is paused, not deleted.
Made-up scenario
Not a real customer: an illustrative example constructed to show the product. The numbers are examples.
Choosing a card
In image work VRAM decides your resolution and how complex a graph you can run; speed decides the rhythm of iteration. The speed column below is a relative classification — we do not publish numeric benchmarks, because our scorecard bot has only just started to accumulate measurements. Rather than inventing numbers, we prefer to give an ordering.
| GPU | VRAM | Comfortable workload (example) | Relative speed class | Example price |
|---|---|---|---|---|
| RTX 3090 | 24 GB | SD 1.5 and SDXL at 1024 px, single user, experiments | Baseline | $0.21/hr |
| RTX 4090 | 24 GB | SDXL + ControlNet, fast iteration, batch generation | High | $0.42/hr |
| L40S | 48 GB | Video diffusion, ComfyUI graphs that want 48 GB | High · large VRAM | $0.79/hr |
| RTX A6000 | 48 GB | Large scene renders, high-resolution output | Medium · large VRAM | $0.88/hr |
| H100 SXM | 80 GB | Heavy video models, bulk generation in a short window | Highest | $2.19/hr |
Example prices, illustrative only; they move with supply and demand on the marketplace. "Relative speed class" is not a measurement result but a rough sense of direction. For storage and traffic line items see Pricing.
Keeping your checkpoint archive independent of the pod: Persistent Storage.
Picking offers by card, region and scorecard: GPU Pods.
Honest limits
Today you bring your own image instead of a ready-made template; that is a gap and we write it as one. The model catalogue sits in the nav with a "soon" badge — we do not describe it as if it already existed.
Frequently asked
No. A persistent volume is independent of the pod: you upload the checkpoint and LoRA archive once, then attach the same volume to later pods. Even if the pod is deleted the volume stays; only the storage line keeps running, and it appears as a separate row on the invoice.
Our target is under two minutes. The actual time depends on the size of the image and the host network; the console records the real duration of every start in the event log. We do not yet have enough measurements to publish an average "start time" figure.
Not today. Our benchmark bot tests every host regularly and the scorecard is fed by those measurements; but the data is not yet enough for a publishable, reproducible images-per-second table. Rather than inventing numbers, we give a relative classification.
The content you generate is yours; the licence of the model and checkpoint you use is your responsibility — we do not provide model licences. Platform rules are written in the terms: Terms of Use.
The hard budget cap steps in: you get a notice at 80% and 95%, and at 100% the pod is paused with its disk preserved. The cap is a brake, not a guillotine — raise the limit and carry on where you left off.
Get started
You don't pay for seconds that don't work, a hard budget cap, per-second billing. Set up your model store once, then just generate.
The console runs on the local development address: localhost:3000