The majority of businesses today operate on legacy software systems. Specifically, more than 70% of the software used by Fortune 500 companies is at least 20 years old.

This software may still be functional, but what about the price in terms of investment vs. the received value? Aging systems accumulate growing software maintenance cost factors due to outdated architecture, undocumented code, and dependencies on the leading experts who understand the code completely. With each year, your software becomes less efficient in meeting business demands, while you need to invest more money and resources just to keep it afloat.

U.S. companies spend up to 80% of their IT budgets on maintaining existing technologies, leaving just 20% for innovation. This disparity exacerbates every year and restrains business growth opportunities.

Legacy applications increase costs and degrade business performance:

• Extended testing cycles because of complex dependencies 
• More complicated debugging due to poor documentation 
• Inability to implement modern tools
• Developer team burnout due to endless fixes

We’ll explain why maintaining modern software is much more beneficial than outdated systems, which factors magnify expenses, and how to optimize annual software maintenance, enabling innovative development.

Key Factors Influencing Software Maintenance Costs

Let’s start by identifying which factors expand your maintenance expenses, year after year.

Code complexity and lack of modularity

Non-modular systems accumulate dependencies that trigger cascading issues with every change. Developers must navigate tangled code to implement even minor updates. This increases risks, time, and cost.

Outdated technology stacks and unsupported libraries

Systems created a long time ago often rely on obsolete frameworks and unsupported libraries. Without vendor updates or compatible tools, teams must rewrite code and patch integrations manually. Compounding technical debts increase security and performance issues, while continual fault fixing consumes unjustified resources.

Dependency on hard-coded integrations and legacy APIs

Old APIs and hard-coded integrations lock the system’s functionality with restricted external services or data formats. When external services change or age, even small updates require extensive rework, creating costly bottlenecks and blocking scalability.

Skill shortage: Fewer developers are familiar with old tech

As legacy technologies age, fewer developers remain proficient in them, which leads to limited availability. Eventually, scarce expertise drives up rates and slows knowledge transfer, turning routine maintenance into a time-consuming expense.

Lack of automated testing and CI/CD pipelines

The absence of automated testing increases manual effort required for quality assurance. The result? Larger operational costs, potential mistakes, and delayed deliveries. The releases are further extended by the absence of CI/CD pipelines, which turns revisions and debugging into a burden. Organizations spend budget and time fixing what could have been prevented rather than enhancing functionality.

Unclear ownership and documentation debt

With the lack of documentation and ownership information, developers can neither identify who made the latest code modifications nor understand the decisions of previous teams. The outcomes include inefficient onboarding and troubleshooting, plenty of redundant effort, and compounding long-term maintenance costs.

Andrew Lychuk highlights the business impact: “Technical debt without documentation undermines the whole process and the whole system. Every undocumented fix becomes tomorrow’s $10,000 problem.”

Maintenance Cost Differences: Legacy Monolithic vs. Modern Architectures

The cost of maintenance in software engineering differs sharply between legacy monoliths and modern modular systems, reflecting variations in technology, risk, and long-term sustainability.

Scope of change

In legacy monoliths, every change can affect the entire system. Developers must run full regression tests to make even minor updates, since tightly coupled code risks unpredictable side effects.

Modular or microservice architectures, however, allow updates within isolated units, without any risk to the entire system.

How does it impact performance and software maintenance cost?

Maintenance complexity

The following software quality metrics can help you measure maintainability:

• Cyclomatic complexity: calculates the number of possible code paths, which translates into potential ease of testing and debugging.
• Modularity index: reflects the quality of the system’s division into independent, reusable parts.
• Halstead volume: describes how much effort is required to understand, test, or modify code.

Infrastructure and operational expenses

Software maintenance costs heavily depend on the utilized infrastructure. Legacy systems rely on on-premises servers that require manual patching and constant upkeep, while modern architectures leverage automated, scalable cloud services.

The human factor

A major hidden risk in legacy systems is tribal knowledge, undocumented logic known only to a few developers.

Modern architectures reduce this risk through shared documentation, CI/CD pipelines, and standardized practices.

The Impact of Technical Debt and Poor Documentation on Maintenance Costs

Every shortcut in software development carries a hidden price tag that will show up in the future.

Igor Omelianchuk explains: “Very often, developers create non-optimized code with some hard codes and tricky stuff inside. If all this additional or non-optimized work is not documented, the new developer who will try to fix some bug won’t be able to understand what is going on in the system. If the system doesn’t have clues, fixing an issue may take forever.”

These compromises compound technical debt that would later result in extra maintenance or refactoring effort. A small compromise today evolves into a financial burden tomorrow, leading to the escalating software maintenance cost percentage.

