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Before moving an AI workload to Azure, prove six things: you know every component and dependency, the target platform has the right identity and network boundaries, the required models and capacity exist in an acceptable region, data and telemetry meet jurisdiction and retention rules, safety and operations are preserved, and the migrated system matches agreed functional, performance, security, and cost baselines. Azure Migrate can inform the decision, but its output is a point-in-time estimate—not a guarantee of fit, price, or performance.
1. Define exactly what is moving
Start with an inventory, not a subscription or virtual-machine estimate. Microsoft Learn describes Azure migration as covering “networking, identity, databases, compute, storage, and the custom integrations that your team built over the years.” An AI workload adds model endpoints, prompts, embeddings, indexes, evaluation data, safety filters, and often several asynchronous jobs.
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Build a component and dependency register
- Record the source platform, application services, model-serving method, model versions, prompt or policy files, and any fine-tuning or batch pipelines.
- List operational and analytical data stores, vector or search indexes, embedding models, model artifacts, backups, caches, and temporary files.
- Map identity dependencies: users, service principals or workload identities, secrets, certificates, key stores, administrators, operators, and automation accounts.
- Draw network flows to storage, search, model endpoints, identity services, monitoring, notification systems, and downstream applications. Include ingress, egress, private links, DNS, firewalls, proxies, and failure paths.
- Classify traffic as interactive, streaming, scheduled batch, evaluation, or training. Capture normal, peak, and burst request rates.
Set measurable success criteria
Write the baseline before changing the source system. Define expected outputs and tolerances for representative prompts, throughput, first-token and full-response latency, availability, recovery time, security controls, safety behavior, and monthly cost. Include data-retention and deletion requirements. A workload is not ready merely because its code deploys; it is ready when the Azure version meets these criteria under representative conditions.
Choose the migration path
For each component, choose rehost, replatform, refactor, retain, or retire. A rehost preserves more behavior but may carry unsuitable networking or scaling assumptions. Replatforming can move to a managed Azure service with limited code changes. Refactoring may be justified when the existing design cannot meet identity, isolation, latency, or cost goals. Mark retained and retired dependencies explicitly so they do not disappear from the risk register.
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Microsoft’s migration journey is plan, prepare, execute, evaluate, and decommission. Its single-workload guidance does not by itself cover an entire datacenter move, a region relocation, or simultaneous multi-cloud operation; those scenarios need a separate program plan.
2. Make Azure Migrate results trustworthy
Azure Migrate assessments are useful inputs only when discovery and assumptions are sound. They estimate readiness, right-sizing, migration tooling, and cost at a point in time. Changes to source configuration, collected telemetry, assessment settings, Azure prices, licensing, discounts, uptime, or target region can change the result.
Prepare discovery and collection
- Discover the complete inventory, including low-traffic services, dormant development systems, scheduled jobs, shared databases, and integration endpoints.
- Resolve collection warnings before creating an assessment. An unknown dependency or missing performance counter should be treated as an open issue, not as zero utilization.
- For an appliance-based assessment, verify that the appliance is connected and continuously sending data. Microsoft recommends allowing at least 24 hours of performance collection for better assessment results.
- Tag workloads accurately. Azure Migrate treats untagged workloads as production by default; apply its Dev/Test and retain/retire classifications consistently.
- Record the assessment’s region, uptime, licensing, discount, security, and utilization assumptions alongside the result.
Interpret the output as a planning snapshot
Review readiness warnings, recommended sizes, estimated monthly costs, and migration-tool suggestions against your own baseline. Re-run the assessment after correcting discovery gaps or changing assumptions. Do not present an estimated cost as a quote, and do not infer an availability or latency guarantee from a right-sizing recommendation.
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An Azure AI platform instance—such as an Azure AI Foundry or Azure Machine Learning instance—creates boundaries for network access, identity, data plane, and quota. Deciding which workloads share that instance is an architecture decision, not an administrative convenience.
