The sprint demo looks great. New features shipped, dashboards look green enough, and leadership is happy. Then the next quarter hits, and suddenly “small changes” take three sprints, incidents keep resurfacing, and nobody can explain why everything feels slower even though the team is working harder than ever.
This is what it looks like when feature delivery quietly outruns code health. It does not fail overnight. It fails by a thousand “we’ll refactor later” decisions.1
Many teams know this in theory but still end up in the same trap: short-term feature pressure, long-term drag on velocity, and a frustrated engineering org caught between the roadmap and a brittle codebase.2
The Hidden Cost Of “Just One More Feature”
When product, sales, and leadership push for speed, tech debt starts as a series of tiny compromises that feel harmless.
- “We can skip tests here. It’s a low-risk area.”
- “Let’s duplicate that logic. We don’t have time to extract a proper abstraction.”
- “We’ll clean this up after launch. Promise.”
Over time, those shortcuts add up to structural friction in the system. Technical debt becomes a tax you pay on every new feature, every release, and every incident recovery.
Teams typically see the impact in three places:
- Slower delivery as engineers wade through complex, fragile code and regressions.
- Reliability and trust issues as rollbacks, hotfixes, and performance regressions pull people away from roadmap work.
- Morale and onboarding drag as new engineers need weeks just to understand the system and experienced ones feel stuck “keeping the lights on.”
From the outside, it looks like a roadmap problem. From the inside, it is a maintainability problem. The two are inseparable.
Why Sustainable Velocity Demands Maintainability
A lot of leadership conversation frames the tradeoff as “velocity now versus maintainability later.” That framing is wrong. Maintainability is the mechanism that makes high velocity sustainable in the first place.3
When tech debt is unmanaged:
- Initial speed is high, but long-term velocity decays as complexity compounds.
- Change safety drops, so teams become more conservative and slow releases.
- The roadmap shifts from growth to maintenance as capacity is consumed stabilizing the system instead of extending it.
By contrast, teams that actively manage tech debt tend to see more stable velocity, faster onboarding, and more predictable releases.
Treating maintainability as separate from velocity usually leads to the worst outcome: constant pressure to ship on top of an increasingly fragile foundation. Sustainable speed comes from investing in the foundation on purpose, not whenever the team finds an empty afternoon.
The Investment Balance: New Features vs Tech Debt
Swarmia captures the risk clearly: overemphasis on short-term business outcomes can lead to technical debt that hampers future productivity and worsens developer experience.4 That overemphasis is rarely malicious. It is usually just invisible.
Most teams simply do not track how much time goes into:
- New feature development
- Maintenance, refactoring, and infrastructure
- Incident response and firefighting
Without that visibility, it is easy to believe that “we mostly work on roadmap features” even when a large part of the sprint is spent keeping systems running. The result is an accidental, unbalanced portfolio where new investments are obvious and code health investments are treated as discretionary.
High-performing teams flip that script and treat tech debt like a financial liability:
- It has a balance that can grow or shrink.5
- It has a cost of carry, as slower delivery and higher incident load.
- It gets explicit paydown plans that are aligned with business milestones, not vague promises to “clean up later.”
Recognizing When Tech Debt Is Choking Your Roadmap
Before you can rebalance, you need to know whether debt is actually the bottleneck. Some signals are obvious, but others are subtle.
Common indicators include:
- Rising lead time without added product complexity, as build and test times stretch out.
- Frequent rollbacks and hotfixes, signaling underlying fragility and operational “interest payments” on debt.
- High cognitive load when developers spend more time understanding existing behavior than implementing new changes.
- Long onboarding periods because the system is hard to reason about.
- Roadmap drift as more of each quarter is consumed by “stability” or “platform” work that was never explicitly planned as investment.
None of these symptoms are solved by pushing harder. They are systemic. The only way out is to treat the codebase itself as a product that needs roadmapping, ownership, and investment.
In this article, we'll cover 10 strategies to tackle these challenges.
Strategy 1: Fixed Capacity For Code Health
One of the simplest and most effective patterns is to reserve a fixed slice of capacity for technical improvements and stick to it ruthlessly.2
Several engineering leaders advocate dedicating a consistent percentage of each sprint to debt reduction, refactoring, and reliability work.6 This is not a magic number, but a practical starting point that:
- Prevents code health from getting perpetually deprioritized in sprint planning.
- Normalizes investment work as part of the job instead of “side projects” engineers do after hours.
- Creates a predictable budget that product can plan around, rather than ad hoc requests mid-sprint.
Two common patterns for this capacity:
- Fixed allocation each sprint, for example, reserving a fixed percentage of story points for tech debt, observability, and infrastructure improvements.
- Debt-to-feature ratio, where each major feature initiative carries a proportional technical improvement attached to it.
The specifics matter less than the discipline. The moment this budget becomes optional, feature pressure tends to consume it completely.
