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Before moving an AI workload to Azure, establish exactly what is moving, where its data and inference may be processed, which Azure services and models are available in the target region, how identities and networks will be isolated, and how success will be measured. Then assess the current system with representative performance data, test the Azure design against functional, security, reliability, and cost baselines, and cut over only when the evidence meets agreed thresholds.
1. Define the workload and the migration outcome
An AI workload is more than a model endpoint. Its migration scope includes the application, model and prompt logic, data pipelines, indexes, identities, network paths, observability, and the people who operate it. Microsoft Learn describes Azure migration as involving “networking, identity, databases, compute, storage, and the custom integrations that your team built over the years.” Treat that list as a minimum inventory rather than a complete architecture.
Build a component inventory
- Application: user interfaces, APIs, orchestration code, background workers, batch jobs, containers, serverless functions, and configuration.
- AI services: model endpoints, model versions, fine-tuning jobs, embedding generation, reranking, speech or vision services, safety filters, tools, and agent frameworks.
- Data: training and fine-tuning sets, prompts, responses, documents, vector indexes, metadata, model artifacts, evaluation sets, backups, and temporary files.
- Dependencies: relational and NoSQL databases, object storage, search or vector services, queues, event buses, key-management systems, identity providers, SaaS APIs, and downstream applications.
- Operations: deployment pipelines, quota owners, on-call teams, dashboards, alerts, incident procedures, evaluation jobs, key rotation, and retention schedules.
For every item, record its owner, environment, data classification, location, authentication method, network path, recovery requirement, and dependency on a particular API or model version. Include interactive and batch traffic separately; they usually have different latency, capacity, and cost requirements.
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Choose the migration treatment
Decide whether each component will be rehosted, replatformed, refactored, retained, or retired. A rehost keeps most code and configuration while changing the hosting location. A replatform adopts a managed Azure service with limited application change. Refactoring changes the design to use Azure-native capabilities, while retaining leaves a dependency in place and retiring removes it. Document the reason for each choice, the code changes it requires, and how it affects supportability and operations.
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Write measurable success criteria
Set a baseline before changing production. Define acceptable functional behavior, throughput, typical and peak latency, availability, recovery time, recovery point, security controls, safety outcomes, and monthly cost. Capture representative prompts and expected outputs where deterministic tests are possible, and define tolerances for model variability where they are not. A migration is not complete because resources exist in Azure; it is complete when the new system meets these criteria under the traffic and data conditions it is expected to handle.
2. Produce a reliable Azure Migrate assessment
Azure Migrate assessments are useful planning inputs, not guarantees. Their recommendations and estimated costs depend on discovery coverage, collected performance data, assessment settings, current prices, and assumptions about region, uptime, licensing, discounts, and security.
Improve discovery quality before trusting results
- Discover every source workload, including dormant environments, scheduled jobs, integration hosts, and supporting databases.
- Resolve collection warnings and missing credentials rather than accepting partial inventory.
- For an appliance-based assessment, verify that the appliance is connected and continuously sending data.
- Allow the appliance to collect at least 24 hours of performance data for better assessment results, as recommended by Microsoft Learn in 2026. Longer collection gives a more representative view of peaks and idle periods.
- Apply Dev/Test and retain/retire tags consistently. Azure Migrate treats untagged workloads as production by default, so an incorrect tag changes readiness, sizing, and cost interpretation.
Read all assessment outputs
Review readiness findings, right-sizing recommendations, suggested migration tools, and estimated costs together. Check which observations came from measured utilization and which came from defaults. Record the assessment date, source configuration, performance window, target region, uptime schedule, licensing assumptions, discount assumptions, and security settings. If any of those inputs changes, rerun the assessment rather than comparing the new architecture with an old estimate.
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3. Set platform and environment boundaries
Choose where Foundry or Azure Machine Learning resources will live and which workloads may share them. An instance establishes network, identity, data-plane, and quota boundaries. Placing unrelated production systems together can enlarge an incident’s blast radius and make access, chargeback, and quota management harder.