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Decide what may share an instance
Compare shared and isolated designs using business ownership, data classification, regulatory separation, independent access control, quota and cost accountability, incident blast radius, and operational overhead. Development and test may share selected resources when data and access are controlled; production, regulated data domains, or unrelated business units may require separate instances, subscriptions, or virtual networks. Document the exception whenever environments are colocated.
| Choice | Advantages | Risks and checks |
|---|---|---|
| Shared platform instance | Lower administrative overhead and easier reuse of connections, policies, and tooling. | Quota contention, broader administrator access, shared failure domain, and harder cost attribution. Verify tenant, project, data, and network boundaries. |
| Separate instances | Clearer ownership, independent quotas, tighter access and incident boundaries, and simpler production change control. | More infrastructure and duplicated operations. Confirm that shared services, monitoring, and model artifacts can be accessed without bypassing isolation. |
Map roles and workload identities
List administrators, project managers, project users, deployment operators, evaluators, and runtime identities. Prefer Microsoft Entra ID and role-based access where the required granularity exists. Use API keys only when role granularity is not required and protect, rotate, scope, and audit them. Separate human administration from application runtime permissions; a model-serving identity should not be able to alter networking or delete production data.
4. Choose network, encryption, and landing-zone controls
Select the virtual-network model
A managed virtual network can simplify a secure baseline. A bring-your-own virtual network provides advanced routing and policy control when the workload must integrate with existing inspection, segmentation, or on-premises connectivity. Decide per environment, then test the complete path to storage, search, model endpoints, identity, monitoring, and downstream systems.
- Approve private endpoints and verify private DNS resolution from every required subnet.
- Confirm firewall rules, route tables, proxy behavior, and egress destinations, including software-update and telemetry paths.
- Test denied traffic and failover behavior, not just the successful connection.
- Document which connections are public, private, or forced through inspection.
Resolve landing-zone questions early
The platform team should settle subscription layout, management groups, policy assignments, logging destinations, region and sovereignty requirements, traffic patterns, and diagnostics routing before application deployment. Centralized or duplicate logs can leave an approved geography if routing is not designed deliberately.
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Choose the key-management model
Determine whether Microsoft-managed encryption satisfies the data classification and audit requirements. If customer-managed keys are required, plan the key vault, key rotation, recovery, access roles, monitoring, and outage behavior before migration. Test what happens when a key is unavailable or rotated.
5. Verify region, model, deployment type, and capacity
Region selection is coupled to compliance and performance. Check each required model family, API, tool, and feature in the intended Azure cloud and region at planning time, then recheck immediately before implementation because availability, quotas, capacity, and prices change.
Document processing geography
For every model deployment, record the permitted inference-processing geography, data-classification rule, acceptable latency variation, and whether interactive or batch traffic is involved. A global deployment type may process inference in any Azure region. Data-zone types process within the applicable Microsoft-specified zone, while regional types constrain processing to the selected geography or region according to that deployment type. Do not assume that storing data in one region forces every inference operation to run there.
Compare deployment choices
| Decision | Compare | Evidence to collect |
|---|---|---|
| Processing scope | Global, data-zone, or regional deployment | Documented inference geography, regulatory approval, cross-border restrictions, and expected latency variation. |
| Capacity model | Standard, provisioned, or batch where offered | Steady-state and peak throughput, quota, reservation or payment model, queueing behavior, and workload pattern. |
| Model and feature fit | Required model family, API version, tools, and safety features | Availability in the target region, version compatibility, context limits, tool support, and migration changes. |
Reserve headroom for peak traffic, retries, evaluations, and failover. Validate quota increases and actual capacity before committing to a cutover date; an enabled model listing does not prove that the required capacity is immediately deployable.
