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AWS Transform
The first agentic AI service to accelerate the transformation of Windows, mainframe, VMware, and more—reducing technical debt and making your tech stack AI-ready
Eliminate tech debt with agentic AI to modernize legacy systems and code
AWS Transform is the first agentic AI service developed to accelerate enterprise modernization of full-stack Windows, mainframe, VMware workloads, as well as custom transformations of code, APIs, frameworks and more. Built on 19 years of migration and modernization experience, it deploys specialized AI agents to automate complex tasks like assessments, code analysis, refactoring, decomposition, dependency mapping, validation, and transformation planning, dramatically reducing project timelines. By executing many tasks in parallel, AWS Transform helps organizations to simultaneously modernize hundreds of applications while maintaining quality and control. Its natural language chat experience and shared workspaces allow cross-organizational and cross-functional teams to collaborate in real time, track progress, and manage transformations from start to finish. The service helps reduce both modernization costs and ongoing maintenance expenses while identifying opportunities to eliminate legacy licensing costs.
Benefits
Modernize Windows, mainframe, and VMware applications up to 5x faster with agentic AI–powered automation of analysis, planning, documentation, and transformation tasks.
Scale modernization initiatives, transforming hundreds of applications in parallel. AWS Transform automates high-effort, repeatable tasks, so teams can deliver larger, more complex projects faster and with ease.
Reduce legacy infrastructure, licensing, and modernization costs on your path to the cloud. AWS Transform helps remove costly overhead and streamlines modernization with agentic AI.
Achieve consistent, reliable modernization using AI agents built on 19 years of AWS migration and modernization experience and deep knowledge across Windows, mainframe, VMware workloads, as well as custom transformations for code, APIs, frameworks, etc.
Impact by the numbers
5x
Up to 5x faster full-stack Windows modernization including .NET and SQL Server.80x
faster conversion of VMware network configurations to AWS.1.1B
lines of mainframe code analyzed by customers.810,000
hours of manual effort—equivalent of 388 developer years of work saved.Use cases
AWS Transform accelerates full-stack Windows modernization by up to 5x across application, UI framework, database, and deployment layers. Using the specialized Windows modernization agent, teams can accelerate modernization of .NET applications, SQL Server databases, and deployment processes, reducing operating costs by up to 70% by moving away from costly licenses.
AWS Transform accelerates mainframe application modernization from years to months. The specialized mainframe agent streamlines the entire mainframe workload transformation process—from initial analysis and planning to code refactoring and application reimagining—accelerating modernization timelines and reducing risk and costs.
AWS Transform for VMware helps you save on VMware licensing costs and drive innovation by modernizing your workloads to Amazon EC2. The VMware agent automates application discovery and dependency mapping, network translation, wave planning, while optimizing Amazon EC2 instance selection to accelerate VMware workload modernization.
AWS Transform accelerates organization-wide modernization through agentic AI that automates custom code transformations. It provides out-of-the-box transformations for common scenarios like Java, Node.js and Python upgrades. The agent also performs custom, organization-specific transformations like version upgrades, runtime migrations, or complex language translations and architectural changes. Through continual learning from code samples, documentation, and developer feedback, the agent delivers high-quality, repeatable transformations without requiring specialized automation expertise, enabling organizations to scale their modernization initiatives effectively.