The hidden cost of missing documentation

Without clear documentation, new developers waste hours and even days exploring legacy functions and trying to understand the intent behind old code.

Igor Omelianchuk illustrates: “When documentation is absent, a new developer will have to go through cycles of trying faults when new bugs are introduced to the system, struggling to fix and understand the current approaches in the code.”

Apart from delaying deliveries, this also raises average software maintenance cost through repeated rework and investigation that would be needless with documentation in place.

The “Bus Factor” risk

The so-called “Bus Factor” is also the consequence of poor documentation and entails the accumulation of critical knowledge with just several engineers. The rest of the team becomes, therefore, dependent on those people: if they leave, the operations may stall. Other developers will have to fight to restore lost knowledge, which inflicts higher onboarding costs, impeded bug resolution, and a greater risk of downtime.

Andrew Lychuk warns: “If one person leaving can stop your business, that’s not loyalty, that’s risk.”

The cumulative effect of documentation debt

Documentation debt creates a snowball effect over time. Every missing comment or undocumented API elevates IT maintenance costs and slows onboarding. Relying on a few key engineers may seem safe, but it creates dependency and potential misuse of insider knowledge. Clean documentation protects both technical and business integrity.

Technical and documentation debts are more than merely technical concerns. They are significant business risks.

Igor Omelianchuk summarizes: “You need to have good documentation, ideally good structured code with explaining all the tricky turnarounds and features.”

How to Forecast Software Maintenance Costs and Build a Maintenance Budget

Flat Percentage of Development Cost Doesn’t Forecast Legacy Software Maintenance Costs

Most teams estimate the cost of software maintenance with a guess dressed up as a formula:

“Take last year’s development cost, apply a flat percentage, and carry that number into the next cycle.” That formula holds up reasonably well for a young, simple system with a short operating history. 

It breaks down almost immediately for a legacy system, where age, architecture, and complexity have already pulled the real cost away from any fixed ratio.

More than 40% of enterprise systems have already reached end of life or lost vendor support, and the average enterprise technology stack is now over 12 years old. In our audits, that’s almost always where a flat-percentage budget starts to unravel. 

Software maintenance costs simply aren’t a stable fraction of anything once a system starts aging; a budget built on last year’s percentage tends to fail quietly, resurfacing as a surprise line item mid-year.

We build maintenance budgets category by category, because each one carries its own real cost drivers:

• Bug fixing — in our audits, this rarely turns out to be the category actually driving the budget, whatever clients expect going in.
• Infrastructure — the category most exposed to sunk-cost thinking: capacity added under pressure, rarely removed once the pressure passes.
• Security updates — our own diagnostic language draws a hard line here: a framework unpatched for years isn’t technical debt anymore — that’s security depth.
• Third-party dependencies — every modernization readiness assessment treats each one as a decision nobody has revisited since it was made.
• Technical debt — in our experience, refactoring with no measurable business impact is the most common way this line quietly drains a budget.
• Support team — we size this by knowledge concentration more than ticket volume: how much of the system lives in one or two people’s heads.
• Monitoring — the systems that worry us most aren’t the ones that already crashed; they’re the ones that look stable until one small change breaks everything.
• Compliance — we’ve seen this line stay flat for years, then jump overnight the moment a system enters a new regulatory scope. 

Each one ages differently.

We Can Turn Incident History and Engineering Hours Into a Forecasting Model

Once a system passes its warranty window into extended or end-of-life support, we typically see the maintenance line climb another 10-15% a year on its own, with premium support on end-of-life stacks running 50-200% above standard rates. That’s before a single new bug adds to your software maintenance expenses.

Blending all eight categories into one percentage hides which category of software maintenance cost is actually driving the increase.

Averages are for the pitch deck. Your budget has to survive contact with the actual codebase.

Our engineers build the forecast a different way: 

• engineering hours logged against maintenance tickets, 
• incident volume by category, 
• and actual spend over the past two to three years. 

It shows how your specific system behaves, with its own age and its own architecture. Software maintenance costs earn their own forecasting model built from that history, separate from development’s budget line.

From there, our architects apply an age, architecture, and complexity adjustment on top of the trailing average. 

→ Has the codebase gotten harder to change?
→ Has the architecture aged into something harder to test and harder to decouple?
→ Has a key dependency lost support, or has the knowledge of how the system actually works started concentrating in one or two people instead of a team?

A system that’s stable on all three fronts can usually hold its maintenance budget flat year over year.

One that’s aging on any of them can’t. That budget needs to be sized ahead of the incidents, with enough headroom built in before they land.

A one-year forecast that’s off by 15% is an inconvenience. A five-year forecast off by the same margin compounds into a very different budget conversation — which is exactly what tracking a metric like the maintenance-to-development cost ratio, covered later in this article, is built to catch early.

