Although IT budgets are growing, they’re not always advancing tech innovation. Almost a third of CIOs admit that budget increases are mainly due to infrastructure modernization and migration projects.
Just like physical equipment, digital technology is prone to aging. Legacy, on-premises software systems lack the performance capabilities of modern cloud architectures, resulting in frequent downtime, clunky user experience, low operational efficiencies, and high maintenance costs.
Legacy solutions also limit your ability to benefit from the latest technologies like big data analytics, artificial intelligence, and IoT due to poor compatibility. This, in turn, undermines your ability to stay competitive in the changing markets.
At some point, leaders need to make a choice — modernize or replace legacy technology. In this post, we explain how to decide between different approaches to legacy software modernization and when to do so.
7 Main Approaches to Legacy System Modernization
When it comes to legacy system modernization, business leaders often picture a sort of “surgical removal procedure” — you take out the old component and plop in a new one, and everything starts running twice as well.
But such scenarios are uncommon and oftentimes, carry operational risks. For example, banks hold on to legacy core banking systems, and even those overnight batch processing impede real-time transaction processing because a full replacement can create operational, compliance, and security risks.
More commonly, businesses opt for one of the following approaches to legacy system modernization:
Encapsulation
Encapsulation “packages” the legacy applications’ data and functions as independent services available via an API. Effectively, you externalize some of the app’s features and can then integrate or embed these into other applications. For example, you can gain access to transactional data from a legacy CRM and integrate it into a modern digital experience management platform.
Encapsulation often requires the least engineering effort. However, it may not solve underlying scalability problems or application performance limitations. For example, if you create APIs to get data from legacy flat file mainframe databases, you’ll still have limited concurrency due to the mainframe’s I/O bottlenecks, resulting in slow response times under heavy load.
Rehosting
Rehosting, also called “lift and shift,” means migrating an application to another type of infrastructure (new physical servers, virtual machines, or cloud) without changing its code or features.
This strategy can result in some hardware consolidation and a shift from CAPEX to OPEX spending in the case of the cloud. However, rehosting mostly solves the resource problem (i.e., the app gets more computing power and extra capacities for data storage) but doesn’t always lead to better app performance.
Because you’re migrating an app ‘as is’, you’re carrying over all the technical debt and underlying inefficiencies without fully benefiting from the cloud capabilities. Likewise, rehosting rarely solves integration problems with more modern tech stacks, as you’d still need to create some form of middleware solutions.
Replatforming
Replatforming assumes bringing the app to a new runtime platform with minimal changes to the code to ensure its compatibility with the new environment, most commonly the cloud. Like rehosting, replatforming brings marginal scalability, reliability, and performance improvements.
Since the core app architecture and core remain mostly unchanged, many of the legacy limitations will still be present. The process can also be time-consuming and error-prone if you need to migrate a lot of data, for example, as part of migrating from one e-commerce platform to another. But when done right, replatforming can help address some of the issues by upgrading the applications’ operating system, database, or some of its components. Moreover, you get access to new platform features and capabilities.
Refactoring
Refactoring optimizes and improves application code before migrating it to a new environment (e.g., a cloud platform). It’s the most common strategy for eliminating technical debt and improving factors like availability, scalability, security, and maintainability.
Code refactoring allows incremental quality improvements and incorporation of new features. For example, developers may separate payment logic from the core transactional platform into a separate service built with cloud-native technologies to increase transaction processing volumes and speed.
Refactoring generally improves application performance, reduces technical debt, and enhances codebase maintainability. However, it can be tedious for large, monolith applications with limited documentation. Since refactoring mainly addresses non-functional requirements (not the app’s external behavior or user interfaces), the updates may not be apparent to end users, making it hard to justify the investment.
Rearchitecting
Rearchitecting an application involves substantial changes to its code and structural design — how components interact and what principles guide their design — with the goal of obtaining better capabilities. For example, transforming an on-premises monolith application into a cloud-based microservices based one.
With re-architecting, you gain the most benefits in terms of scalability and maintainability, as you’re essentially creating improved guardrails for its business logic and strategically tackling problematic areas. In the case of microservices, modularity also enhances fault isolation and allows the most tech stack diversification (i.e., each microservice can be written in a different programming language or with a different front-end framework if that makes sense).
