Category: AWS Transform

Enterprises need their core systems to support business transformation and AI. When those systems are undocumented mainframe monoliths, they become the bottleneck. Reverse engineering a mainframe application, capturing its business intent, and translating that into modern code could take months or years for even a small set of business functionality. Many organizations settled for migration […]

Introduction Consider a team with over 400 servers to migrate, dozens of applications with varying dependencies, and multiple teams waiting on cutover windows. Every migration is unique, yet the challenges remain consistent. Teams struggle with manual tracking through spreadsheets, human errors in repetitive tasks like agent installation and DNS updates, decentralized orchestration across disconnected consoles, […]

When enterprises embark on their cloud migration journey, they bring years of operational maturity from running their workloads on-premises: separate environments for development, testing, and production, often segmented by business unit, with dedicated infrastructure, network boundaries, and identity controls for each. Translating these well-established patterns into cloud-native constructs like account structures, organizational units (OUs), and […]

Introduction Migration assessment and planning is rarely a single-pass exercise. Inventory data is incomplete; assumptions shift as more stakeholders get involved, and new scenarios emerge as the strategy evolves. Traditional assessment workflows struggle with this reality because they treat each assessment as a one-shot report: collect data, run analysis, deliver a static business case, and […]

The term ‘mainframe modernization’ is itself out of date. Modernization used to mean upgrading the technology. AI has made it about reimagining the business. Introduction The term “mainframe modernization” has been associated with infrastructure and technology modernization projects rather than business transformation. This covers a wide range of projects with very different objectives: application and […]

Today, we’re announcing the model-to-model migration assessment custom transformation for AWS Transform, an AI agent that assesses your generative AI applications and produces a comprehensive migration plan for moving from other GenAI providers to Amazon Bedrock. Enterprises that built generative AI on OpenAI, Google Gemini, or direct Anthropic APIs find that scaling to production demands more than […]

Traditional VMware migrations have long suffered from fragmentation and inflexibility. Enterprises had to cobble together disconnected tools for discovery, planning, network configuration, and rehosting, each requiring specialized expertise and lengthy manually orchestrated handoffs. These rigid, rules-based tools can’t adapt to each customer’s unique environment, resulting in compounding costs, expensive customizations, and timelines that stretch into […]

Introduction AWS Transform Custom (ATX) automates code modernization at scale. What makes it different from running an AI coding assistant on each repo is that it learns. Each execution captures patterns, fixes, and edge cases as reusable knowledge, so transformations get faster and more reliable with every run. If you’ve got hundreds of Java 8 […]

A year ago, we launched AWS Transform with one mission: modernize the world’s infrastructure and applications. 12 months in, here’s what 4.5+ billion lines of code processed, hundreds of thousands of servers migrated, and saving customers 1.6+ million hours taught us about migration, modernization, and why this work cannot wait. Why this matters more than […]

Migrating hundreds of servers from an on-premises VMware environment to AWS involves coordinating across teams, accounts, and tools. You need to set up your target environments, plan waves, deploy replication agents, monitor their progress, run tests, and perform cutover, often across multiple AWS accounts. Each step can be challenging on its own, requiring careful planning […]