AI infrastructure and GPU capacity
Model deployment, job queues, API access and shared GPU pools. We match the configuration to your processing type and usage frequency — not to the top of the price list.
Running models
We deploy image and video generation, transcription and speech synthesis models. ComfyUI with ready-made workflows, or a custom pipeline shaped around your process.
- text-to-image and image-to-video
- text-to-video and video sequence processing
- speech-to-text, subtitles, speech synthesis
- Local deployment or cloud GPUs
GPU infrastructure
We pick the GPU type for the job: for most generative scenarios, extra headroom doesn't translate into a proportional speed-up.
- GPU choice based on model and job volume
- VRAM usage optimisation
- Batch job processing
- GPU utilisation monitoring
Shared GPU resources
Several projects run in one compute pool with isolated workloads and their own quotas. That removes the need to pay for a card around the clock for a few hours of work.
- Pay for your share of the resources
- Isolated workloads and separate keys
- Queue quotas and priorities
- Move to a dedicated GPU when needed
API access
Models become an ordinary HTTP service: your application submits a job and receives the result through a webhook or a file link.
- REST API with access keys
- Webhooks on job completion
- Per-client rate limits
- Integration examples
Processing queues
Long jobs don't block the interface. The queue spreads the load, keeps statuses and lets you track progress.
- Job priorities
- Retries on failure
- Statuses and execution history
- Protection against worker overload
Automatic scaling
Worker count follows the queue: capacity is added at peaks and released when things go quiet.
- Scaling by queue depth
- Maximum spend limits
- Fast worker start-up
- Graceful job completion
Private deployment
If the data can't leave your perimeter, we deploy the models inside your infrastructure and hand control to your team.
- Deployment in your environment
- Isolated network and access
- Documentation and knowledge transfer
- Support under a separate agreement
Dedicated GPU or shared pool
Both work — the question is simply how even your workload is.
Dedicated GPU
- The full card is available to one project
- Predictable latency under constant load
- Billed around the clock regardless of usage
- Makes sense with a steady stream of jobs
Shared pool
- Resources are distributed across several projects
- You pay for the share you actually need
- A queue with quotas and priorities
- Makes sense with uneven load
There is no guaranteed saving: if jobs run continuously, a dedicated card can be the better deal. That's why we look at the load profile first.
Find the right infrastructure model
A concept calculator with no payment involved. It doesn't show an exact price — it suggests which option to consider first.
Image and video generation, inference of large models
Load profile
Light
Suggested model
Shared GPU pool
What to consider
The load is uneven, so a dedicated card would idle most of the time. A shared pool gives you GPU access while you pay for your share of the resources.
- GPU access without renting a whole card
- A queue with quotas for your project
- Room to move to dedicated resources later
The result is a reference point, not a commercial offer. Exact cost depends on the models, file sizes, latency requirements and the chosen provider — so it is calculated individually.
Need GPU capacity for a specific workload?
Tell us which models you use and what volume you expect, and we'll prepare a configuration option and an estimate.