On the downside, application re-architecture requires expertise with both the legacy and the modern tech stack. The process also requires meticulous planning to minimize operational disruptions. Furthermore, managing the coexistence of old and new architectures during the transition period can be resource-intensive.
That’s why many business leaders involve external technology partners (like us!) to help plan and deliver the project in iterations. As part of our legacy software modernization service, we bring the necessary expertise to create new reference architecture patterns, perform code updates and data migration, and provide comprehensive quality assurance to ensure that the modernized system meets performance, security, and reliability requirements.
Rebuilding
A rebuild involves dismantling and rewriting the legacy application from scratch while preserving its scope and specifications. Effectively, you salvage the application features and business logic but place these on an entirely new technology stack.
Rebuilding an application can make sense when the initial app design contains some fundamental flaws, or the technology stack is severely outdated. But you still want to have a proprietary version of this application instead of purchasing an off-the-shelf solution as a replacement.
Replacing
Full application replacement with a completely new solution, built according to the new software specifications, is the most drastic measure. Naturally, this approach is the most cost- and resource-intensive, but it may be the best course of action when legacy technology no longer has strategic value for your business and erodes a good fraction of your technology budget.
Each of the above application modernization strategies differs in terms of “invasiveness” — the degree to which you’d meddle with the application code and key structural components. Likewise, there are tradeoffs in each case. 8allocate team can help you decide on the optimal strategy and execute the modernization project.
When to Incrementally Modernize Legacy Applications
Legacy software is an ever-growing cost center and an operational nuisance.
Outdated technologies hinder employee productivity, user experience, and have a hefty total cost of ownership (TCO) due to the need for specialized talent and constant “workarounds” to extend their lifespan. Especially if the official vendor support has long ended. Subsequently, half of business leaders name “challenges with technology infrastructure” as the chief internal barrier to cost control.
Here are cases when incremental software modernization makes the most sense:
You’ve Maxed Out System Scalability
By design, legacy monolithic architectures have limited throughput as all the application components are bundled together. The entire application must be scaled to handle the increased load. For example, if only one database in your ERP needs more storage, you can’t just scale his database — you need to scale the entire application, requiring greater labor efforts and overpayment for computing resources.
Likewise, because all monolith application components share the same resources (CPU, memory, and I/O), you’ll hit the ceiling at some point regarding scalability. When that happens, the app components start competing for the same resources, resulting in resource contention and complexities in load balancing.
Oftentimes, such scalability problems emerge when an MVP product, designed as a monolith, starts gaining traction and the system can no longer handle increased usage.
If that’s your case, you’ll need to incrementally upgrade to service oriented architecture (SOA) or straight to microservices.
The optimal approach to decomposing and scaling such a monolith application will depend on its design pattern (and your business and tech requirements). In some cases, it may even pay off to keep a modular monolith — a microservice-like architecture where the application is split into loosely coupled modules with a distinct domain or bounded context but some shared resources (e.g., a shared data tier).
Source: Medium
Amazon, for example, recently reverted to a modular monolith architecture after microservices for its video monitoring services as it delivered superior performance and 90% cost reduction.
Your Codebase Has Grown Too Complex
Legacy codebases, spanning millions of lines of code, make it more difficult for developers to understand and troubleshoot code. Bugs and system vulnerabilities go unnoticed, causing costly issues in production or, worse, data breaches.
Bloated codebases also prompt developers to come up with their own solutions to various coding problems rather than following the existing standards. As a result, you get a lot of “spaghetti code”, which no one understands what it does and is afraid to tinker with for the sake of crashing the entire system. Code complexity results in further tech debt and all its consequences of poor system performance, security, and scalability.
Organizationally, large codebases also undermine software development productivity. Developers are more likely to experience merge conflicts, get frustrated with long code compilation time, and spend a lot of time retesting the same code for integration issues. As a result, you have slower release cycles, limiting your ability to release patches fast and deploy new product features.
