Every codebase past its first release carries some technical debt. The question that
actually matters for founders and CTOs isn’t “how do we eliminate it” — you can’t — it’s
how to prioritise technical debt sensibly: what to fix, when, and with whose time. This
piece is for anyone running an engineering team who’s watched a backlog of “we’ll fix it
later” items grow quietly until a release starts taking twice as long as it should.
You’ll get a practical way to make debt visible, prioritise technical debt against real
business impact, and fund the work so it actually gets done.
What technical debt actually is
Technical debt is any shortcut, gap or ageing decision in a system that makes future
change slower or riskier than it needs to be. That covers a wide range: a data model
that no longer fits how the product is used, a missing test suite that makes every
deploy nerve-wracking, an authentication system nobody wants to touch, or a dependency
so old it blocks a security patch. Some debt is taken on knowingly — a genuine, sensible
trade-off to hit a launch date. Some accumulates by accident, through changing
requirements or team turnover. Both are normal. The problem isn’t that debt exists; it’s
when nobody can see how much there is, where it sits, or what it’s costing.
Why it’s worth your attention as a founder, not just engineers
Technical debt is often talked about as an engineering-only concern, which is exactly
why it gets ignored until it’s expensive. In practice it shows up as business risk: slower
feature delivery, a rising bug rate, engineers who avoid touching certain files, and
onboarding that takes new hires weeks longer than it should. Left unmanaged, it’s one of
the most common reasons a growing product needs a costly rewrite rather than a
straightforward upgrade — the kind of situation we cover in our guide to legacy application
modernisation. Treating debt as a standing business cost, not an engineering
indulgence, is the mindset shift that makes everything below work.
Step 1: make it visible with a debt register
You can’t prioritise what you can’t see. The single highest-leverage move most teams
can make is keeping a simple, shared list of known debt items — a spreadsheet or a
labelled board column is enough to start. For each item, capture:
- What it is and where it lives (service, module, or page)
- Why it’s a problem — the concrete symptom, not just “messy code”
- Rough effort to fix
- Business impact if left alone (slower delivery, security exposure, support load)
Keep this register in the same tool your team already uses for sprint planning, not a
document that only gets opened once a quarter. Debt that lives in hidden engineering
notes never gets funded; debt that shows up next to feature tickets in planning gets
weighed against everything else competing for the same time.
Step 2: prioritise technical debt by impact versus effort, not gut feel
Once debt is visible, resist the urge to tackle whatever’s most annoying that week.
A simple two-axis view — business impact against effort to fix — does most of the work:
- High impact, low effort — fix these first. They’re the quick wins:
an unpinned dependency, a missing index causing slow queries, a flaky test blocking
deploys. - High impact, high effort — plan these deliberately, as their own
piece of work with a named owner, not squeezed into spare sprint capacity. - Low impact, low effort — fine to batch into normal feature work
when you’re already in that part of the code. - Low impact, high effort — usually not worth doing yet. Revisit if
circumstances change.
“Impact” should mean something concrete to the business: does this slow every release,
create a security exposure, or actively cost customers? If an item can’t be tied to a
real consequence, it’s worth questioning whether it belongs on the register at all.
For schema and data-layer debt specifically — often the highest-impact, hardest-to-see
category — our piece on database schema design that survives growth is a useful
companion to this framework.
Step 3: fund it with dedicated capacity
Prioritisation without budget is just a wish list. The teams that actually pay down
debt reserve a consistent slice of capacity for it — commonly somewhere in the 10–20%
range of sprint time — rather than hoping it happens between features. There are a few
ways to structure this, and the right one depends on team size and how urgent the debt
has become:
- A fixed percentage of every sprint, reviewed and adjusted quarterly
- A dedicated “fix-it” sprint every few cycles for larger items
- Pairing debt paydown with the feature work that touches the same code, so it isn’t
a separate ask
Whichever model you choose, report on it the same way you report on feature delivery —
monthly or quarterly is enough. Tracking progress does two things: it justifies the time
spent to stakeholders who don’t see the code, and it surfaces new debt before it becomes
urgent.
Step 4: prevent the easy-to-avoid debt
Some debt is worth accepting deliberately. Plenty more is avoidable. Consistent coding
standards, enforced through linting and formatting tools, close off a large share of the
debt that accumulates simply from different engineers making different small decisions.
Code review that explicitly checks for test coverage and readability — not just “does it
work” — catches the rest before it merges. None of this needs to be heavyweight; it needs
to be consistent, and it needs to be part of how the team already works rather than an
extra process bolted on.
Step 5: get an outside view when you can’t trust your own
It’s genuinely difficult to assess your own codebase objectively — the team that wrote
it is usually too close to see which shortcuts have become liabilities and which are
harmless. This is where a structured, independent review earns its keep, whether you’re
deciding what to fix next in a live product or evaluating a codebase you’re about to
inherit or acquire. If you’re planning a raise, an acquisition, or bringing in new
technical leadership, a technical due diligence review gives you an
honest, external read on real risk rather than internal assumptions. For teams simply
trying to get a handle on an existing product, a focused code audit does the same job at
a smaller scale — a clear, prioritised list of what needs attention and why, produced by
someone with no stake in defending past decisions. Delliux runs both as part of our
wider engineering services, alongside the build work that follows once
priorities are clear.
Common mistakes to avoid
- Treating debt paydown as a one-off project. It’s a discipline, not
a sprint you finish and move on from. New debt accrues continuously; managing it needs
to as well. - Letting “impact” mean “annoys engineers”. Some things genuinely are
just annoying, not risky. Keep the register honest about which is which, or you’ll
spend budget on comfort instead of risk reduction. - Waiting for a crisis to justify the work. By the time a slow
database or brittle auth system causes an outage, the fix costs far more — in engineering
time and customer trust — than it would have earlier. - Skipping the register because “everyone already knows”. They don’t,
particularly once the team grows past a handful of people, and unwritten knowledge
leaves with whoever holds it.
Technical debt isn’t a failure of engineering discipline — it’s a normal by-product of
shipping software that real customers use. What separates teams that stay fast from
teams that grind to a halt is whether debt gets tracked, weighed against real business
impact, and funded on a schedule, rather than left to surface as a crisis. If you’d like
a second opinion on where your own product stands, book a free consultation and we’ll
help you work out what’s actually worth fixing first.
FAQ
How much of our engineering time should go on technical debt?
There’s no universal number, but many teams find 10–20% of sprint capacity, reviewed
quarterly, keeps debt from piling up without stalling feature delivery. Startups moving
fast pre-product-market-fit may run lower; teams supporting paying customers at scale
often need more.
Is all technical debt bad?
No. Debt taken on deliberately — a known shortcut to hit a launch date, with a plan to
revisit it — is a normal trade-off. The debt worth worrying about is the kind nobody
chose and nobody’s tracking.
How do we know if our technical debt is serious enough to need outside help?
If releases are slowing down, the same few files cause repeated bugs, or your team can’t
agree on where the risk actually sits, an independent code audit is usually worth the
cost — it replaces internal disagreement with a prioritised, evidence-based list.
Should technical debt live in the same backlog as feature work?
Yes. Debt that’s tracked separately from the main planning process tends to get
deprioritised indefinitely. Keeping it visible alongside features is what gets it
funded.
Related: Feature Flags Best Practices for Startups — a good next read if forgotten toggles are quietly adding to your technical debt pile.