Essential Metrics and KPIs for Measuring Maintenance Efficiency

When seeking how to reduce software maintenance cost, you should identify ways to elevate the maintenance efficiency.  Tracking key efficiency metrics reveals weaknesses, optimizes resource allocation, and justifies modernization investments.

Mean Time to Repair (MTTR)

MTTR measures how much time it takes to identify a failed asset and restore the system to the initial state. High MTTR is the sign of slow recovery, unclear ownership, or excessive complexity. This is typical for legacy environments with poor monitoring and documentation.

Mean Time Between Failures (MTBF)

MTFB shows the average operational time between system failures. Low MTBF is inherent in systems lacking stability and prone to frequent breakdowns, caused by outdated infrastructure, technical debt, or lack of automated testing.

Change Failure Rate (CFR)

CFR demonstrates the percentage of software changes that lead to downtime, rollbacks, or other issues. High values can be caused by fragile code and limited test coverage. Each update can inflict downtime or rollback, common in legacy systems.

Cost per Change Request (CPCR)

The average cost of implementing a particular change or feature. Every change in monolithic code may require full regression testing, involving extensive resources and escalating costs.

Test Coverage Ratio

The percentage of code that can be tested automatically. Low coverage is usually the case with legacy systems, posing the risk of undetected regressions and leading to unpredictable maintenance.

Code Duplication Ratio

The percentage of repeated or redundant code blocks in the codebase. With duplicated code, any emerging bug may appear in numerous places, extending the fixing and maintenance effort.

Comment Density

This indicator demonstrates the ratio of comments to lines of code, showing whether the code is properly documented. Little comments mean a lack of documentation and hidden logic, while excessive comments may witness unclear or complex code.

Coupling & Cohesion

Coupling assesses the interdependence between modules. Cohesion measures the relation between the functions within a module. High coupling and low cohesion mean a fragile code and tight interdependencies. Low coupling improves scalability and speeds up maintenance.

Technical Debt Ratio

The estimated cost of fixing maintainability issues (bugs, refactoring needs, missing tests) compared to the cost of developing new functionality. A high ratio indicates excessive resources spent on maintenance, with lower capacity to innovate.

Maintenance-to-Development Budget Ratio

The part of IT maintenance expenses for keeping old systems alive versus implementing innovations. A ratio above 80% indicates an unjustified legacy load.

Outdated systems often conceal performance issues until they turn into costly failures. Tracking these KPIs helps address gaps through refactoring and modernization, redirecting budgets from  reactive fixes to optimized maintenance.

How to Reduce Software Maintenance Costs Without Increasing Operational Risk

The Optimization Moves That Actually Lower Software Maintenance Costs

Not every dollar cut from a maintenance budget saves money. 

Some of it just moves the cost into next quarter’s incident report.

The fastest way to reduce software maintenance costs without also raising operational risk is to be selective about where the reduction actually comes from. 

→ Automate the recurring work.
→ Retire what’s genuinely obsolete. Refactor where it changes the numbers.
→ Leave the safety-critical work alone.

This is where genuine software maintenance optimization comes from — a short list of moves that consistently pay off:

• Prioritizing the technical debt that costs the most to maintain today
• Automating testing and deployment to remove manual verification from every release
• Improving monitoring and observability so issues surface before they become incidents
• Updating documentation while the people who hold the knowledge are still around to explain it
• Standardizing infrastructure to shrink the number of configurations that can drift
• Retiring outdated, unsupported dependencies
• Refactoring the specific components generating the most incidents or maintenance hours

In our experience, the technical debt that actually costs money has little to do with how long it’s been sitting there. 

1. A five-year-old shortcut in a rarely-touched module rarely matters. 
2. A six-month-old one buried in the path every transaction runs through can be the most expensive line in the budget. 

→ That’s the debt worth prioritizing.

Automation and monitoring tend to pay back the fastest, because they improve maintenance efficiency on work a team is already doing, without asking for new investment.

The Software Maintenance Cost Cuts That Backfire Later

Some cuts look safe on a spreadsheet and cost far more than they save.

Refactoring Without a Measured Driver

Done for its own sake, without a component that’s demonstrably driving cost, refactoring consumes engineering budget without moving the numbers that matter. Our architects see this often enough in client codebases to flag it on its own: teams enjoy refactoring on principle, and a business rarely has an easy way to tell a refactor that’s paying off from one that’s simply keeping engineers busy.

We anchor every refactoring decision to a measured cost driver.  That’s what keeps this lever effective instead of becoming its own quiet source of maintenance spend.

Delaying Security Updates

In our experience, the same patch costs far less on a planned maintenance window than it does as an emergency fix after an incident — rushed work skips the testing steps that would normally catch a second problem. 