You can improve the code quality by refactoring and re-architecting. Sha Ma, VP of software engineering at GitHub, shared that when modernizing legacy applications, she always insists on conceptually separating the code and data based on feature functionalities before creating new microservices.
This allows the team to design smaller services with easier-to-read code, quicker ramp-up time, and easier troubleshooting. Effectively, the developers no longer need to know and understand the entire legacy product codebase to be productive. Instead, they can focus on improving and extending the features of one microservices, which they know in and out.
You Want to Increase New Feature Deployment Speeds
Time-to-market for new features is pressing, and legacy systems aren’t conducive to continuous feature deployment. Because monolith architectures require full re-deployment to incorporate new releases, new code deployments sit in the pipeline for days, and major system updates require planned downtime.
Likewise, the need for constant maintenance undercuts the time for innovation. A CISCO survey found that half of the time, developers are being dragged into ‘war rooms’ to solve application performance issues rather than invest their time in developing new software applications.
Limited deployment capabilities were a major bottleneck for Airbnb circa 2015. At that time, the home-rental platform was built as a monolith Ruby on Rails application. A team of 200 engineers was deploying 200 commits to production daily to respond to the market demand. But they also experienced around 15 hours on average of time when the Monorail was blocked, recounts Jessica Tai, a Senior Software Engineer.
To address the problem, Airbnb went on a mission to migrate to service-oriented and microservices-based architecture over the next four years. By incrementally re-architecturing and upgrading its platform, Airbnb managed to increase its throughput speed to over 10,000 deployments a week while also increasing its workforce to over 1K people, getting new code to production faster and more reliably.
Your Want to Experiment With Innovative Technologies
Legacy monolith applications aren’t compatible with many more modern technologies like real-time big data analytics, machine learning, computer vision, and generative AI, among many others. By design, a legacy app may not support machine learning workloads’ computational and storage requirements. Likewise, batch data processing limits data throughput speeds, required by modern data processing and analytics tools.
Encapsulation is one option for solving this data management issue. With the help of APIs, you can stream data from legacy apps to a cloud-based storage system (e.g., a data lake or a data warehouse), from where it can be queried by cloud-native analytics tools and ad-hoc ML models. However, refactoring and rearchitecting may be necessary if you want to improve the applications’ performance and/or extend its functionality.
You can also achieve greater fault isolation through microservices, ensuring that bugs in individual app components don’t affect the entire system, resulting in costly downtime.
Lastly, you can achieve greater system performance by combining different tech stacks in a microservices architecture, picking the best technologies for each purpose. For example, you may develop a user authentication service with Node.js to benefit from its lightweight and asynchronous capabilities, while the content recommendation service can be built with Scala and Apache Spark to handle large-scale data processing and machine learning algorithms. This approach also lets you address the UX issues of a legacy app, as you can experiment with different front-end frameworks and designs.
Your Customer Experience is Lagging
The complexity of legacy systems is the most significant barrier to meeting customer needs and expectations, according to the ICMI study. From missing self-service capabilities to planned application downtime, obsolete technology undercuts employee and customer experience.
Dated applications don’t have the best interfaces, alienating users with complexity and low utility. Many also lack modern features consumers have come to expect from businesses, like instant payment processing or a high degree of service personalization, resulting in further frustration.
Businesses that don’t make amends risk losing their market share. By 2028, financial institutions can miss out on 42% of extra payments-related revenue annually unless they upgrade to modern paytech platforms. The stakes are similar in other industries.
Incremental modernization helps you progressively improve your product’s technical performance and address CX frustrations with new features. For example, by re-architecting its systems, a payment services provider can enable real-time transaction updates and integrate AI capabilities like real-time fraud detection or automatic risk management.
When To Fully Replace Legacy Software
Incremental system modernization is a more common route for salvaging legacy technologies. In several cases, rebuilding or replacing a legacy application makes more sense.
The Software Nearing End of Support
Large software vendors like Microsoft, SAP, and Oracle, among many others, also feel the financial squeeze of maintaining legacy software products as these divert resources away from innovation.
