What Does Software Maintenance Include, How Much Does It Cost, and How Should Businesses Plan for It?

Software maintenance is the ongoing care and improvement of a software system after it has been launched. It helps ensure the application continues to work as expected, stays secure, adapts to changing business needs, and remains compatible with the tools, platforms, and services it depends on.

Maintenance is not just about fixing things when they break. It is also about reducing risk, improving reliability, supporting users, and making thoughtful updates so the software continues to create value over time.

Software exists in a changing environment. Operating systems, browsers, cloud services, APIs, security standards, user expectations, and business processes all evolve. Even a well-built application can become less reliable or less useful if it is not actively maintained.

Without maintenance, small issues can turn into larger problems. Security vulnerabilities may go unpatched, integrations may break, performance may decline, and teams may start working around the system instead of relying on it.

A software maintenance plan can include:

• Technical support and bug fixes
• Security updates and dependency management
• Performance monitoring
• Hosting and infrastructure support
• Documentation updates
• User support

For systems that include AI or automation, maintenance may also include monitoring AI-supported workflows, reviewing outputs, updating prompts or instructions, checking data sources, maintaining integrations, and making sure the system continues to operate within the right business and security boundaries.

Yes. Websites, web apps, and mobile apps all need maintenance, but the work can look different depending on how the product is built and how people use it.

If a mobile app is not maintained, it may eventually stop working properly on newer devices or fail to meet current app store requirements.

Software maintenance is often grouped into four categories:

• Corrective maintenance — fixes bugs or issues.
• Adaptive maintenance — keeps software compatible with changing systems, platforms, APIs, or regulations.
• Perfective maintenance — improves existing features based on user needs or business priorities.
• Preventive maintenance — reduces future risk by improving code quality, security, performance, documentation, or infrastructure before problems occur.

With off-the-shelf software, the vendor is responsible for maintaining the core product. Your team may still need to manage configuration, integrations, data, permissions, and user adoption — but you are not responsible for the underlying application code.

With custom software, your organization has more control over how the system works, but that also means you need a plan for maintaining the application, infrastructure, integrations, security, documentation, and future enhancements.

A maintenance plan helps protect that investment.

Yes. AI features need maintenance because their usefulness depends on the quality of the data, instructions, workflows, permissions, and systems around them. Even when the underlying AI model is provided by a third-party platform, the business logic and implementation still need ongoing review.

AI features can become less effective if source data changes, prompts become outdated, users begin relying on the tool in unexpected ways, or connected systems change. Maintenance helps ensure AI features remain accurate, useful, and aligned with the business problem they were designed to solve.

An agentic workflow is a process where AI is not just generating an answer, but taking steps toward an outcome. That might include:

• Retrieving information
• Summarizing documents
• Routing a request
• Drafting a response
• Updating a system
• Triggering an approval
• Handing work off to a person

These workflows are powerful because they can reduce manual effort across multi-step processes. They also require thoughtful maintenance because they depend on several connected parts: data sources, system permissions, business rules, user inputs, model behavior, and human oversight.

Agentic workflows require ongoing maintenance because they operate across changing business processes, systems, and data. A workflow that performs well today may need adjustment when:

• A team changes its process
• An API changes
• A new exception appears
• A business rule needs to be updated

Maintenance helps ensure the workflow continues to make the right decisions, use the right information, respect the right permissions, and escalate the right cases to a human. For business-critical workflows, this kind of oversight is essential.

AI workflows can produce outdated, incomplete, or misaligned outputs if the surrounding system is not maintained. They may:

• Pull from stale data
• Apply outdated instructions
• Miss important exceptions
• Route work incorrectly

The risk is not only that the AI gives a bad answer. The larger risk is that a team begins relying on a workflow that no longer reflects the current business process. Regular maintenance helps catch those issues before they affect customers, operations, reporting, or decision-making.

Maintaining AI workflows usually involves:

• Reviewing how the workflow is performing
• Identifying where outputs are unclear or incorrect
• Updating prompts or instructions
• Improving source data
• Refining escalation rules
• Adjusting the workflow as business needs change

In some cases, maintenance may also include evaluation testing, logging, permission reviews, human-in-the-loop checkpoints, and documentation updates. The goal is to make sure the AI-supported workflow remains reliable, explainable, and useful in real-world conditions.

AI-enabled software should be maintained by a team that understands both the technology and the business context. That may include:

• Software developers
• Product managers
• Operations leaders
• Subject-matter experts
• Security or compliance stakeholders

The technical team can maintain the application, integrations, data pipelines, and infrastructure. The business team helps confirm whether the workflow still reflects the right process, rules, exceptions, and outcomes. For AI to stay useful, both perspectives matter.

Software maintenance costs vary based on several factors, including:

• Complexity of the system
• Number of users
• Quality of the existing codebase
• Hosting environment
• Number of third-party integrations
• Security requirements
• Level of support needed

A small internal tool may only need light monthly support. A business-critical platform with multiple integrations, user roles, data flows, AI features, or compliance requirements will usually need a more active maintenance plan. For a deeper breakdown of the factors that influence maintenance costs, read our guide to understanding and estimating software maintenance costs.

