Organizations facing increasing VMware licensing costs are seeking efficient ways to modernize their VMware workload strategy while managing limited resources and expertise.
AWS Transform for VMware, now in general availability, is a new service that uses agentic AI to guide customers through the entire migration journey. This service helps organizations streamline and accelerate migrations from VMware environments to native AWS services. It provides AI-assisted automation throughout the migration lifecycle, significantly reducing the complexity and effort typically associated with cloud migrations.
IBM Consulting® has been supporting an extensive VMware Cloud on AWS estate for a major UK public sector department. As this department has a strategic direction of cloud native, the logical first step is to rehost applications from VMware Cloud on AWS onto Amazon EC2. This approach has minimized their risk of exposure to traditional licensing practices, including lock-in and severe price increases.
In collaboration with AWS, IBM Consulting implemented VMCloud on AWS to systematically migrate legacy Windows applications to Amazon EC2. Through this process, they successfully used AWS Transform to reduce migration effort by 60% for automated tasks while maintaining full application functionality and security. The following approach—from choosing a target application through the migration phases—provides a replicable framework that other organizations can adopt for their own cloud native transformation initiatives.
The IBM Consulting team targeted a legacy Windows 2008 virtual machine (VM) in the customer’s VMware Cloud on AWS development environment. The application runs in its own VM and uses an Amazon RDS SQL Server instance in a customer account. This application presented an ideal test case for two reasons:
Figure 1 presented here shows the high-level architecture of the application in the development environment. In this original state, there is a Windows Server 2008 VM running in the VMware Cloud on AWS that connects to an Amazon RDS SQL Server in the customer’s AWS account
After enabling AWS Transform in AWS Organizations and accessing the web application URL, a workspace was selected to run the transformation job to migrate a VMware application to Amazon EC2.
As shown in Figure 2, there are four supported types of VMware migration jobs:
Because no network migration was necessary by the application in scope, option 4—discovery and server migration—was selected. This option includes discovery mapping, wave planning and migrations of servers.
The final migration plan must include the target AWS network infrastructure details (VPC, subnets and security groups). Users can use the natural language chat feature within AWS Transform when they need to update any information, such as modifying job names.
AWS Transform automates discovery by integrating with AWS Application Discovery Service to collect detailed information about server configurations, performance metrics and network connections. This data forms the foundation for accurate migration planning and sizing recommendations.
For this migration project, AWS Transform’s automated inventory and dependency-mapping capability was crucial in understanding the existing infrastructure. The tool’s autonomous agent is designed to automatically discover VMware workloads, map application dependencies and analyze network configurations with minimal manual intervention.
After creating the initial migration job, the next critical step was to establish a connection between AWS Transform and the discovery account. This dedicated AWS account would store and host the discovered infrastructure data, enabling AWS Transform to generate precise migration recommendations. The IBM Consulting team facilitated this connection by creating a connector request with specific permissions, authorizing AWS Transform to read, write and manage S3 buckets and AWS Application Discovery Service (ADS) configurations.
With only one application in scope for the proof of technology, the AWS Discovery Agent was installed on the VM hosting the application. This agent collects information about server configurations, performance and network connections. The server and network details are displayed in the AWS Migration Hub within the discovery account as data is collected.
Figure 3 shows the AWS Migration Hub console displaying the discovered Windows 2008 server with its system specifications and network connections clearly visible. The discovery agent collected detailed information about CPU, memory, disk usage and network communications to help AWS Transform generate accurate migration recommendations.
AWS Transform’s planning capabilities include automated analysis of discovery data to create logical application groupings and migration waves. The service provides AI-driven recommendations for target infrastructure and migration sequencing, which migration teams can review and adjust before implementation.
After running the discovery job for seven days, the IBM Consulting team initiated the perform discovery step in the AWS Transform job to review the data. In larger migrations, AWS Transform automatically suggests the application groupings and Waves in a CSV file, which migration teams can review and adjust. Once confirmed, AWS Transform automatically created the application groupings and Waves in the AWS Application Migration Service (MGN). This setup was based on the information provided in the CSV file.
Following discovery, the IBM Consulting team connected AWS Transform to the target AWS account where the migrated servers would be deployed. This connection required a default VPC in the target AWS region. Since the team needed to target a specific VPC, they manually created the default VPC and modified the automatically created service-linked roles to ensure proper permissions for the targeted VPC ID.
