2025 Legacy Modernization Price Guide

Written by
Last updated on:
October 16, 2025
Written by
Last updated on:
October 16, 2025

Legacy system modernization can eliminate tech debt and bolster efficiency. But what does modernization cost in 2025?

Legacy systems are the backbone of even the most powerful companies, with a 2022 survey finding that more than two-thirds of businesses still use mainframe or legacy apps for core business operations. McKinsey corroborates this, with a recent article finding that, even in 2025, 70% of the software used by Fortune 500 companies was developed 20 or more years ago. 

Unsurprisingly, these outdated systems are expensive both to run and maintain. The same McKinsey article reports that one large European bank spends 70% of its IT capacity maintaining its legacy systems. Additionally, nearly 60% of CTOs said their legacy tech stack is too costly and inadequate for modern applications. Legacy systems are also vulnerable to several other issues and hidden costs, including potential data breaches, slow performance, and general inefficiencies.

If you’re struggling with these issues and looking for change, exploring your legacy modernization options is a smart first step. This 2025 Legacy Modernization Price Guide outlines typical application modernization pricing, reveals hidden costs, and helps align project expectations.

What Different Legacy Modernization Options Could Cost

Not every modernization project looks the same, with specific approaches working better for some companies more than others. According to Gartner and IBM, there are seven main modernization options that businesses can use to update their systems. These are, in order of least to most challenging and disruptive to implement:

  1. Encapsulate: This option preserves the legacy system as-is, while making its data and functions accessible through APIs. It’s often the lowest-cost approach, though the ongoing expenses of the old system remain. Encapsulation does not typically address technical debt.
  2. Rehost: Also known as “lift and shift,” rehosting involves moving the application to new infrastructure, like virtual machines or cloud servers, without changing its code. It’s relatively quick and low-cost compared to deeper modernization, but, like encapsulation, doesn’t solve long-term issues like technical debt.
  3. Replatform: Here, you move the system to a new runtime platform with small adjustments to the code. It costs more than rehosting, but it can unlock performance gains, better scalability, and modest operational savings.
  4. Refactor: Refactoring involves restructuring the code to clean up technical debt and make the system easier to maintain. The upfront effort is higher, which raises costs, but it can reduce ongoing expenses significantly over time.
  5. Rearchitect: This approach reshapes the application so it can take advantage of a modern architecture, such as microservices or cloud-native. It requires significant investment, but it allows the system to scale more easily and integrate with new tools.
  6. Rebuild: Rebuilding means rewriting the application from scratch while keeping its purpose intact. It’s a significant investment in both cost and time, but it produces a system tailored to current business needs.
  7. Replace: The most disruptive option is replacing the legacy system entirely with a new solution. It’s often the most expensive path upfront, but it can deliver the strongest ROI if the old system no longer supports the business.

In many cases, companies don’t pick just one option—they combine them. For example, a business might quickly rehost its non-critical apps, while taking the time to put its core systems through refactoring or complete rebuilds. This mix-and-match approach keeps budgets under control while still addressing long-term needs.

Common Legacy Modernization Pricing Models in 2025

Enterprises planning legacy modernization in 2025 will find that not all vendors price projects the same way. Some rely on tools, others on services, and many combine the two. Understanding the common pricing models up front makes it easier to compare proposals and avoid surprises later.

Time and Materials (T&M)

This is one of the most common models for legacy modernization projects. Companies pay for the actual hours and resources used, which provides flexibility when requirements evolve. However, the trade-off is that final costs can be harder to predict if the scope expands.

Fixed-Cost Pricing

In fixed-cost models, vendors set a price for a well-defined legacy modernization project. This offers predictability and reduces financial risk, but it leaves less room for changes once work begins. Fixed-cost pricing is best suited for projects with clearly scoped deliverables and minimal uncertainty.

Subscription and Usage-Based Pricing

Some modernization platforms now follow a subscription or usage-based model, similar to SaaS. This is especially common with AI application modernization tools that handle code translation, refactoring, or testing. Pricing might be tied to the number of users, the volume of code processed, or monthly platform access. While this keeps costs scalable, companies still need to budget separately for testing, compliance, and integration.