For example, Microsoft has already relinquished support for several Dynamics CRM editions (2011 to C5 2014) and will end support for Dynamics CRM 2014 to 2016 in the next couple of years, encouraging users to adopt cloud-based Dynamics 365.
End of support means the vendor will no longer provide updates, security patches, and tech support for the product, leaving the software vulnerable to:
- Security risks
- Interoperability issues
- Bugs and glitches
This increases maintenance workloads for the engineers, forcing them to come up with workarounds to keep the system safe and running. At the same time, systems past the expiry date create operational issues as they become unstable, prone to downtime, and data loss.
You’ve Discovered a Critical Vulnerability
Legacy systems may contain critical bugs hidden deep within the bloated codebase. Likewise, when the system contains no longer supported technologies (e.g., components in Python 2) it becomes prone to new security threats as there’s no official patch available to address the gap.
Vulnerabilities create a backdoor for hackers. A Gartner survey found that end-of-life systems with critical vulnerabilities were the root cause of data breaches in healthcare. Although replacing a tech system is expensive and somewhat challenging, it’s better than dealing with the aftermath of a security breach.
The System No Longer Aligns With Your Tech Roadmap
Considering just how much the business models have changed, companies may find themselves with redundant or outdated systems in their portfolio, like a legacy CMS (e.g., Microsoft FrontPage) or an outdated accounting (e.g., Peachtree Accounting).
Likewise, some early product features, deployed at an MVP stage, may have shown little traction and generated little value for end-users. For example, Microsoft scaled back Cortana’s consumer-facing features and instead focused on embedding its capabilities into Microsoft 365 apps.
When the tech systems no longer align with your business objectives, retiring them and redirecting the resources toward more strategic objectives makes sense.
Final Thoughts
Leaders are often reluctant to let go of legacy software because of the perceived status quo and loss aversion. But the sunken costs only get higher when you promulgate modernizing legacy products. Technical debt is like credit card debt: The longer you keep it, the more overwhelming and expensive it becomes to take corrective actions.
Legacy software modernization indeed has its challenges. But it also sets the path for business model innovation and new revenue enablement through modern technologies. More secure, reliable, and feature-rich systems drive in new customer audiences and improve your position in the market.
If you’re not sure how to proceed, talk to our team. We can offer a candid, expert take on how to maximize the value of your tech portfolio without creating operational disruptions and critical downtime.
Frequently Asked Questions
Quick Guide to Common Questions
How to decide between modernizing or replacing a legacy system?
If the core business depends on the system but limits scalability, performance, or integration with modern tools, incremental modernization is the best approach. If security risks, maintenance costs, or outdated technology stacks are blocking growth, a full replacement may be necessary.
What are the key risks of maintaining a legacy system too long?
- Security vulnerabilities due to lack of updates and compliance risks.
- Rising operational costs from maintaining outdated infrastructure.
- Scalability bottlenecks, limiting growth and feature development.
- Incompatibility with cloud, AI, and real-time data processing.
Which modernization approach works best for improving scalability?
Rearchitecting or refactoring is often the best option if scalability is the primary concern. Breaking a monolithic system into microservices or cloud-native architecture allows for incremental scaling while reducing system dependencies.
How can AI and analytics be integrated into a legacy system without complete replacement?
Encapsulation via APIs allows data extraction and integration with AI-driven analytics or machine learning models without overhauling the entire system. This is a common approach for companies wanting real-time insights without major disruptions.
What are the biggest challenges in migrating a legacy system to the cloud?
- Refactoring code to ensure compatibility with cloud-native environments.
- Minimizing downtime during migration.
- Optimizing costs, as simple rehosting doesn’t fully leverage cloud benefits.
- Ensuring regulatory compliance in cloud-based data handling.
How to reduce modernization risks while keeping operations running?
A phased approach—migrating components in stages, prioritizing critical pain points, and running parallel systems—minimizes disruption. Proof-of-concept deployments also help validate the impact before full rollout.
How does 8allocate help with legacy system modernization?
8allocate assesses current architecture, technical debt, and business priorities to recommend the best modernization path—incremental upgrades, re-architecture, or complete rebuild—while ensuring a seamless transition and minimal risk.
