AI Platforms & Models

Private AI Deployment

Where data sovereignty, sector regulation, or air-gapped operation prevents sending data to a third-party model provider at all.

Private deployment runs models inside your own environment. It is chosen when hosted services cannot satisfy a requirement at any price, which is usually a compliance decision rather than an engineering preference.

The trade is real in both directions. Running your own inference means capacity planning, upgrades, and hardware a hosted service handles for you, and it can lag on model availability. What it buys is the ability to meet requirements hosted deployment cannot.

What the work includes

  • Model selection and deployment inside your infrastructure or private cloud
  • Capacity planning sized to real usage rather than to a demo
  • Upgrade and rollback paths, since model changes become your operation
  • Integration with existing identity, network, and monitoring
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Related services

AI Infrastructure Setup

Before AI workloads move from experiment to something people depend on during their working day.

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Data Governance for AI

Before an AI system is pointed at a corpus nobody has classified, which is the point at which governance stops being theoretical.

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AI Security & Observability

Before broad rollout, not after. Logs you did not collect cannot be reviewed retrospectively.

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Model Management

Once a model change is capable of altering behaviour people rely on, which is immediately after the first production deployment.

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// Common Questions

Questions
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Common questions about Private AI Deployment.

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When data sovereignty rules, sector regulation, or air-gapped operation prevent sending data to a third-party API. The deciding factor is usually a contractual or legal constraint on where data may travel, rather than a general preference for keeping things in-house.

Access to the largest frontier models, which are generally API-only, and the operational convenience of someone else handling capacity. You gain control over where data goes and what the workload costs at volume. Which trade favours you depends on the constraint driving the decision.

Yes, and it is a common arrangement: sensitive workloads run privately while less sensitive ones route to hosted models. This requires the routing decision to be made on data classification rather than convenience, and enforced rather than left to whoever writes the call.

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