Value-Based Pricing

Other vendors are experimenting with value-based models. Instead of charging for hours or licenses, they tie fees to business results, such as faster release cycles or lower operating costs. It aligns spending with outcomes, but the structure can be more complex to negotiate and measure.

Bundled Pricing

Cloud providers sometimes package modernization services with infrastructure credits or additional tooling. While bundles can reduce upfront spending and simplify purchasing, they often come with usage caps or licensing restrictions that companies must closely monitor.

AI-Augmented Legacy Modernization: Minimizing Costs & Timelines

While legacy modernization is necessary to keep systems going strong, many companies opt to postpone it. The reasons are understandable: These projects can drag on for months or years, disrupt their day-to-day operations, and cost hundreds of thousands of dollars. The average cost of a traditional legacy tech upgrade was $2.9 million in 2023, and according to recent industry analyses, 67% of legacy modernization projects exceed initial budget estimates by over 35%. 

Fortunately, AI-augmented legacy modernization is providing a faster and more affordable alternative. McKinsey & Co. reports that, by automating much of the manual work, generative AI can lead to a 40 to 50% acceleration in tech modernization timelines, while reducing the costs of technology debt by 40%. This is largely due to the rise of agentic AI, which can autonomously complete complex, multi-step tasks.

With the help of AI agents, companies can streamline tasks that were previously manual and time-consuming. These include:

  • AI-Powered Code Analysis and Refactoring: AI tools can analyze millions of lines of complex, undocumented legacy code, identify redundant or inefficient sections, and even translate it into modern languages like Python or Java. This drastically reduces the time and specialized developer hours needed for deep refactoring.
  • Documentation Generation: One of the significant hidden costs of legacy systems is the lack of documentation. AI can crawl through an application's code and automatically generate technical documentation and user stories, saving countless hours of reverse-engineering work.
  • Enhanced Testing and Validation: AI-powered testing tools can generate test cases, run automated tests, and identify bugs with a speed and accuracy that manual testing cannot match. This reduces the risk of post-launch issues and the associated costs of emergency fixes.
  • Accelerated Development: AI coding assistants can generate code snippets and entire functions, freeing up developers to focus on higher-level architectural decisions and creative problem-solving. This boosts productivity and accelerates project timelines.

AI-augmented legacy modernization also requires much smaller teams compared to traditional modernization. While a traditional project needs a large number of legacy specialists, an AI-augmented team typically consists of a core group of senior developers, an automation-focused QA team, and specialists like a prompt engineer. This brings costs down further, making it significantly more affordable for the average business owner.

Here's a breakdown of the typical costs and timelines for traditional versus AI-assisted projects.

<table border="1" cellpadding="5" cellspacing="0" style="width:100%; border-collapse: collapse; text-align: left;"><thead><tr><th>Project Size</th><th>Traditional Modernization</th><th>AI-Assisted Modernization</th></tr></thead><tbody><tr><td>Small project</td><td>$50K – $150K (3–6 months)</td><td>$25K – $75K (1.5–3 months)</td></tr><tr><td>Medium project</td><td>$180K – $270K (6–12 months)</td><td>$75K – $150K (3–6 months)</td></tr><tr><td>Enterprise project</td><td>$500K+ (12–18 months)</td><td>$250K+ (6–12 months)</td></tr></tbody></table>

Mainframe and COBOL Modernization Costs: What to Expect

Common business-oriented languages, such as COBOL, are widely used by financial institutions and government organizations. According to IBM, despite being considered a legacy system, COBOL’s imperative, procedural, and object-oriented configuration supports over 40% of all online banking systems, 80% of in-person credit card transactions, and 95% of all ATM transactions.

However, while COBOL has its perks, roughly 85% of COBOL applications run on outdated mainframe infrastructure. These systems are expensive to run, disconnected from valuable cloud services, and, as time goes on, it’s becoming more of a challenge to find workers experienced in the language. Unfortunately for business owners, these systems are also pricy to update, with traditional COBOL modernization tools for enterprises often costing millions.