→ A skipped preventive-maintenance cycle plays out the same way, just deferred to whenever the system finally forces the issue.

Skipping Documentation

Skipping documentation carries the same delayed bill. It looks free the day you skip it, until it becomes the reason a routine fix takes a new developer three weeks instead of three hours.

→ The short-term saving and the long-term cost are almost never paid out of the same budget cycle. That’s exactly why the cut gets approved in the first place.

When Does Software Maintenance Become More Expensive Than Modernization?

Every system eventually costs more to maintain than to modernize. The real question is recognizing that point before the decision gets made for you by an outage.

Software maintenance cost rarely announces the tipping point with a single number. It shows up as a combination of signals that, together, mean the calculus has already changed.

Our architects watch for these together, never any one in isolation:

• Maintenance costs climbing year over year with no plateau in sight
• A growing share of engineering hours going to bug fixing instead of new work
• Incident frequency and mean time to repair (MTTR) both trending up
• Updates that take longer to test and ship safely than they used to
• Security and compliance risk that keeps compounding
• A shrinking pool of specialists you can actually hire for legacy software maintenance
• New features that take noticeably longer to ship than they did two years ago

None of these alone proves much — every system has a bad quarter. The real signal is the combination holding for two or three consecutive quarters in a row.

There’s also a threshold our architects track directly: once the maintenance-to-development cost ratio crosses roughly 30-40% of the engineering budget, that’s treated as a signal for application modernization in its own right, separate from ordinary cost control. 

As Andrew Lychuk puts it, “When 70% of engineering time is spent just to keep the lights on, that’s not a technology story, it’s a business continuity story.”

In our experience, the tipping point rarely means the system stopped serving the business — it usually means the architecture stopped being affordable to keep serving it, because it was never built with the modularity or documentation to stay cheap to change.

The comparison that actually holds up projects both paths three to five years out. Modernization costs are visible and upfront. The status quo’s cost is just as real, but it’s spread across three places that never show up on a maintenance line item: 

• a slower time-to-market on everything the roadmap depends on, 
• real constraints on which new products or features the team can even take on, 
• and the ongoing cost of an engineering team spending its capacity on firefighting instead of building.

Run both projections side by side instead of comparing maintenance to modernization for a single budget year, and the “cheaper” option often changes once the full horizon is on the table.

Andrew Lychuk’s own framing draws the line clearly: maintenance is like servicing your existing trucks, and modernization is buying new ones — only once the old fleet can no longer do the job.

The signals above are how you find out which stage of that comparison you’re actually in.

Balancing Maintenance Costs with Innovation and Feature Development

We often refer to the balancing of enterprise software maintenance costs between maintenance and innovation, which may lead to a misconception when the two are viewed as competitors for the same budget. Igor Omelianchuk specifies: “Maintenance and innovation are not the opposing priorities; they’re different stages of your business evolution.”

Maintenance protects and stabilizes existing assets, and modernization prepares the business for what comes next.

Continual maintenance guarantees that current systems retain security, compliance, and focus on your business needs. Maintenance is necessary to prevent costly breakdowns and keep trust with clients and partners. Essentially, it creates a solid ground for further innovation efforts.

Modernization, in turn, comes as a natural next step. Innovation isn’t an auxiliary option, rather a necessity. Even properly maintained applications will once reach the end of their lifecycles, typically in 15-17 years. Then, significant modernization is required to restore performance and security.

The right approach: Strategic investment cycles

System maintenance and innovations should be balanced on a distance, not year to year. The correct strategy includes allocating separate budgets for modernization cycles every 5–10 years. It allows businesses to spread costs over time, ensure continuous improvement, and prevent financial emergencies caused by unexpected technical faults and rebuilds.

That said, modernization must start with careful analysis that addresses technical debt and hidden causes of stagnation. Andrew Lychuk warns: “Never rewrite yesterday’s mistakes in tomorrow’s code.” Otherwise, you will reproduce old inefficiencies in a new form. Competent modernization combines refactoring, reengineering, and rebuilding to make your systems scalable, flexible, and cost-efficient.

Conclusion

Maintenance can keep business systems afloat for a long time. But without modernization, it only delays the end of the software lifecycle. Every year, the maintenance cost of software expands with the accumulated tech debt, old team habits, and documentation gaps. These factors accelerate the rise of maintenance expenses until a full rebuild becomes unavoidable.

Organizations should recognize that maintenance alone is necessary, but not sufficient. Preventive support must be coupled with planned modernization cycles that reinvent architectures, fight tech debt, train teams, and match the system capacity with business demands.

A proactive strategy integrates modernization into maintenance to control expenses, reduce risk, and enable innovation. The essential goal of maintenance isn’t to make the old system work well today, but to prepare it for tomorrow.