Money talks, and right now it is telling us something important about old software. McKinsey's research shows that 70% of the software Fortune 500 companies use is at least 20 years old. These systems that helped companies grow in the past are now holding them back. They slow down innovation, eat up budgets, and leave organizations vulnerable to faster competitors.
Even when these systems still work fine. But many companies still wonder if upgrading is worth the cost and effort. To answer that question, we need to look at real examples of legacy modernization, where companies deeply reinvented their systems and measured the results.
Application modernization business impact
When companies update their old systems, the benefits go way beyond just technical improvements. They see real business value through better operations and stronger market positions. These improvements come from systems that work more reliably and development processes that move faster.
This means companies can launch new features and services much quicker than before. The financial side gets even more interesting when you look at return on investment. Research consistently shows that companies that successfully modernize their systems cut their IT operating costs by 40%. At the same time, they boost productivity by about the same amount.
Think about what this means – you are spending less money while getting more done. This combination frees up resources that companies can then invest in new ideas and growth opportunities.
1. Jira application modernization example
The name "Jira" is a truncation of "Gojira," the Japanese name for Godzilla. It originated as a nickname Atlassian developers used for their previous internal bug tracker, Bugzilla, which they saw as a large, cumbersome "monster" of a tool.
Atlassian's transformation of Jira from a single, monolithic Java application to a flexible cloud-based system made up of smaller, connected services. This change stands out as one of the most complete application modernization examples in business software today.
Legacy system assessment and modernization strategy
The old Jira system ran as a single-tenant application that companies installed on their own servers. It was built as one big piece of software using Java, along with tools like the Pico inversion of control container, Apache OFBiz entity engine, and WebWork 1 framework.
Originally launched in 2002 as a self-contained bug tracker, Jira's architecture served its initial purpose effectively, enabling rapid development and straightforward deployment. However, this early strength turned into a major problem as millions of users started using Jira for all kinds of business needs, not just tracking software bugs.
The code was so tightly connected that making even small changes became risky. Rolling out updates took forever. Users really felt the pain when they tried to work with boards showing 500 or more issues - the screens would load slowly, and every click felt sluggish, which killed their productivity.
Implementation approach and technology choices
Atlassian's modernization strategy centered on re-architecting Jira for a multi-tenant, stateless cloud environment hosted on Amazon Web Services. The implementation followed a systematic approach, prioritizing the development of custom management tools before large-scale customer migration.
Microscope, an internal service catalog, tracked every microservice, its dependencies, and operational status, providing essential visibility for the distributed architecture. ServiceQuest automated quality gates, performing security, privacy, and reliability checks before production deployment.
The new architecture utilized containerization technologies like Docker and orchestration platforms like Kubernetes, with RESTful APIs standardizing communication between microservices to ensure loose coupling and maintainability.
Before and after application comparison
The legacy system supported primarily software development teams with perpetual server licenses and maintenance fees.
Post-modernization, Jira functions as a distributed, cloud-native platform serving all business teams through tiered subscription models. The new architecture enables independent service deployments, advanced automation capabilities, AI-powered features through the Rovo platform, and enterprise-grade security compliance.
Atlassian now offers 99.9% uptime SLAs, a guarantee made possible by the fault-isolated microservices design that prevents single points of failure from affecting the entire platform.
2. Duolingo modernization success story
Duolingo’s application modernization review starts with a move from a Python monolith to a high-performance microservices setup on AWS and shows how a planned rebuild can support very large scale while cutting costs and speeding AI-based product work. The shift removed core performance bottlenecks that threatened user retention and growth in a crowded education technology market.
COVID story and scalability challenges
The COVID-19 pandemic created an unexpected demand for Duolingo's services, with new registrations up 101% in March 2020 alone. That wave exposed limits in the Python-based Session Generator, where latency directly hurt the learning experience. The monolithic system needed about 750 milliseconds to build a lesson —too slow for an app designed around game-like flow and instant feedback. With next-day retention at only 12% in the platform’s early days, improving speed became a must to turn casual users into daily learners and, later, paid subscribers.
Cost control approach and results
Duolingo adopted an aggressive cost program built on smart AWS resource use through Spot.io’s Elastigroup. The platform automatically balanced discounted Spot Instances with Reserved Instances and other options, providing hands-off compute management while lowering spend. Results were clear: compute costs fell by more than 60% in a single quarter, and total AWS costs dropped 25%.
The company redirected those savings into product work, especially AI and machine learning that sharpen personalization and learning outcomes. Lower unit costs also allowed larger A/B tests of new ML models at roughly 50% less expense than before.
Scale improvements and speed gains
The rebuilt architecture now supports the world’s most used language learning app, with 97.6 million monthly active users and 31.4 million daily active users completing over 7 billion exercises each month. Refactoring the core engine from Python to Scala delivered a step-change in speed, cutting lesson generation time from 750 milliseconds to 14 milliseconds while maintaining 100% uptime.
Follow-on work for Android improved app startup by 40%, which is vital for mobile users in emerging markets. More than 100 independent services, orchestrated on Amazon ECS, enable rapid feature releases and steady performance even under massive load.
3. Salesforce core system replatforming
Salesforce's Hyperforce is a strategic replatforming that moved the company from proprietary data centers to a globally distributed architecture on public clouds. The shift, an example of legacy modernization, addressed key constraints: faster global expansion, compliance with data residency rules, and a stronger competitive posture in enterprise software. Let’s take a closer look at this legacy modernization example.