Working through AWS Transform’s guided workflow, the IBM Consulting team set Amazon EC2 instance recommendations, including sizing preferences, instance type preferences and exclusions for certain instance types. AWS Transform generated an inventory file for the migration wave, including EC2 instance type specifications and tenancy details. The team downloaded this file, updated it to reference the specific VPC, subnet and security groups for the new instance (all tagged with CreatedFor:AWSTransform) and reuploaded it to AWS Transform.
AWS Transform simplifies the replication process by orchestrating AWS Application Migration Service (MGN) to perform block-level replication from source servers to AWS.
To carry out the migration, a replication agent needs to be installed on each source server. AWS Transform can automate this process or users can install it manually. Once the agent is connected to the AWS MGN service, an initial synchronization of the source VM takes place.
After completion, the source server showed as ready for testing with healthy data replication status, as displayed in Figure 4, indicating successful block-level synchronization from the source VM to AWS.
At this stage, AWS Transform launched a test instance based on the latest inventory file. This test instance appears in the AWS EC2 console, allowing the team to perform acceptance testing while the source environment continues operating normally. The IBM Consulting team’s test validation confirmed:
After acceptance testing was completed, the application was marked as ready for cutover in AWS Transform and the test instance was automatically stopped. AWS Transform was then used to launch the cutover instance, ensuring that the original test instance was properly shut down.
After the cutover instance was launched, the IBM Consulting team performed verification checks confirming that:
After successful verification, AWS Transform finalized the cutover. This process stopped the replication from the source server, uninstalled the agents and automatically moved the source server from active to archived status in the AWS MGN service. The AWS Transform job is now marked as complete.
Figure 5 displays the AWS Transform job completion screen. It confirms that the migration was successfully completed. The status indicators show that all phases from discovery through cutover have been completed and the source server has been archived in the AWS MGN service.
Figure 6 illustrates the final architecture after migration completion. The Windows application now runs on an Amazon EC2 instance directly in the customer’s AWS account, connected to the existing Amazon RDS SQL server database.
AWS Transform’s intuitive design enabled the IBM Consulting team to execute this migration efficiently without requiring deep specialization in every underlying AWS service. The team was able to focus on the migration workflow and business requirements. AWS Transform provided benefits like a central interface to launch a test instance, track the migration status and perform a cutover—demonstrating how the service optimizes team productivity and resource allocation.
Where manual activity was required, AWS Transform provided step-by-step guidance and links to the relevant documentation. The IBM Consulting team worked closely with the customer’s AWS Solution Architect and the AWS Transform service team to unblock any issues encountered and get them resolved in a timely manner.
Another valuable feature is its built-in collaboration capability that allows multiple team members to participate in the migration project. Each collaborator must be assigned into one of four roles:
This role-based access model enables engineers to oversee the migration process. It also allows subject matter experts to collaborate at specific touchpoints—such as evaluating the wave plans and application groupings. They can also confirm the target Amazon EC2 instance configuration or marking an application as ready for cutover after completing acceptance tests. All collaboration occurs within a unified platform, eliminating lengthy handoffs or the need to create multiple AWS accounts.
AWS Transform reduces risk in the migration process through several key mechanisms. The block-level replication from the source servers data to AWS in near-real time is non-intrusive. Source servers continue to be fully functional throughout the replication process.
If launching the test instance fails or issues are found during acceptance test on the new instance, customers can continue to use the source environment uninterrupted. Shutting down and decommissioning the source server remains under customer control as part of the post-migration activities.
The successful migration of a VMware application onto Amazon EC2 through AWS Transform gave both the IBM Consulting team and the customer confidence to expand the use of AWS Transform.
AWS Transform for VMware improved the application discovery and application migration efficiency tasks by 60% for this migration. Further gains are anticipated when applying this methodology to larger-scale migrations, where the service’s analysis of discovery data to create application groupings and wave plans would eliminate manual effort. AWS Transform simplifies and accelerates the customer’s modernization journey, eliminating the need for in-house deep expertise development, reducing time to value and enhancing team productivity.
If you’re considering AWS Transform for your VMware migration, here are three steps to begin your journey:
Read AWS Transform documentation
Learn about AWS Application Discovery Service