Sundancæ Research Inc.
Pricing
Solutions

VFX & animation

Shows do not wait for a workstation. Dailies, simulation caches and final frames run on the render farm - same GPU-hour rate, either site, with a queue you can actually see.

Talk about a showRender farm
Reserved discount
Off the public on-demand rate, by term
−18%
1 mo
−24%
3 mo
−28%
6 mo
−31%
12 mo
30-day notice after month one. Same SKUs as on-demand.
DCC native
Maya, Houdini, Blender, Cinema 4D, Nuke
Priority
Jump the standard queue when the date is real
Reserved block
Lock nodes for a delivery window
Per-frame
Progress, thumbnails, automatic retry
Cost estimator

Pick a SKU, a mode and a run length

Illustrative USD before tax. Spot is typical, not a guarantee. Reserved discounts are the published 18 / 24 / 28 / 31% schedule.

Instance
How you pay
Estimate · $2.19/GPU-hour
$420
8× H100 PCIe 80GB for 24 hours on on-demand. Illustrative, USD, before tax. Spot is typical, not a guarantee.
Per hour
$17.52
Per day, 24h
$420
30-day continuous
$12614
This run
$420
Reserved discounts are off the public on-demand rate (18 / 24 / 28 / 31%). In-region transfer is included. NVMe and object storage are extra.
In practice

How this actually runs

Most teams do not buy a ‘solution’. They have a job: serve a model, finish a sequence, train a checkpoint, move off a hyperscaler bill. The GPUs are the same. What changes is isolation, term, queue and whether engineering is in the room.

Inference wants a reserved or bare-metal baseline behind load balancing, with on-demand overflow when QPS jumps. Training wants a host that will not disappear mid-epoch - bare metal or a reserved block, InfiniBand if you scale past one node. Render wants the farm software on L40S / 4090, or a reserved block if the delivery date is real.

Batch and ETL should be on spot if they checkpoint, on-demand if they do not. Reserved is wasted money on a job that runs twice a week. Scientific codes that already scale on CUDA use the same H100 / A100 SKUs as training; the research programme is a separate conversation about hybrid scheduling, not a SKU.

Migration is inventory first: map instance types, storage and network to our card, run one job on on-demand, then reserve or go bare metal if the numbers work. We will not promise global edge latency from two sites. If your users are far from our sites, we measure before we quote, and sometimes the honest answer is keep inference where they are.

Engineering retainers exist for the cases where the hard part is the system, not the card: pipelines, pricing engines, inference control planes, farm-to-deadline wiring. Same commercial model as advertising - flat monthly, compute à la carte. Discovery is two weeks against real systems before anyone signs a retainer.

Workload

Same GPUs, four different jobs

Switch the tab. The recommendation changes. If none of these is your job, say so on the contact form. We will tell you whether rentals, a retainer or a no is the honest answer.

Always-on replicas, burst when QPS jumps

Keep a reserved or bare-metal baseline behind load balancing. Overflow onto on-demand in the same site when traffic spikes. Same image, same network, higher unit price only on the extra seconds.

  • H100 / A100 / L40S are the usual SKUs
  • vLLM, TensorRT, Triton or your container
  • Cross-site failover is opt-in
  • Data and weights stay in-country
On a production

Farm work that looks like your pipeline

Dailies

Overnight lighting and animation reviews on L40S or 4090. Pull frames in the morning; do not keep a room of cards idle all day.

Sim and caches

Houdini and CPU-heavy caches land on CPU render nodes. GPU renders stay on L40S / 4090. Same account.

Finals

A reserved block for the last two weeks of a shot. You are not sharing those nodes with a random on-demand job.

Signal

Illustrative week · mix of work on one account

Inference
Train / render
62%47%31%16%0%
Mon
Tue
Wed
Thu
Fri
Sat
Sun
How to read it
Same GPUs, different jobs. Inference holds the baseline; render and training burst. Hover a point for the exact value. These series are illustrative of how the product behaves, not a live feed from your account.
Rate card excerpt

Published SKUs, both sites

Full table including reserved discount math lives on Pricing. Spot is typical. Fabric is InfiniBand at InfiniBand fabric, or 100 GbE (25 GbE on smaller cards) depending on the SKU.

InstanceSiteVRAMCPU / RAMFabricOn-demandSpot typ.
H100 PCIe 80GBInfiniBand80GB HBM324 / 200 GBInfiniBand$2.19$0.66
A100 PCIe 80GBInfiniBand80GB HBM2e16 / 180 GBInfiniBand$1.24$0.37
L40S 48GB100 GbE48GB GDDR612 / 128 GB100 GbE$0.86$0.26
RTX 4090 24GB25 GbE24GB GDDR6X8 / 64 GB25 GbE$0.41$0.12
RTX A6000 48GBMulti-site48GB GDDR612 / 96 GB25–100 GbE$0.95$0.29
CPU render nodeMulti-site64 / 256 GB25 GbE$0.68$0.20
Renderers

What we already run

Arnold, V-Ray, Redshift, Cycles, Karma, Unreal
Custom engines via Docker
Output to object storage shared with any training or editorial job
Data stays in the site you submit to
Engineering can wire the farm into an existing deadline or tractor setup
Questions

Straight answers

Both. Rentals are ordinary nodes. The farm is optional software on the same hardware. Inference usually wants load balancing, not the farm.
Related
Send a shot, not a slide deck
Renderer, resolution, average frame time on your current box, and the date. That is enough to quote a queue.
Talk about a showRender farm