Reserved capacity
Cards held for a fixed term at a committed rate rather than taken on demand.
Training runs, and inference with a predictable floor of traffic.
We build and run on thisRent the card, not the data centre.
This is not a company on the page — it is how we buy compute for you. Capacity rented from providers we work with, sized to the workload, run by us, and billed as one line.
Not a boxed licence — we design and operate workloads here, and can place your committed spend through us.
What we work with
The products, and who each one suits.
5 listed
Fractional GPU
A slice of a card by the hour instead of the whole thing.
Development, evaluation and inference that would leave most of a card idle.
Inference endpoints
A model served behind an endpoint, with batching, caching and autoscaling in front of it.
Product teams who need an API, not a cluster to look after.
India-resident placement
Capacity inside India for workloads with a residency condition attached.
Regulated sectors, public-sector buyers, and anyone with a data clause in their contract.
Metering and unit cost
Cost per answer, per document, per customer, measured against real traffic.
Any finance team that has been handed a GPU bill with no denominator on it.
What buying through us changes
A partnership is only worth what it changes.
Better than list on committed capacity
Where capacity is placed through a provider we hold a partnership with, committed spend prices below the published hourly rate. We size the commitment to measured usage — over-committing is the common way to lose the saving you were buying.
One escalation path across providers
Named contacts at the providers we use, and we carry the escalation rather than handing it to you. When a node fails you do not learn a new support portal for each vendor.
Startup credits we sponsor
GPU providers and the hyperscalers run credit programmes for early-stage companies. We prepare and sponsor the application; the award is the provider’s decision, and we will not pretend we can influence it.
How we help
The work around the product.
- Profile the workload first — memory, batch size, context length, expected concurrency — so the card is chosen on evidence rather than on fashion.
- Compare providers on your own model and hand you the numbers, including the ones that do not favour our own recommendation. One of the providers we use, Podstack AI, was co-founded by Anocloud’s CEO — we put that in writing before the comparison starts.
- Place and manage the commitment, so there is one invoice and one line to query.
- Build the runtime: images, drivers, schedulers, checkpointing, and restarts that do not lose a week of training.
- Serve the model in production with batching, caching and autoscaling, then measure cost per answer and reduce it.
- Give the capacity back when the project ends — being able to stop is the point of renting.
Where this is not the answer
Renting is not always cheaper. If you will keep a card busy around the clock for years, buying hardware wins on the spreadsheet, and we will show you that comparison instead of hiding it. Fractional GPUs do not suit every workload either — some models want the whole card, and pretending otherwise buys you an out-of-memory error in the middle of the night. We do not own a data centre and we manufacture nothing: every card here belongs to a provider, and their availability constrains our start dates as much as yours. If the workload can never leave your building, renting is not the answer — we will design the on-premises build instead and say so plainly.
Next step
Buying GPU as a Service? Talk to us first.
Tell us what you are trying to do and we will come back with the sizing, the licensing and the price — from a partner, within two working days.