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6. Trace data, integrations, and day-two operations
Follow every copy of the data
Trace training and fine-tuning data, prompts, responses, embeddings, indexes, model artifacts, logs, evaluation sets, backups, and temporary exports through storage, processing, monitoring, and recovery. For each copy, record geography, retention, encryption, access owner, deletion method, and whether it contains personal, confidential, or regulated information. Check telemetry as carefully as primary data: a prompt captured in a diagnostic log is still data subject to the same policy.
Prove connectivity and failure handling
Run tests from the actual workload subnets to storage, search or vector services, model endpoints, identity, and downstream applications. Verify DNS, private endpoint approval, firewall policy, timeout values, retry budgets, idempotency, circuit breaking, and behavior when a dependency is unavailable. Measure egress volume and cost assumptions rather than treating all traffic as internal.
Plan versioning and ownership
- Version application code, model deployments, prompts, safety policies, indexes, embedding models, and evaluation data independently.
- Define rollback triggers and the sequence for reverting code, model, prompt, and index changes.
- Assign owners for quotas, deployments, incidents, evaluations, key rotation, data retention, and access reviews.
- Specify diagnostic retention, alert thresholds, escalation paths, and who can approve production changes.
7. Preserve responsible-AI and agent controls
Migration must preserve the controls that make the workload acceptable, not only the controls that make it reachable. Document the intended use, prohibited use, user transparency, consent requirements, and workload-specific safety policies. Re-run evaluations after changing model versions, prompts, filters, retrieval sources, or system instructions.
Monitor both incoming and outgoing data when the risk warrants it. For agents that read private or public data, execute tasks, or influence decisions, require activity auditability, role-based access control, and a circuit breaker that can stop actions quickly. Confirm that human review, escalation, and evidence retention still work after the network and identity changes.
8. Test before cutover
Deploy the target architecture in a non-production environment with representative data, model versions, traffic shape, and failure conditions. Compare it with the pre-migration baseline in four dimensions: functional behavior, performance, security and safety, and total run cost.
- Run golden test cases and compare classifications, generated text, tool calls, citations or retrieved records, refusal behavior, and error handling.
- Load-test typical and peak traffic. Record throughput, queue time, first-token latency, full-response latency, throttling, retries, and recovery after dependency or zone failure.
- Verify least-privilege access, secret and key rotation, private connectivity, audit logs, data residency, retention, deletion, and incident alerts.
- Measure model, compute, storage, networking, logging, backup, and support costs using the same traffic assumptions as the baseline.
- Exercise rollback with a known-good model, prompt, application version, and index. Confirm that queued work and duplicate requests are handled safely.
Use a staged cutover
Shift traffic gradually, using a canary or parallel run where the application allows it. Define automatic stop conditions for error rate, latency, safety regressions, quota exhaustion, data-residency violations, or cost spikes. Keep the source system available until the Azure workload satisfies the agreed criteria and the rollback window has expired.
9. Decommission only after evidence is complete
Evaluation comes before decommissioning. Retain source resources while you complete reconciliation, legal holds, backup validation, data-retention obligations, and access revocation. Then remove unused credentials, endpoints, snapshots, indexes, and monitoring rules—not just the compute layer—and capture the final architecture, owners, quotas, and operating procedures.
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Readiness decision sheet
| Area | Go decision requires |
|---|---|
| Scope | Complete component, data, identity, network, integration, operator, and dependency inventory with measurable success criteria. |
| Assessment | Discovery warnings cleared, appliance connected, at least 24 hours of performance data collected where applicable, and assumptions recorded. |
| Platform | Explicit shared-versus-isolated decision, role mapping, quota ownership, network model, DNS, endpoint approvals, and encryption choice. |
| Region and model | Required model and deployment type available, processing geography approved, quota and capacity confirmed, and latency variation acceptable. |
| Data and operations | All copies and telemetry classified, connectivity tested, versioning and rollback defined, and operational owners assigned. |
| Validation | Functional, performance, security, safety, observability, recovery, and cost results meet the baseline under representative load. |
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