Decide between shared and isolated instances
| Decision | Shared platform can fit when | Isolation is preferable when | Evidence to review |
|---|---|---|---|
| Environment and workload placement | Teams have compatible data classifications, access rules, release schedules, and quota needs. | Production must be separated from development or test, business units require independent administration, or a failure must not affect other workloads. | Regulatory boundaries, independent access control, quota and cost limits, incident blast radius, and added operational overhead. |
Make the boundary decision per environment and data domain, not only per subscription. Document who can administer the platform, who can manage projects, who can deploy models, and which workload identities can read data or invoke endpoints.
Apply least-privilege identity
Map administrators, project managers, project users, deployment pipelines, applications, and agents to Microsoft Entra ID groups and role-based permissions wherever possible. Separate human administration from workload identities, and grant each identity only the data and operations it needs. API keys can be used where role granularity is not required, but they demand clear ownership, rotation, storage, revocation, and leak-response procedures.
Choose a network model
Managed virtual networking can provide a simpler secure baseline. A bring-your-own virtual network offers more control over routing, inspection, segmentation, and integration with existing controls, but it also makes DNS and endpoint operations your responsibility. For either model, verify private DNS resolution, private endpoint approval, firewall rules, route tables, outbound dependencies, and the path to storage, search, identity, and monitoring services before production.
Confirm encryption and key ownership
Determine whether Microsoft-managed encryption satisfies policy. If customer-managed keys are required, identify the key vault, key lifecycle owner, rotation interval, access roles, backup strategy, and behavior when a key is disabled or unavailable. Include model artifacts, indexes, logs, backups, and temporary processing locations in the decision.
4. Verify region, model, and capacity together
Region selection is both a compliance decision and a performance decision. A model may be supported in one region or deployment type but unavailable in another, and the location where inference is processed may differ from the location of the resource.
Check availability at planning time
For every required model family, version, deployment type, API, and tool, confirm support in the target cloud and region. Recheck immediately before implementation because model availability, quota, capacity, and pricing are volatile service facts. Record the date and the evidence used for the decision.
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Global deployments may process inference in any Azure region. Data-zone deployments process within the applicable Microsoft-specified zone, while regional deployments constrain processing to the selected geography or region according to the deployment type. Do not infer data residency from the resource’s region alone. Verify where prompts, responses, files, embeddings, safety processing, and supporting telemetry can be handled, and obtain a compliance decision for each data class.
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Match capacity to traffic shape
Document acceptable latency variation, expected throughput, concurrency, token or request rates, and peak behavior. Interactive applications need predictable response time; batch and asynchronous jobs may trade latency for lower unit cost or different capacity options. Verify quota for steady state, deployments, fine-tuning, and failover, then test whether the service can actually allocate that quota in the chosen region. A quota increase is not the same as guaranteed physical capacity.
| Deployment choice | Questions to answer |
|---|---|
| Global | Can inference be processed in any Azure region, and does policy permit that variation? What latency range and capacity behavior are acceptable? |
| Data-zone | What Microsoft-defined zone applies, and do all data classes meet its processing boundary? |
| Regional | Does the selected geography provide the required model, quota, capacity, and failover options? |
| Standard, provisioned, or batch where offered | Which payment and capacity model matches the traffic pattern, and what happens during bursts or reservation exhaustion? |
5. Trace data, integrations, and telemetry
Map every copy of AI data from ingestion through processing, storage, indexing, inference, evaluation, monitoring, backup, and deletion. Include prompts, responses, embeddings, documents, model artifacts, red-team data, and diagnostic records. Confirm that retention periods, legal holds, backup copies, and exported logs obey the same geography and classification rules as the primary data.
Validate connectivity before application testing
- Resolve private and public DNS names from each execution environment.
- Test private endpoints, firewall policies, route tables, and egress controls.
- Verify access to object storage, databases, search or vector services, model endpoints, identity services, and downstream APIs.
- Measure connection setup, TLS validation, retry behavior, timeouts, and failure responses.
- Confirm whether outbound traffic crosses inspection points or regions and whether egress charges are included in the cost model.
