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Use Microsoft Azure for AI by matching the workload to the right platform before you deploy anything. Microsoft Foundry is the usual starting point for generative-AI applications and agents; Azure Machine Learning is better suited to traditional machine learning, custom training, fine-tuning and end-to-end MLOps; and a standalone Azure OpenAI resource can be enough for an application that only needs model completions.
After choosing the platform, select a model and deployment type that fit your traffic, latency, budget and data-processing boundary. Then design identity, networking, monitoring, responsible-AI controls and recovery as part of the workload—not as optional add-ons. Model availability, regions, deployment types, prices and preview features change, so verify them for your exact subscription and location immediately before provisioning.
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1. Identify the AI workload before choosing an Azure service
The first decision is what the system must do. Microsoft’s Azure Well-Architected guidance separates intelligent applications from training and fine-tuning workloads because they have different lifetimes, dependencies, scaling patterns, availability requirements, security concerns and responsible-AI risks.
Intelligent applications
An intelligent application uses an existing model to perform a user-facing or business task. Examples include a grounded support assistant, an internal knowledge chatbot, document summarization, extraction from incoming forms, or an agent that calls business tools. These applications usually need prompt and context management, retrieval, tool access, authentication, monitoring, content controls and a reliable serving path.
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Training, fine-tuning and traditional machine learning
A training workload creates or adapts a model. It may involve data preparation, experiment tracking, distributed compute, feature engineering, model registration, evaluation and repeatable deployment. A conventional forecasting, classification or recommendation system may not need a generative-AI application platform at all.
Training and serving can be connected, but they should not automatically share the same workspace, permissions or release process. Treat the model-development lifecycle and the production application lifecycle as related systems with separate operational controls.
| What you need to build | Typical Azure path | Important design questions |
|---|---|---|
| Generative application or agent | Microsoft Foundry, with connected Azure services | Which model, grounding sources, tools, identity, safety controls and response-time target are required? |
| Traditional ML or custom model training | Azure Machine Learning | How will data preparation, experiments, compute, registration, deployment and retraining be governed? |
| Simple completion service | Standalone Azure OpenAI resource may be sufficient | Do you really need agent hosting, evaluation, project isolation or other Foundry capabilities? |
| Open-source or custom-weight model serving | Foundry managed compute, where supported | Is the model available, what GPU capacity is required, and is the preview status acceptable? |
2. Choose the Azure platform that matches the work
Microsoft Foundry for generative applications and agents
For a new generative-AI application or agent, Microsoft recommends considering Foundry as the development platform. It brings model access, agent development and connections to other Azure services into a workload-oriented environment. A Foundry resource provides the top-level governance boundary, while projects provide development isolation. Connected services can supply storage, search and secrets management.
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A single Foundry resource with one project is described as a sensible default for an individual exploring the platform. That boundary may be inadequate for multiple teams, separate environments, regulated data or different ownership and billing requirements. Decide whether projects should be separated by team, application, environment or compliance boundary before a large number of assets accumulate.
Azure Machine Learning for custom models and MLOps
Azure Machine Learning remains the better fit when the central problem is traditional ML, custom model training, fine-tuning, advanced data preparation or end-to-end MLOps. It provides the operational concepts needed to move from data and experiments to registered models and managed endpoints.
Microsoft’s guidance recommends separating exploratory and production workspaces, providing sufficient compute, using controlled release strategies such as blue-green deployment for managed online endpoints, and considering availability zones or multiple regions where the workload justifies them. These are Azure Machine Learning recommendations; they are not a guarantee that every Azure AI service has the same failover behavior.
Standalone Azure OpenAI for narrow completion scenarios
If an application only needs text or multimodal completions from an Azure OpenAI model, a standalone Azure OpenAI resource may be simpler than introducing the broader Foundry project model. Choose the narrower option deliberately: once you need agent hosting, evaluation workflows, connected tools, richer project isolation or a wider model-management surface, Foundry may provide a more suitable home.
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3. Plan the resource and environment boundaries
Azure AI workloads normally span more than a model endpoint. The application may depend on identity services, storage, search, secret management, logging, networking and a compute platform that hosts the user interface or API. Assign ownership and lifecycle rules to each dependency.
Separate development, test and production
- Use distinct environments so experiments cannot change production prompts, indexes, secrets or model deployments accidentally.
- Give developers least-privilege access to projects and connected services rather than broad subscription permissions.
- Keep production data and test data separated, especially when prompts or retrieved documents may contain personal or confidential information.
- Define how a model, prompt, retrieval index, safety configuration and application build are promoted together.
Decide the network and secret-management model
Foundry supports networking choices, but the exact configuration depends on the isolation requirement and may require SDK or command-line work rather than a single portal setting. Decide whether public access is acceptable, whether private connectivity is required, which subnets and DNS paths are involved, and how applications authenticate without embedding keys.
Use a managed secret store for credentials and connection details. Rotate secrets, audit access and ensure that logs do not accidentally retain sensitive prompts, retrieved passages or model responses.
