Private Cloud AI
Cloud AI that runs in your tenant, on your terms
Private cloud AI means running AI models inside your own Azure, AWS or Google Cloud account, with your identities, your encryption keys and your logs, instead of sending data to a public AI service. Rhentech designs and runs it for London firms that need the speed and scale of cloud AI with the control a regulated business requires.
30 minutes, free of charge. We respond within one business day.
- Your tenantModels deployed in your own cloud account, not a shared consumer service.
- Your keys and logsEncryption keys and the audit trail stay under your control.
- Vendor-neutralAzure, AWS or Google, chosen for the workload.
What does a private cloud AI deployment include?
Seven workstreams, from platform choice to day-to-day operation.
- 01
Workload and provider choice
We match each workload to the platform that fits it: Azure for firms built on Microsoft 365, AWS for firms already running there, Google Vertex AI where Google models or Workspace are central. The choice is driven by your data and estate, not by a vendor relationship.
- 02
Your own tenant, private networking
Models are deployed inside your own cloud account or tenant, reached over private network connections rather than the open internet, so your AI sits behind the same perimeter as the rest of your cloud estate.
- 03
Data location
Where UK data residency matters, we deploy to the providers' UK regions where the chosen model is offered there, and document where any processing happens outside them. That record supports your UK GDPR obligations on international transfers.
- 04
Identity and encryption keys
Access runs through your existing identities and conditional access. Customer-managed encryption keys are keys you create and control in the provider's key service, so you, not the provider, decide who can read stored data.
- 05
Model routing and cost control
We route each task to the most cost-effective model that meets the quality bar, set budgets and alerts, and keep the option to switch models as prices and capability change. Aggregators such as OpenRouter are used for testing and comparison where the data allows.
- 06
Logging and audit trail
Every request and response can be logged to your own storage, with the user, the model and the sources used, so you can answer a client, auditor or regulator about how an answer was produced.
- 07
Operations and optimisation
We monitor quality, usage and spend, update deployments as better models arrive, and report quarterly on value and risk. This can run as a monthly retainer, on its own or alongside managed IT.
How does a private cloud AI project run?
Part of the Turnkey AI loop: discover, build, manage, review, then repeat.
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Discovery
We map the workloads, the data classes and the users, and compare cloud, private cloud and on-premise options. You get a recommendation with the reasoning.
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Architect and deploy
Tenant design, networking, identity, keys and logging first, then models and integrations, each stage tested before staff rely on it.
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Manage and review
Monitoring, cost control and model updates, with a quarterly review of value and risk.
Platforms we work with
- Microsoft Azure
- AWS
- Google Vertex AI
- OpenRouter
- Microsoft 365
- Cloudflare
What do leaders ask about private cloud AI?
Not answered here? Ask it on the call.
What is private cloud AI?
Private cloud AI is AI deployed inside your own account with a major cloud provider, using your identities, encryption keys, network controls and logs. It differs from consumer AI tools, where the provider controls the environment and its terms. It sits between public AI services and fully on-premise AI.
When is private cloud better than on-premise AI?
When demand varies, when you need results quickly, or when you want access to the strongest managed models without buying hardware. On-premise is the better answer when data must not leave your site at all, or when steady high volume makes owned hardware cheaper.
Which provider should we use?
Usually the one your business already runs on. Firms built on Microsoft 365 often fit Azure best, AWS suits firms with workloads there, and Google Vertex AI fits where Google models or Workspace are central. We compare them on your data, models and cost before recommending.
Will our data be used to train the provider's models?
Enterprise cloud AI services generally state that customer prompts and outputs are not used to train their models, but terms differ by service and change over time. We check the current terms for each service you use and record the position in your AI register.
Can this be managed with the rest of our IT?
Yes. Private cloud AI can sit inside your Rhentech managed IT service, with monitoring, cost control and model updates on a monthly retainer, and governance covered by the same quarterly review.
Free consultation
Get the scale of cloud AI without giving up control.
A 30-minute call with a senior consultant about your workloads and the right platform for them. We respond within one business day.
Or call 020 3886 3001 or message us on WhatsApp.
What happens next
- 01You tell us briefly what has landed on your desk.
- 02We respond within one business day to arrange a time.
- 03A 30-minute call with a senior consultant, and an honest view of what to do first.