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Private and Hybrid AI Infrastructure

Where should our AI actually run, and what will it cost?

Decide which AI workloads belong on public services, private cloud or on-premises, then build and run them.

In brief

Private and Hybrid AI Infrastructure is a Kalosys service that decides which AI workloads should run on public AI services, private cloud or on-premises, based on data sensitivity, volume, latency and a three-option total cost comparison. Kalosys then designs, deploys and, if you choose, runs the environment.

THE PROBLEM

Why this matters now

Public AI APIs are the fastest way to start, but they get expensive at volume and raise questions about where sensitive data goes. Organizations need to decide which workloads stay public and which move to private cloud or on-premises, and then build and run that environment.

56% → 41%

drop in enterprises using public cloud as the main home for production AI inference, in one year

Broadcom Private Cloud Outlook 2026

56%

now run or plan production inference on private cloud

Broadcom Private Cloud Outlook 2026

HOW IT WORKS

Start small, prove value, then run it

Public AI services are the fastest way to start, but at volume they get expensive, and some data shouldn't leave your control. We help you decide which AI workloads should stay on public services and which belong on private cloud or on-premises, with a clear cost comparison. Then we design, deploy and, if you want, run that environment.

01 · Fixed fee

Assess

  • Classify AI workloads by data sensitivity, volume and latency
  • Compare total cost: public API, private cloud, on-premises
  • Check capacity, power and cooling

YOU RECEIVE

  • Workload placement recommendation
  • Three-option cost comparison
  • Reference architecture
  • Recommendation, including staying on public services

02 · Fixed scope

Build

  • Select and procure the platform
  • Deploy model serving and an AI gateway
  • Integrate identity, network, security and monitoring
  • Move the first workloads

YOU RECEIVE

  • Deployed private AI environment
  • As-built documentation
  • Operating runbooks

03 · Monthly

Run

  • Capacity management and patching
  • Model updates and performance monitoring
  • Hardware maintenance and support

YOU RECEIVE

  • Monthly capacity and performance report

SCOPE AT A GLANCE

What's included, and what isn't

Included

  • The AI workloads in scope and a three-option cost comparison
  • Design, deployment and handover of the chosen environment

Not included

  • Building the AI applications themselves, which sits in Pilot to Production

Done when

The environment is running, documented and accepted by the team that will operate it.

OUTCOMES

What you get

  • Predictable cost at scale
  • Sensitive data stays under the client's control
  • Performance for latency-sensitive workloads
  • A clear policy for where each AI workload runs

IS THIS FOR YOU?

You may need this if

  • Fast-growing API or GPU bills
  • Data residency or customer contract requirements
  • Existing data centre or colocation capacity
  • An upcoming hardware refresh

QUESTIONS

What leaders usually ask

Do you supply the hardware?

Kalosys can provide the hardware and its maintenance as part of the engagement. We'll confirm how that is structured for you.

Which platforms do you support?

Kalosys's infrastructure practice covers storage, networking and compute, data center migration and consolidation, and hybrid cloud, across Microsoft, AWS, VMware, Nutanix, Proxmox and Hyper-V.

We don't have room in our data center for GPU servers.

That's common. Kalosys also does data center consolidation, which often frees the space and power needed. The assessment checks capacity, power and cooling as part of the cost comparison.

How do you compare the options?

We model total cost for each option at your expected volumes, including hardware, power, operations and licensing for private options, and usage charges for public ones.

We're all-in on public cloud.

That's fine. The assessment may confirm that's right for you. Where it helps is showing the cost at your expected volumes and which workloads, if any, would be cheaper or safer elsewhere.

GPU hardware is expensive and hard to get.

It can be, which is why we size it to real workloads and compare against cloud options first. Kalosys handles procurement and maintenance as part of the engagement.

We don't have the skills to run AI infrastructure.

That's what the Run service is for: we manage capacity, patching, model updates and performance.

RELATED

Often paired with

AI Cost Control"Our AI bill is growing faster than its results."AI Resilience"What happens when the AI goes down, or goes wrong?"AI Decision Review"Should we make this AI investment, and is it the right one?"

Not sure where to start?

Start with an AI Decision Review, or talk to us about Private and Hybrid AI Infrastructure. Every engagement starts with a fixed fee and a clear recommendation.

GET IN TOUCH

Talk to us about Private and Hybrid AI Infrastructure

Tell us what you are working on. A senior member of our AI team will get back to you within one business day.

Prefer to talk now? Talk to Alleria, our AI sales agent.

Last updated October 29, 2026