Well-run modernization can deliver under budget
Hyperforce shows how careful planning and phased rollout can meet financial limits while still hitting strategic aims. Instead of a disruptive “big bang,” Salesforce kept the core multitenant application design and rebuilt the infrastructure layer underneath it. The team leaned on public cloud providers’ global regions and managed services, avoiding large capital spend on new facilities. This freed people and budget to focus on higher-value work, such as AI features like Agentforce that set the platform apart in crowded markets. The result was a controlled shift that delivered business benefits without waste.
Customer service continuity measures
Backward compatibility was a hard requirement, so customers could keep running without changes during the move. Service level agreements held steady, while elasticity and global distribution on public cloud improved performance and reliability. Existing apps, customizations, and integrations stayed intact, and many customers saw faster response times.
The Zero Trust model—with least-privilege access, network segmentation, and end-to-end encryption—raised the security bar and supported strict frameworks like GDPR and CCPA. Communication plans explained data residency choices and expected performance gains, which helped build confidence.
Lessons learned and best practices
The Hyperforce implementation revealed several critical success factors for large-scale modernization projects. Strategic timing proved essential – Salesforce initiated the replatforming before crisis situations forced reactive decisions, allowing for careful planning and execution. The decision to preserve proven application architecture while modernizing infrastructure minimized risk and complexity.
Tight alignment across engineering, operations, and business groups kept execution focused on shared outcomes. A phased rollout created room to learn and adjust without harming customer trust. Investments in automation and monitoring delivered visibility and control throughout the change. Most importantly, the work was framed as a platform for future innovation, not only a fix for technical debt, which rallied broad support.
4. Microsoft Teams platform evolution
Microsoft rebuilt the Teams desktop client, once an iconic legacy application example, from the ground up to boost performance and set a base for advanced capabilities. The change targeted heavy CPU, memory, and disk use that had begun to hurt Teams when compared with lighter rivals.
Implementation phases and rollout strategy
Teams followed a full replacement plan, rebuilding the desktop app rather than layering fixes on top of old code. Phase one moved from Electron to Microsoft Edge WebView2, using the browser engine already present on Windows instead of bundling separate runtimes. Next, the team shifted the entire front end from AngularJS to React to support modular code and faster screens.
All UI elements were aligned on Fluent UI, Microsoft’s design system, which improved consistency and reduced redraw cost. Microsoft ran broad beta programs with enterprise tenants to verify speed gains and check that workflows, add-ins, and compliance features kept working.
Performance improvements and operational benefits
The new Teams client brings large, visible gains for the platform’s 320 million users. Startup is faster; memory use is down by 50% and disk footprint by 70% versus the old client. Video calls are far more efficient, with power draw cut by about 50% during calls—important for laptops and tablets. Joining meetings is twice as fast, which reduces friction at the moment people connect. By separating data work from UI rendering, the app stays responsive under heavy load and avoids the freezes that users saw before.
Post-migration optimization and monitoring
Because Teams now runs on WebView2, performance and security updates in the Microsoft Edge engine flow to users quickly, which keeps the client fresh without extra downloads. Microsoft built end-to-end monitoring to watch key metrics across many device types and usage patterns, then uses that data to guide tuning work.
The lighter client leaves room for demanding features such as Microsoft 365 Copilot, turning Teams into a hub for communication and AI-driven productivity. Current efforts aim to trim startup further, raise video quality on poor networks, and enrich collaboration features that use the new foundation.
5. Adobe
Adobe’s transformation from perpetual licenses to cloud subscriptions shows how a platform shift can enable new business models and reach more users. The change required new technology, a different approach to product building and delivery, and a direct customer model.
The WHY behind Adobe’s legacy system modernization case study
Adobe moved away from boxed software because that model led to uneven revenue, high entry costs, and easy piracy. The 2008 downturn, which cut revenue by about 20%, made the need for a steadier subscription model clear. Cloud services and new customer expectations—constant updates and access across devices—made the switch both feasible and timely. New cloud-first creative apps were also putting pressure on Adobe, while the high upfront price of Creative Suite kept out students and small firms. By shifting to subscriptions, Adobe widened access to pro tools and built direct relationships with customers instead of relying on retail channels.
User experience enhancement strategies
Creative Cloud focused on steady value through cloud-connected apps, smooth file sync, and integrated services. Adobe rebuilt desktop apps to use cloud storage so users could open projects on any device and work with teammates in real time. Subscriptions supported continuous feature drops instead of long waits for major releases, so customers always had the latest tools.
Deep links to Adobe Fonts, Adobe Stock, and collaboration features like Team Projects created a system that went past single apps. New mobile apps and simpler tools such as Adobe Express brought in casual creators and business users who needed capable results without pro-level complexity.
Before and after application comparison
Results show Adobe’s move from selling boxed apps to running a full creative platform. Before the shift, Adobe sold separate desktop tools under perpetual licenses priced around $1,300–$2,500 upfront, with major updates every 18–24 months. The core buyers were pro creators in large companies, which limited growth. After the shift, Adobe runs a subscription-based system that starts at about $10 per month for single apps or about $70 per month for the full suite.
The cloud-connected platform now serves professional, education, and consumer users. Revenue rose from $4.4 billion in 2013 to $19.41 billion in 2023, and market cap grew from $16 billion to above $276 billion. The new platform supports fast releases of AI features such as Adobe Firefly, which helps Adobe keep its lead as creative tech keeps changing. And could probably make another interesting AI-oriented application modernization case study in the future.
Partner with us for your platform upgrade
Organizations planning large application changes need experienced support to handle both technical and business issues. Our end-to-end services pair deep engineering skill with clear business alignment so your program delivers measurable value while keeping risk and disruption low. We bring experience from full rewrites, phased refactors, and cloud moves. Contact us to discuss your needs and see how a well-run transformation can advance your organization’s growth goals.