4. Select a model and deployment type
Deployment type affects where inference data may be processed, how capacity is purchased and which traffic patterns are economical. The options shown for a particular model depend on the Azure region and the model’s support matrix.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware match| Deployment choice | Processing boundary | Best suited to | Billing or capacity model | Key caution |
|---|---|---|---|---|
| Standard | Depends on the selected standard scope; verify the model’s current regional terms | Interactive, variable or moderate traffic | Pay per token | Throughput, quotas and latency can vary with demand and model availability. |
| Global | Inference data may be processed in any Azure region | Workloads that prioritize broad capacity and do not require a single geography for processing | Standard or provisioned variants may be offered, depending on the model | Confirm that worldwide processing is acceptable for your contractual and regulatory requirements. |
| Data zone | Processing is constrained to the specified Azure data zone | Organizations that need a defined zone without limiting processing to one geography | Standard or batch variants may be offered, depending on the model | Read the current service definition for the zone and model; availability is not universal. |
| Regional or geography-based provisioned | Processing remains within the selected Azure geography, potentially between regions in that geography for operations | Steady, high-volume workloads that need reserved throughput | Reserved provisioned throughput | Capacity planning and commitment are required; geographic processing is not the same as a single physical region. |
| Batch | Global Batch or Data Zone Batch follows its documented scope | Asynchronous jobs that can wait for results | Batch pricing; Microsoft lists a 50% discount for supported Global Batch and Data Zone Batch deployments | The discount is a documented relative price condition, not a promise that your total bill will be half as high. Check model and region support. |
| Developer | Check the current model-specific definition | Development and experimentation | Developer pricing or limits as documented for the selected model | Do not assume development capacity or terms are suitable for production. |
| Managed compute | Model-specific compute placement | Open-source or custom-weight models where dedicated GPU hosting is required | Dedicated GPU capacity | The cited documentation describes this as public preview, without an SLA, and not recommended for production workloads. Re-check its status before use. |
Standard versus provisioned capacity
Standard, pay-per-token deployment is usually easier for bursty or uncertain demand. Provisioned capacity reserves throughput for a workload with predictable, sustained volume. Compare the reservation commitment with your measured demand, expected concurrency, latency target and tolerance for throttling before selecting it.
When batch processing is appropriate
Batch is useful when requests do not need an immediate response—for example, overnight classification or a large document-enrichment run. It can reduce the unit price where the supported discount applies, but it changes the application design: you need job submission, status tracking, retries, result storage and a way to report partial failures.
5. Treat data location as two separate decisions
Ask both where data is stored at rest and where inference is processed. A storage account, search index or database may be located in one geography while a global model deployment processes prompts and outputs in other Azure regions. Encryption and storage location do not, by themselves, establish the location of inference processing.
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Document the required boundary in plain language: a specific region, an Azure geography, a data zone or global processing. Then check whether the exact model, deployment type and features support that boundary. If a global deployment is not acceptable, do not select it merely because it offers more capacity.
6. A practical Foundry application pattern
Microsoft’s introductory chat architecture places a web interface on Azure App Service. The application calls an agent hosted in Foundry Agent Service. The agent can retrieve grounding information from Azure AI Search and call other connected tools before passing relevant context to an Azure OpenAI model. Identity and monitoring components are included in the pattern.
This is a useful way to understand the request path:
- A user authenticates to the web application.
- The application validates the request and invokes the Foundry agent.
- The agent decides whether it needs retrieved documents or a connected tool.
- Azure AI Search or another approved service supplies relevant context.
- The agent sends the bounded context and user request to the selected model deployment.
- The application applies response handling, logging and content controls before returning an answer.
Microsoft explicitly labels this basic, single-region architecture as a learning and proof-of-concept example, not a production design. For production, use Microsoft’s baseline Foundry chat architecture as a reference and adapt it to your identity, network, data-residency, availability and operating requirements.
7. A deployment workflow you can reuse
Step 1: Write the workload contract
Record the user task, supported languages and modalities, expected request rate, peak concurrency, response-time objective, maximum prompt and output sizes, grounding sources, retention rules and human-review points. Include unacceptable outcomes, escalation paths and who owns the service after launch.
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Step 2: Choose the service path
Select Foundry for a generative application or agent, Azure Machine Learning for custom or traditional ML and MLOps, or a standalone Azure OpenAI resource for a narrowly scoped completion API. If the workload may expand, document why the initial boundary is still appropriate.
Step 3: Confirm regional support
Before creating the resource, check the model catalog and current regional documentation for the model version, modality, deployment type, quota, safety features and networking options you need. Availability can differ by subscription, model and region.
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Step 4: Create the environment boundaries
Establish separate development, test and production resources or projects as required by your governance model. Connect only the storage, search, secret and monitoring services that each environment needs.
Step 5: Deploy a small evaluation path
Use representative prompts and documents, not only ideal examples. Measure answer quality, grounding accuracy, refusal behavior, latency, token consumption, failure rates and the effect of prompt or index changes. Record the model version and deployment type with each evaluation.
