Solutions

Enterprise AI

Plan model selection, training or transformation, compute, delivery, and ongoing operations as connected decisions. Existing AI products cover distinct needs; choose them from the workload and deployment boundary, not from a generic promise.

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Business challenges

Enterprise AI work often spans teams and runtimes. Clear data boundaries and measurable acceptance criteria help prevent a prototype from being mistaken for a production service.

Select a model and runtime that fit task, licensing, data, and hardware constraints.
Decide where data, training, inference, evaluation, and artifacts are handled.
Plan ownership for access, model updates, monitoring, and operational handover.

Relevant products

Explore AI Hosting, the model catalogue, and Model Training as distinct product paths with their own scope and commercial terms.

AI HostingReview hosted AI services and their available public entry points. AI model catalogueInspect currently documented model releases, capabilities, and hardware guidance. Model TrainingReview the customer-model training workflow and its delivery boundaries.

Logical architecture

A workload path separates data preparation, model work, compute, delivery, and operational ownership.

01Data and taskApproved inputs, objectives, and evaluation criteria

02Model workSelection, training, transformation, or inference

03Compute and runtimeEligible model, hardware, and execution environment

04Delivery and operationsArtifact or service, monitoring, and ownership

Logical reference only. The actual runtime, provider, data path, and service boundary depend on the selected product and engagement.

Use cases

Assess a model-serving workload and candidate hardware.
Develop a customer model with an agreed dataset, objective, and evaluation plan.
Move a validated model artifact toward a supported hosted or customer-run runtime.

Deployment considerations

Choose hosted or customer-managed execution per workload. Document data handling, model rights, environment access, artifact delivery, runtime responsibility, and any hosting dependencies before work begins.

Integrations

Application access through product-supported APIs or delivery paths.
Customer data and identity systems within an approved, documented scope.
Evaluation, storage, and monitoring components selected for the specific runtime.

Commercial engagement

Start with workload discovery and an agreed acceptance plan. Training, model engineering, and hosting are separate product decisions and may be separately priced.

Documentation

Read the product and technical documentation for each selected stage; available catalogue entries and published capabilities can change.

Trust and responsibilities

Make data and model ownership reviewable

Document permitted data, model licensing, access, retention, artifact ownership, and validation responsibilities. Availability of a model or runtime does not itself establish rights for a particular use or verify an output.

Architecture resources

Pricing and procurement

Price each existing product on its own terms

Consult current product pages and catalogue records for published prices and orderable options. Work spanning training, transformation, and hosting may require separate configurations or a scoped proposal; no bundled price is implied here.

Procurement Request a formal quote

Frequently asked questions

Questions for AI planning

Does this solution include every AI product in one contract?

No. It connects relevant product paths for planning. Confirm availability, scope, and commercial terms for each product or written proposal.

Does listing a model mean it is supported for every workload?

No. Check the model release details, documented capabilities, licensing, and runtime fit for the intended use.

Scope an AI workload across its full lifecycle

Describe the task, data boundary, target environment, and how your team will assess delivery.

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