Strategy 2: Dedicated Refactor Sprints (Used Carefully)
Sometimes a codebase is so tangled that incremental work is not enough. In those cases, teams run explicit refactor or “stability” sprints to restore baseline health.
Done well, these sprints can create space for major architectural changes, reduce incident volume by addressing root causes, and reset team morale by allowing engineers to fix the things that slow them down every day.
Done poorly, they can turn into unspecific “cleanup weeks” with unclear goals, get canceled the moment external pressure appears, and create a false sense of security that encourages taking on more reckless debt afterward.
Good refactor sprints follow a few principles:
- Clear, outcome-based goals, such as reducing latency by a specific percentage or eliminating a legacy module that drives incidents.3
- Measurable constraints, limiting scope to a specific bounded context instead of “make everything better.”
- Explicit communication with stakeholders about how this sprint will unlock future features, reduce downtime, or improve customer experience.
Refactor sprints are a tool of last resort, not a primary operating mode. The healthiest teams rely more on continuous investment than on periodic heroics.
Strategy 3: Impact vs Effort For Tech Debt
Tech debt often loses in prioritization because it is framed as “engineering wants to clean things up” instead of “this change will unlock business impact.” A simple impact versus effort matrix helps neutralize that bias.6
Many product and engineering teams already use 2x2 matrices to prioritize features by impact and effort.7 Applying the same lens to tech debt items makes the tradeoffs visible.
For each candidate:
- Estimate impact on delivery speed, reliability, or customer outcomes.
- Estimate effort in terms of people and sprints required.
- Place it on the matrix as a quick win, strategic bet, fill-in, or long shot.
Using this framework side by side for features and tech debt makes conversations concrete. “This refactor will unblock three roadmap items and cut incident time in half” is easier to justify than “we want to clean up this messy area.”
Strategy 4: Make Tech Debt Visible In The Backlog
Untracked debt is rarely addressed. Healthy teams treat tech debt as first-class backlog citizens rather than private to-do lists in engineers’ notebooks.
Practical steps:
- Create explicit backlog items for tech debt with clear acceptance criteria and business rationale.
- Use tags or labels to categorize debt by area, severity, and type such as performance, security, maintainability, or observability.
- Review debt items in regular planning and refinement sessions alongside feature work.
Some organizations go further and maintain a “technical debt register,” similar to a risk register, that lists major debt items, their impact, their estimated “interest rate,” and clear ownership.
Visibility does not fix debt on its own, but it creates the conditions for better decisions. Once tech debt stands next to features in the same system, ignoring it becomes a deliberate choice rather than an accident.
Strategy 5: Integrate Debt Reduction Into Daily Work
Not all debt needs a project or a ticket. Some of the most effective teams operate with a simple rule: if you are touching an area, leave it a little better than you found it.
This mindset turns everyday work into an ongoing refactor:
- When you add a new endpoint, you extract duplicated validation logic into a shared helper.
- When you notice a flaky test you rely on, you stabilize it instead of working around it.
- When you see an outdated pattern in code you are modifying, you modernize it while context is still fresh.
To support this behavior without derailing delivery:
- Encourage small, focused refactors within feature pull requests rather than massive unrelated cleanups.
- Set review guidelines that favor incremental improvement over perfection.
- Give engineers limited autonomy to fix small issues without seeking extra approvals up to a certain size.
This distributed model complements planned investment work. Big problems still need explicit prioritization, but the overall entropy of the codebase decreases instead of increasing by default.
Strategy 6: Quantify The Cost Of Debt In Business Terms
Executives and product leaders rarely react to “we need to refactor” as strongly as to “we are losing deals and uptime.” Connecting tech debt to measurable business outcomes changes the conversation.
Practical moves include:
- Quantifying lost productivity by estimating how much time is spent inside fragile modules each week and translating that into cost.
- Measuring slowed innovation where features were delayed because the platform was too fragile or complex to extend.
- Tracking maintenance and incident costs and comparing them to the one-time cost of addressing root causes.
- Linking to customer impact using performance metrics, churn, support tickets, or satisfaction scores that correlate with reliability issues.
When you can say a refactor is one sprint of work that will pay back within months by reducing failed deployments or incident load, the decision stops being a matter of taste and becomes an investment discussion.
Strategy 7: Align Tech Debt Plans With Business Milestones
Treating technical debt as purely internal work makes it harder to justify. Treating it as an enabler for business milestones makes it part of the strategy.
Leaders who manage debt well often:
- Tie major paydowns to funding events, market entries, or product pivots.
- Use milestones like onboarding a certain number of customers or hitting specific revenue thresholds as triggers to replatform or re-architect components.
- Decide upfront which compromises are acceptable at each stage and which must be closed once the business validates the bet.
In other words, technical debt is not a random byproduct. It is a deliberate, time-bound tradeoff: accepting a shortcut now to gain speed, with a clear date and plan to pay it down later.
This requires visibility into both engineering work and business goals. When they are aligned, “keeping the lights on” becomes protecting the company’s ability to move fast in six months rather than a vague maintenance bucket.
