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NuGenIT

Solution

We need to run AI on infrastructure we control

Regulation, contractual commitments or data sensitivity mean it cannot all sit in a public cloud.

Some workloads cannot be sent to a shared public endpoint. Regulated data, customer contracts that specify where information may reside, and internal policy all push part of the estate onto infrastructure the organisation controls — colocation, private cloud, or an on-premises cluster.

This is the requirement that touches all four layers at once, and the one where an early assumption is most expensive to reverse. Power and cooling in particular are decided long before software is chosen, and cannot be changed afterwards without a project.

What is usually going on

The residency requirement was never written down precisely

There is usually a large difference between "the data must stay in-country", "it must not leave our tenancy" and "it must never touch third-party hardware". Each implies a different architecture and a very different cost.

Power and cooling assumed rather than confirmed

Dense accelerator racks draw far more than traditional racks and may require liquid cooling. This has to be confirmed with the facility before any hardware is ordered.

Everything moved when only part had to

Frequently only a subset of the workload is genuinely constrained. A hybrid design keeps the sensitive part controlled and the rest elastic, at a fraction of the cost of moving everything.

No plan for who operates it

Owned infrastructure is a permanent operational commitment — patching, on-call, capacity planning and hardware failure. It needs an owner before it needs a purchase order.

Build versus rent decided on sticker price

An honest comparison includes power, cooling, staff, depreciation and the risk that the workload changes before the hardware is paid off.

What we look at, in order

  1. 01 Write the actual constraint down precisely, with the obligation it comes from
  2. 02 Separate what is genuinely constrained from what merely feels safer to keep close
  3. 03 Confirm what the facility can take in power and heat before anything is specified
  4. 04 Design the connectivity between controlled and elastic environments
  5. 05 Compare owning against renting over a realistic horizon, and decide the operating model first

What you receive

  • A written statement of the residency and control requirement
  • Hybrid architecture separating constrained from elastic workloads
  • Facility and connectivity requirements specification
  • Build-versus-rent comparison over an agreed horizon

Delivered as a written assessment. More on how we work.

At the facility layer we specify requirements and bring in specialists to deliver. We will tell you exactly where that line falls before you engage us.

Products relevant to this

Not a shortlist for you specifically — that depends on constraints this page cannot know. These are the options an engineering team addressing this problem will encounter first.

Schneider Electric

L1 · Power distribution and facility management

Desk Research

Power distribution, UPS, cooling and data centre infrastructure management software.

Fit
Facilities being assessed or upgraded for high-density racks.
Catch
Facility decisions are long-lived and expensive to reverse.
Read the notes

LiquidStack

L1 · High-density liquid cooling

Desk Research

Specialist liquid cooling systems, including immersion and direct-to-chip, for rack densities air cannot handle.

Fit
Facilities being retrofitted or built for modern accelerator density.
Catch
Changes how the facility is operated, serviced and warranted.
Read the notes

Megaport

L2 · Network as a service

Desk Research

A software-defined network service for creating private connections between data centres and cloud providers on demand.

Fit
Hybrid estates moving enough data for transfer charges to matter.
Catch
Only useful where they are already present in your facility.
Read the notes

Kubernetes batch scheduling

L4 · Batch scheduling

Desk Research

Add-ons giving Kubernetes the queueing and gang-scheduling behaviour AI workloads need and plain Kubernetes does not provide.

Fit
Organisations already standardised on Kubernetes.
Catch
You assemble a scheduler from components rather than buying one.
Read the notes

Other problems

Our inference costs rise faster than our usage We are weighing model APIs against running our own It works in the pilot but not in production We cannot see where our AI spend actually goes We are paying for accelerators that sit idle We are locked into one model provider Our data pipeline is the bottleneck, not the compute We are about to commit, and it is hard to undo

Tell us what you are trying to solve.

Describe your current setup and the problem in your own words. We reply within 2 working days with an honest assessment of whether we can help.

Request an assessment hello@nugenit.in