The biggest cost drivers are:

• System complexity and code quality
• Documentation quality
• Infrastructure and hosting
• Number of integrations
• Security and compliance requirements
• Release frequency
• User volume
• Required response time for issues

AI-enabled systems may have additional maintenance considerations, especially if they rely on third-party models, retrieval-augmented generation, agentic workflows, proprietary data, or business-critical automations. These systems may require monitoring, evaluation, prompt updates, workflow adjustments, and data quality reviews in addition to standard software support.

A business should budget for maintenance as an ongoing operating cost, not a one-time expense. If the software supports important workflows, customers, revenue, reporting, or internal operations, it needs a maintenance plan that reflects its role in the business.

The right budget depends on:

• How critical the system is to daily operations
• How often the system or its requirements change
• How much support users need
• How quickly problems must be resolved

A maintenance plan should be realistic enough to handle routine updates and flexible enough to respond when something unexpected happens.

Maintenance is usually separate from the initial cost of building custom software. The original build covers the strategy, design, development, testing, and launch of the application. Maintenance covers the ongoing work required to support and improve the application after launch.

It is best to discuss maintenance before the software goes live. That gives the team time to plan for documentation, monitoring, hosting, support responsibilities, security updates, and future enhancements.

Yes. Proactive maintenance can reduce long-term costs by:

• Catching problems early before they become expensive failures
• Keeping systems secure and reducing breach risk
• Improving performance and reliability over time
• Making future development faster and less risky

When a system is well-documented, regularly updated, and supported by a team that understands it, new features are easier to plan and implement. Maintenance helps protect both the current system and future investment.

If software is not maintained, it can become slower, less secure, harder to use, and more expensive to fix. Integrations may fail, dependencies may become outdated, bugs may accumulate, and users may lose confidence in the system.

Over time, neglected software can become a business risk. Teams may start building manual workarounds, data may become unreliable, and the cost of making future changes may increase significantly.

A business should consider a maintenance and support plan when software is important to:

• Daily operations
• Customer experience
• Revenue generation
• Compliance or reporting
• Internal efficiency

A support plan is especially useful when the system has multiple integrations, active users, sensitive data, AI-enabled workflows, or frequent business changes. It gives the business a clear path for resolving issues, making updates, and planning improvements.

Yes, but the new team usually needs time to review the codebase, infrastructure, documentation, integrations, and known issues before they can support the system effectively.

This review helps the team understand how the application works, where the risks are, and what level of maintenance is realistic. If documentation is limited or the codebase has technical debt, the first phase of maintenance may involve stabilization and knowledge transfer.

Before starting a maintenance plan, it is useful to review:

• Application codebase and architecture
• Hosting environment and infrastructure
• Third-party services and integrations
• Security posture
• Documentation quality
• User roles and permissions
• Known bugs and open issues
• Business-critical workflows

For AI-enabled systems, the review should also include prompts, data sources, retrieval logic, model providers, evaluation criteria, escalation paths, and any places where users rely on AI-generated outputs to make decisions or take action.

In many cases, the best first step is an assessment that identifies what can be maintained, what should be improved, and what may need to be replaced over time.

The best way to reduce maintenance risk is to keep the system understandable, documented, secure, and aligned with the business. That includes:

• Using reliable architecture and clean code
• Documenting key workflows and decisions
• Monitoring performance proactively
• Updating dependencies on a regular schedule
• Planning changes carefully before making them

For AI and automation, risk can also be reduced by defining clear boundaries, maintaining human oversight where needed, reviewing outputs, tracking exceptions, and making sure workflows are connected to current business rules and approved data sources.

LookFar Labs' maintenance and support service helps businesses keep custom software stable, secure, and useful after launch. Depending on the system, support may include:

• Bug fixes and security updates
• Performance improvements
• Hosting and infrastructure support
• Integration maintenance
• Documentation
• User support
• Small enhancements

For systems that include AI features or agentic workflows, LookFar Labs can also help monitor and refine how those workflows perform over time — including reviewing workflow behavior, updating prompts or instructions, maintaining data connections, improving guardrails, and making sure the system continues to support the right business outcomes.

Yes. LookFar Labs can support software built by another team after reviewing the application, codebase, infrastructure, documentation, and business context.

This review helps identify the system's current condition, known risks, support needs, and opportunities for improvement. From there, we can recommend a practical maintenance plan based on what the software needs and how important it is to the business.

LookFar Labs treats AI maintenance as part of the larger system, not as a separate experiment. AI features, automations, and agentic workflows are only useful if they stay connected to the right data, business rules, systems, permissions, and people.

Our approach focuses on practical value, reliability, and context. We help teams maintain the software around the AI, the workflows powered by AI, and the oversight needed to make sure those workflows continue to support real business needs.

The first step is to understand the software, how it is used, and where it creates value or risk for the business. That usually includes reviewing:

• The application and its architecture
• Infrastructure and hosting
• Integrations and dependencies
• Documentation and known issues
• User needs and future priorities

From there, LookFar Labs can help define a maintenance plan that fits the system's complexity and business importance. The goal is to give your team a clear, practical path for keeping the software stable today while making smart decisions about what comes next.