Azure landing-zone planning must account for region, sovereignty, traffic patterns, and diagnostics routing. Centralized or duplicate logs can leave an approved region even when the primary workload does not, so inspect every diagnostic destination and export rule.
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Version models, prompts, safety policies, application code, schemas, indexes, and evaluation sets independently but release them as a traceable bundle. Define how to restore a prior model or index, how to replay or quarantine messages, and which owner can authorize rollback. Assign operational ownership for quotas, deployments, incidents, evaluations, and key rotation before cutover.
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6. Preserve responsible-AI and agent controls
Migration must preserve controls, not just functionality. Document policies for the intended use, prohibited use, sensitive data, user transparency, consent, human review, and escalation. Include evaluation and monitoring that reflect real users, languages, data distributions, and failure costs.
Monitor inputs and outputs
Where risk warrants, inspect incoming data for prompt injection, sensitive information, malicious files, and policy violations. Inspect outgoing responses for unsafe content, data leakage, unsupported claims, and tool misuse. Establish alert thresholds, retention limits, access controls for review data, and a process for updating evaluations when the model or application changes.
Secure agents and tools
An agent that reads private or public data, executes tasks, or influences decisions needs activity auditability and role-based access control. Restrict tools and data sources by identity and task, require approvals for consequential actions, and implement a circuit breaker that can stop execution when behavior, spend, or call volume exceeds a defined limit.
7. Test the target before cutover
Deploy the target architecture in a non-production environment and replay representative data and traffic. Include normal, peak, degraded, and dependency-failure conditions; a successful quiet-period test does not establish production readiness.
Run four baseline comparisons
- Functional: required workflows complete, integrations return the expected schema, retrieval finds the right records, and model outputs meet agreed quality tolerances.
- Performance: measure p50, p95, and worst-case latency, throughput, concurrency, queue depth, timeouts, and cold-start behavior under typical and peak load.
- Security and safety: verify identity boundaries, network isolation, secret handling, encryption, logging access, data-loss protections, abuse controls, and agent circuit breakers.
- Cost: compare compute, model calls, storage, search, networking, monitoring, backup, support, and idle capacity with the pre-migration cost envelope.
Also test observability, backup restoration, regional or service failure handling, deployment rollback, quota exhaustion, and key revocation. Record results with the test configuration so another team can reproduce them.
8. Cut over in stages and retire safely
- Freeze the versions and data assumptions used for the final validation.
- Deploy production resources with monitoring, alerts, access reviews, quotas, and rollback artifacts already in place.
- Send a small, controlled percentage of traffic to Azure or run parallel processing where duplicate execution is safe.
- Compare live functional, latency, error, safety, and cost signals with the baseline.
- Increase traffic only when the agreed thresholds hold for the required observation period.
- Keep the source system available for rollback until data synchronization, retention, and recovery obligations are satisfied.
- Decommission source resources only after the Azure workload meets success criteria and an owner has approved the retirement record.
Use a written go/no-go checklist with named approvers. Include a rollback trigger for security incidents, unacceptable model behavior, quota failure, data-residency breach, sustained latency regression, or cost outside the approved envelope.
Quick Recap
Decision worksheet for architecture review
Bring these answers to the review meeting:
- What components, data stores, identities, integrations, and operators are in scope?
- Which components are rehosted, replatformed, refactored, retained, or retired, and why?
- What are the measured functional, latency, throughput, availability, security, safety, recovery, and cost baselines?
- Which workloads share a platform instance, and what is the documented blast-radius decision?
- Which identities can administer, deploy, read data, invoke models, or approve agent actions?
- Which network model, private DNS, endpoints, routes, firewall rules, and egress controls are required?
- Where may each data class be stored, processed, logged, backed up, and reviewed?
- Which model and deployment type are available in the target region, with what quota, capacity, latency, and payment model?
- How will prompts, models, indexes, code, and data changes be versioned and rolled back?
- What evidence authorizes staged cutover, and what exact condition triggers rollback or source decommissioning?
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