The good news is that, through the use of generative AI, COBOL modernization is becoming a more affordable and practical option. In AI for IT modernization: Faster, cheaper, better, McKinsey & Co. notes that “a transaction processing system…which three years ago would have cost much more than $100 million to modernize…is well less than half of that when using gen AI.”

Legacy Modernization ROI and Hidden Costs Checklist

When you’re deciding whether or not you’re ready to modernize your systems, you might be tempted to take the price tag at face value. However, the full cost usually includes more than the first estimate. Projects like this often bring real savings over time, but they also create expenses that don’t always show up until later.

Some common hidden costs include:

  • Extra testing and validation before launch
  • Running legacy and new systems at the same time
  • Data migration and clean-up work
  • Integration with finance, HR, or CRM platforms
  • Training and change management for teams
  • Compliance reviews and audit cycles

Some extra ROI benefits of modernization, however, can include:

  • Lower licensing and infrastructure fees
  • Easier updates and long-term maintenance
  • Reduced risk from outages, security gaps, or scarce skills
  • Flexibility to scale and connect with modern tools

By planning for both the upside and the less obvious costs, companies can build a budget that reflects the whole picture.

Budgeting for Legacy Modernization Costs in 2025

The cost of modernization in 2025 depends on many factors—from the complexity of the systems involved to the pricing model a vendor uses. Some expenses are easy to spot, like licensing or subscription fees, while others can sneak up later, such as extended testing or compliance reviews. The most effective budgets plan for both, and connect spending to long-term value instead of short-term fixes.

At FullStack Labs, we help companies work through this process. Our team breaks down where costs are likely to come from, explains the trade-offs between different modernization options, and develops plans that align with real business goals. Whether your project involves traditional legacy updates or newer approaches like AI-assisted modernization, we can help you create a clear path forward for your business. 

Contact us today to learn more about AI-augmented legacy modernization services for your business! 

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Frequently Asked Questions

While traditional enterprise modernization projects can cost over $500,000, with some reaching into the millions, AI-assisted projects for enterprises typically cost $250,000 and up. The cost varies widely based on factors such as the complexity of the legacy system, the scope of the project, and the specific pricing model used by the vendor.

Agentic AI refers to a more advanced form of artificial intelligence that can act with a degree of autonomy to achieve complex, multi-step goals. Unlike traditional AI, which typically performs a single task based on a prompt, an AI agent can break down a high-level objective—like "modernize this application's API"—into sub-tasks and execute each step independently. This allows for the automation of entire workflows, leading to unprecedented speed and efficiency.

Yes, the shortage of skilled COBOL developers is a significant driver for AI modernization. It is becoming increasingly difficult to find workers experienced in the COBOL language. While 85% of COBOL applications run on outdated mainframe infrastructure, the language still supports critical systems in banking and finance.

AI provides a solution to this talent gap by automating the translation of COBOL code into modern languages, allowing companies to update their systems without relying on a shrinking pool of legacy specialists.

Choosing between these options still depends on your specific business goals, but with AI in the loop, the decision-making process is enhanced.

  • Rehost is the fastest and least expensive option, ideal for quickly moving an application to the cloud.
  • Replatform offers a balance, providing better performance and scalability with minor code adjustments.
  • Refactor is the most comprehensive option, creating a clean, modern system that is easier to maintain long-term.

AI's ability to automate many of the labor-intensive tasks within each of these options (especially in refactoring) makes even the most complex choices faster and more affordable than in the past. The best approach is often a combination of these options, with AI accelerating the parts that require the most effort, allowing you to choose the strategy that best fits your budget and timeline.

Yes, AI can rewrite legacy code. Advanced AI tools and platforms, powered by large language models and specialized AI agents, are capable of analyzing and translating millions of lines of code from outdated languages like COBOL to modern ones like Python or Java. This ability is a major factor in accelerating modernization projects and is a key reason many companies are adopting AI-augmented solutions.