Strategy 8: Protect Developer Experience
Technical debt is not only a delivery issue. It is also a developer experience issue.
Teams stuck in tangled systems report:
- Constant context switching between firefighting and feature work.
- Low confidence in changes, leading to anxiety around deployments.
- Reduced sense of ownership because the system feels too complex to influence meaningfully.
Over time, this erodes retention, hiring, and culture. Experienced engineers leave for environments where they can build instead of babysitting systems, word spreads that the stack is brittle and slow to work with, and the team shifts from proactive builders to reactive maintainers.1
Conversely, teams that invest in code health, observability, and clear ownership patterns tend to have higher engagement, stronger collaboration between product and engineering, and a culture that values outcomes over heroics.
Developer experience is not a soft benefit. It is a leading indicator of whether your current level of technical debt is compatible with your growth plans.
Strategy 9: Use Tools To Track And Prioritize Debt
Managing tech debt at scale is hard without tooling that surfaces where the problems are and how they evolve.
Common practices include:
- Using static analysis and code quality tools to highlight hotspots in the codebase.
- Integrating prioritization matrices into visual collaboration tools so engineers and product managers can jointly rank work.
- Adding tech debt tasks to agile boards alongside features and bugs to keep them visible and measurable.
Teams also benefit from correlating code changes with incidents, areas of high churn with areas of high complexity, and historical decision points like rushed releases with current pain points.
This kind of visibility turns anecdotal complaints about “terrible parts of the system” into actionable insights about modules that consistently cause incidents and delays.
Strategy 10: Build A Shared Language Around Tradeoffs
Ultimately, balancing feature delivery and tech debt is not a tooling problem. It is a communication problem.
Organizations that navigate this well:
- Avoid framing tech debt as “engineering versus product” and instead talk about a portfolio of investments with different time horizons.2
- Use a shared vocabulary for risk, impact, and effort so conversations become concrete instead of emotional.7
- Encourage engineers to explain constraints in business terms and encourage product managers to engage with technical tradeoffs.
The goal is not to eliminate tech debt. Some debt is strategic and necessary, especially in early stages where learning speed matters more than elegance.5 The goal is to ensure every shortcut has a clear purpose, an owner, and a plan.
When that happens, “keeping the lights on” stops being a vague category of work and becomes a deliberate, funded part of how the organization sustains its velocity.
How One Horizon Helps Make The Balance Visible
Most of these strategies rely on one critical capability: seeing where time actually goes and how work connects to outcomes.
If your team lives across tools like Slack, Google Calendar, Linear, Jira, and GitHub, it is easy for the story behind the code to fragment. Meetings, incidents, roadmap discussions, and implementation details all happen in different places, so the picture of how much capacity truly goes into new features, tech debt, and “keep the lights on” work stays blurry.
One Horizon is built to restore that story by connecting the dots between conversations, plans, and changes. It helps teams:
- Track the balance between investment in new development and maintenance, so overemphasis on short-term outcomes becomes visible before it hurts velocity.
- See how work flowing through the tools you use ties back to goals, incidents, and long-term initiatives without adding more status meetings or manual reporting.
- Give engineering and product leaders a shared view of where debt is accumulating, which investments unlock the most future speed, and how current decisions shape the roadmap.
The practices in this article work even with spreadsheets and sticky notes. But when the stakes are high and the systems are complex, having a clear, connected view of your engineering investments turns balancing feature delivery and tech debt from an art into a repeatable habit.
If your team feels like it is always shipping and somehow still slowing down, it might be time to look at the balance. One Horizon can help you see it.
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References
Footnotes
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Okoone (2025). "How technical debt quietly kills product speed and what to do about it." https://www.okoone.com/spark/strategy-transformation/how-technical-debt-quietly-kills-product-speed-and-what-to-do-about-it/ ↩ ↩2
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CTO Magazine (2025). "Tech Debt vs. Feature Velocity: How to Find the Right Balance." https://ctomagazine.com/tech-debt-vs-feature-velocity-balance/ ↩ ↩2 ↩3
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Miro (2024). "How to reduce technical debt and build a stronger foundation." https://miro.com/agile/how-to-reduce-technical-debt/ ↩ ↩2
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Swarmia (2025). "How to balance engineering investments — and not just keep the lights on?" https://www.swarmia.com/blog/balancing-engineering-investments/ ↩
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TechClarity (2025). "Why Technical Debt is Sometimes a Strategic Asset." https://www.techclarity.io/article/strategic-technical-debt-startup-scaling ↩ ↩2
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Kodus (2025). "Technical debt vs. new features: how to set priorities." https://dev.to/kodus/technical-debt-vs-new-features-how-to-set-priorities-38pc ↩ ↩2
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VFunction (2025). "How to Reduce Technical Debt: Key Strategies." https://vfunction.com/blog/how-to-reduce-technical-debt/ ↩ ↩2
