Azure Migrate assessments provide a centralized way to evaluate on-premises servers, data, and applications for migration to Azure. The assessment process begins with discovery, where a lightweight Azure Migrate appliance inventories servers, databases, and applications without installing agents on every machine. This inventory captures performance metrics such as CPU, memory, storage IOPS, and network utilization over time. It also maps application dependencies to reveal how servers and services interact with each other. These initial steps establish the performance baselines and topology required for accurate migration planning.
After discovery, Azure Migrate performs a readiness evaluation to determine whether workloads can migrate to Azure services. The assessment checks for compatibility issues with operating systems, database versions, storage sizes, and other configuration settings. Each workload is categorized as Ready for Azure, Conditionally ready, Not ready, or Unknown, which guides remediation efforts before migration begins. Assessment settings like target region, storage type, and compute families influence the readiness outcomes, ensuring that every workload is validated against suitable Azure targets.
The assessment then generates right-sized recommendations and cost estimates for target Azure resources. Performance-based sizing uses collected utilization data and percentile analysis, such as the 95th percentile, multiplied by a comfort factor to recommend optimal VM sizes, disk types, or database SKUs. As-is on-premises sizing bases recommendations solely on existing server configurations without considering actual usage patterns. Finally, the tool calculates monthly costs by aggregating compute, storage, licensing, and ancillary service charges based on selected pricing settings. This end-to-end cost estimation helps determine whether to rehost, refactor, or rearchitect workloads.
To help assess recommendation reliability, Azure Migrate assigns confidence ratings to performance-based assessments. Ratings from one to five stars reflect the availability of required data points, such as CPU, RAM, IOPS, and network I/O metrics. Low confidence prompts administrators to extend profiling duration or switch to on-premises sizing, while high confidence confirms accurate recommendations. The assessment results, including readiness status, right-sizing, cost details, and migration guidance, are packaged into reports for stakeholders. Based on these insights, migration teams can choose the optimal approach and timeline for moving workloads to Azure.
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Azure Migrate uses a lightweight appliance to inventory servers, databases, and applications without installing agents on every machine, capturing performance metrics and mapping application dependencies.
Azure Migrate categorizes each workload as Ready for Azure, Conditionally ready, Not ready, or Unknown, which guides remediation efforts before migration begins.
Performance-based sizing uses collected utilization data and percentile analysis to recommend optimal Azure resource sizes, while as-is sizing bases recommendations solely on existing on-premises server configurations without considering usage patterns.
The confidence rating, from one to five stars, reflects the availability of required performance data points like CPU and memory metrics, where low confidence may prompt extending the profiling duration.
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