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Design the solution around a measurable business outcome—not around a model or cloud service you have not yet shown you need. Define who the solution serves, what decision or process it should improve, and the security, performance, availability, recovery, and cost constraints it must meet. Then choose the simplest suitable AI approach and service model, map the full workload, and plan how you will evaluate and operate it.
Start with the business outcome and constraints
Write a brief that connects the proposed AI workload to a business need. Microsoft Learn’s guidance on architecture specifications stresses that design decisions should be rooted in clear business needs and agreed requirements—not in technology choices made first.
Make the brief specific enough to guide architecture decisions:
- Outcome: What measurable result should improve, and what is the current process or decision?
- Users and decisions: Who will use the system, and what will they do with its output?
- Success measures: How will you judge whether the solution works? Include task quality and, where relevant, business-process measures.
- Data: Which sources are needed? Who owns them? How sensitive are they, and what access, retention, lineage, or compliance rules apply?
- Workload constraints: What response times, availability and recovery objectives, expected demand, and peak loads must the design support?
- Delivery constraints: Which existing systems must it integrate with? Which regions and budgets apply? What skills does the team have to build and operate it?
Involve product owners, business partners, technical leads, developers, operations staff, and other stakeholders who own these requirements. Record what is agreed, what remains uncertain, and how decisions connect to the requirements.
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Decide whether AI fits the task
Describe the task before selecting a model. Predictive or discriminative AI estimates an outcome or assigns a class; generative AI creates content. Some problems may be better handled by deterministic software or a human workflow. Compare those alternatives rather than assuming an AI system is necessary.
For any candidate approach, define what counts as an acceptable result and what kinds of errors matter. A system that suggests a low-risk next step has different review needs from one whose output affects a consequential decision. Specify where a person must check, approve, or override the output.
AI behavior can be nondeterministic, so testing must reflect the actual workload and its risks. Microsoft’s Azure AI workload guidance recommends workload-specific evaluation and responsible design; it is guidance for Azure-oriented workloads, not a claim that every AI task should use Azure.
Choose a service model that fits
There is no universal ranking of managed services, platforms, and custom implementations. Compare them against your requirements, including data governance, customization, explainability, latency, availability, team capability, operating burden, and total cost. Begin with the least complex option that can meet the business and control requirements.
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| Approach | When to assess it | Key trade-off |
|---|---|---|
| Prebuilt or managed service, including SaaS | The task is common and generic outputs or behavior may be sufficient. | Can reduce the need to build and operate model infrastructure, but may not provide the business-specific behavior, data controls, or compliance characteristics required. |
| Platform service, including PaaS | You need a managed environment to build, connect, deploy, or operate a more tailored solution. | Offers a platform for customization, but the team still needs to design, evaluate, and manage the application and its lifecycle. |
| Custom implementation or model | Specific behavior, control, or requirements cannot be met by an available prebuilt or platform approach. | Can support specialized needs, but adds responsibility for data, evaluation, deployment, and ongoing maintenance. |
These are design categories, not product recommendations. Microsoft’s overview distinguishes SaaS, PaaS, and custom-build choices, and its guidance makes the build-versus-buy decision dependent on business need. Do not treat custom training as automatically better: it is justified only when its benefits outweigh the additional lifecycle work.
Map the complete workload
Draw the path from source data to user outcome, including the operational services that make the solution secure and supportable. Microsoft’s Azure AI reference pattern groups the work into data processing and analytics, model training or fine-tuning, intelligent applications, AI practices and processes, and platform services. Adapt the pattern to your use case rather than adopting every component by default.
Data
Identify source systems, ingestion, validation, preprocessing, storage, retention, access controls, and governance. For business information used to ground an assistant, include the steps that clean, enrich, index, retrieve, and refresh that information. Stale or poorly prepared source material can undermine the usefulness of generated answers.
