Enterprise Architecture

Customer-cloud, dedicated or managed AI: choosing the deployment boundary

Enterprise AI deployment is an architecture decision involving data, identity, networking, operations, cost and regulatory constraints.

There is no universally correct deployment model

A vendor-managed service can be the simplest and most capable option. A customer-cloud deployment can give the organisation tighter network and runtime control. Hybrid designs can keep selected data or execution inside customer boundaries while still using managed model APIs.

Ask what must remain controlled

Identify sensitive data classes, identity systems, private network dependencies, required logging, key-management requirements and where tool execution occurs. Those constraints determine which components need to run where.

Operational responsibility matters

A customer-hosted architecture shifts more responsibility for patching, availability, scaling and observability to the customer or implementation partner. Dedicated managed deployments trade some direct control for operational ownership.

Design for change

Model providers and policies will evolve. Separating the model backend from the enterprise context and action layer can reduce the cost of future change.

Apply this to your environment.

If this problem exists in your estate, we can review the current architecture and determine whether an assessment, pilot or engineering engagement makes sense.

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