Step 6: Select capacity and traffic handling
Choose standard, provisioned or batch processing based on observed traffic rather than a guess. Add queues, retries with backoff, timeouts and idempotency for asynchronous or tool-using operations. Ensure retries cannot duplicate irreversible business actions.
Step 7: Promote through controlled release
Package application code, prompts, retrieval configuration, safety settings and model-deployment identifiers as a versioned release. For Azure Machine Learning managed online endpoints, Microsoft specifically identifies blue-green deployment as a useful release strategy.
Step 8: Operate and review
Monitor availability, latency, token use, quota pressure, retrieval failures, tool errors, safety events and user feedback. Set alerts with owners and runbooks. Revalidate the model, price, region and preview terms whenever you change the deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Build production reliability and recovery
Azure Machine Learning recovery planning
Microsoft states that Azure Machine Learning does not provide automatic failover. A recovery plan therefore has to cover the workspace and its dependencies, including Key Vault, Azure Storage and Azure Container Registry. Define how infrastructure, model artifacts, environment definitions, endpoint configuration and secrets will be recreated or restored.
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- Use availability zones or multiple regions where the service and workload justify the added complexity.
- Keep a tested deployment procedure instead of relying on an undocumented portal state.
- Back up model artifacts, configuration and critical data separately from the running endpoint.
- Define recovery-point and recovery-time objectives, then test them with a failure exercise.
- Plan for dependency outages: a search index, secret store, storage account or tool API can fail even when the model endpoint is healthy.
Generative application failure modes
Design explicit behavior for rate limits, unavailable models, empty retrieval results, malformed tool responses, unsafe requests and responses that exceed downstream limits. A useful fallback may be a delayed response, a human handoff or a restricted capability—not an unbounded retry loop.
9. Make responsible AI specific to the application
Responsible AI is an operating policy, not a switch that makes every use case safe. Microsoft advises teams to define how their organization applies responsible-AI principles at each lifecycle stage. Azure OpenAI guidance frames the work as identifying potential harms, measuring them, mitigating them and operating the system responsibly.
Questions to answer before launch
- What user data is collected, and what consent or notice is required?
- Can users opt out of model-assisted processing or human review?
- Which requests or outputs require a human decision-maker?
- How will users correct inaccurate information or request deletion under a right-to-be-forgotten process?
- Which content categories are disallowed, restricted or escalated?
- How will prompts, retrieved documents and outputs be protected in logs and evaluation datasets?
- How will you test disparate error rates, harmful instructions, prompt injection and data leakage?
Document the answers, assign owners and review them when the model, data source, user group or business purpose changes.
10. Estimate cost without relying on a generic price
There is no meaningful single Azure AI price for an unspecified workload. Estimate from the actual model, deployment scope, input and output tokens, request volume, concurrency, storage, search, compute, networking, monitoring and any reserved capacity. Use Microsoft’s current pricing pages or calculator with the configuration you intend to deploy.
Separate fixed and variable costs in the estimate:
- Model inference: token-based standard usage, provisioned throughput or batch pricing.
- Application hosting: the web app, API, containers or functions that handle requests.
- Grounding: search indexes, storage, ingestion and document-processing jobs.
- Operations: logs, metrics, tracing, alerting and retention.
- Resilience: duplicate environments, zones, regions and replicated dependencies.
- Development: experiments, evaluation runs and temporary GPU or compute capacity.
Recalculate after a pilot using measured token counts and peak traffic. A lower per-token rate can still produce a larger bill if prompts contain unnecessary context or if retries multiply requests.
11. Pre-provisioning verification checklist
- Have you classified the workload as an intelligent application, traditional ML, custom training, fine-tuning or a simple completion service?
- Is Foundry, Azure Machine Learning or standalone Azure OpenAI the smallest platform that satisfies the requirements?
- Is the exact model and deployment type available in the target region and subscription?
- Does the processing boundary—global, data zone or Azure geography—meet contractual and regulatory requirements?
- Have you distinguished inference processing location from storage-at-rest location?
- Did you estimate token, provisioned-capacity, batch, hosting, search, storage, monitoring and resilience costs?
- Are development and production resources, projects, identities and data separated?
- Are private networking, secret management, authentication and least-privilege access designed?
- Do monitoring and logs cover latency, failures, token use, safety events, retrieval quality and quota pressure?
- Do you have content-safety, human-review, consent, opt-out and deletion procedures?
- Can you restore the application and its dependencies, and have you tested the recovery path?
- Have you checked whether any managed-compute or model feature is still in preview and lacks an SLA?
12. What to recheck before publication or launch
Microsoft’s Azure AI product names, model catalog, quotas, regions, deployment scopes, prices and service-level terms are volatile. Recheck the official documentation on the day you provision the workload, especially for managed compute and any other preview capability. The architecture guidance cited here includes a pattern last updated May 7, 2026, while deployment pricing and availability can change independently.
Microsoft describes its Well-Architected reference as guidance for building secure, scalable conversational AI solutions on Azure with Foundry, Azure OpenAI and related services. Use that guidance as a design input, then make the workload-specific decisions—data boundary, identity, recovery, safety, capacity and operating ownership—that a reference architecture cannot make for you.
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