Model lifecycle
Choose a model or service based on the task and evaluation evidence. Include training or fine-tuning only when the need is established. Track versions, evaluate changes before release, and monitor for changes in data or performance that could affect results.
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Application
Map the user interface or API, business logic, orchestration, and model inputs. Add retrieval when answers need relevant business context. Specify guardrails, feedback handling, and any human review or approval steps the task requires.
Platform and operations
Account for identity, network boundaries, secrets, encryption, monitoring, deployment automation, scaling, backup and recovery, and cost controls. These are part of the workload design, not optional extras to bolt on after a model has been selected.
Compare viable designs against the same criteria
If more than one architecture could meet the brief, score or discuss each against the same questions. The comparison below combines business requirements with the reliability, security, cost, operational excellence, and performance concerns emphasized in cloud architecture guidance. It is a decision framework, not a provider performance ranking.
| Decision area | Questions to resolve |
|---|---|
| Business fit | Does this design support the agreed outcome and success measures? |
| Data and governance | Can it access the necessary data lawfully and securely, with suitable retention and lineage? |
| Quality and risk | How will you evaluate accuracy, robustness, explainability, bias, and unsafe outputs? What errors are tolerable? |
| Reliability and recovery | What availability and recovery objectives apply? What can fail, and what dependencies could interrupt the workload? |
| Performance and scale | Can it meet required latency and throughput at expected and peak demand? |
| Cost and team capacity | What are the model, data, compute, and operating costs? Can the team support the design? |
| Change over time | How will updates to the model, data, service, or application be evaluated and rolled out? |
Resolve gaps against requirements rather than allowing an attractive feature or an assumed provider advantage to decide the outcome. Provider-specific features, prices, regional availability, and compliance suitability must be checked for the intended deployment; they cannot be inferred from general architecture principles.
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Set evaluation and operating plans before launch
Choose representative test data and task-specific measures before comparing candidates. Microsoft’s Azure AI overview gives accuracy, precision, sensitivity, and specificity as examples of evaluation measures. Which measures matter depends on the task and the relative cost of different errors; a single headline score may conceal a failure that matters to users.
For generative systems, test whether outputs are grounded in appropriate information, useful for the task, safe, and appropriately uncertain. Define how people can report problems and how the team will investigate them. Plan monitoring, incident handling, rollback, and periodic reassessment alongside the release process. Microsoft’s AI methodology guidance highlights experimentation, responsible design, explainability, model decay, and adaptability as relevant concerns.
In production, evaluate changes rather than assuming an existing result will hold after a model, data source, service, or application update. Define who can approve a release, what evidence is required, and how to return to a known-good version if a change causes unacceptable results.
Document decisions and keep the design current
Maintain an architecture specification that connects requirements to the chosen design. Record the alternatives considered, reasons for the choices, security and compliance constraints, assumptions, and any unresolved risks. Include routine, ad hoc, and emergency operating procedures so that the system can be supported beyond its initial build.
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Review the specification with the stakeholders responsible for the business outcome and the workload. Update it when requirements, evidence, services, or operating conditions change; a design is useful only while it reflects the real workload.
Keep provider guidance in scope
Microsoft’s Azure Well-Architected AI workload guidance and architecture patterns offer a structured reference for Azure-oriented designs. AWS also publishes a Machine Learning Lens for designing and operating ML workloads on AWS, including custom and pretrained approaches. These are provider-specific references rather than a provider-neutral standard. Use them to inform questions, then verify current product capabilities, regional deployment options, costs, and compliance requirements for the actual workload. Microsoft’s Azure Machine Learning guidance describes a managed service for training, deploying, and managing ML models; product names and capabilities can change, so consult current provider documentation when implementing.
What a sound design decision looks like
A defensible cloud AI architecture can be traced from the business outcome through requirements, service choice, data and application design, evaluation, and ongoing operations. If those connections are unclear, the next step is to clarify the workload brief—not to add a more sophisticated model.
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