🧱 What Is Technical Debt?
Technical debt is the consequence of prioritizing speed, constraints, or client value over long-term code health — and eventually needing to pay the price.
Examples:
- Prioritizing tight timelines over architecture
- Customizing for one client at the expense of others
- Skipping testing
- Writing poor code or infrastructure setup
- Running broken or manual change management processes
But not all technical debt is bad. Most teams carry some.
The question is: How much is it costing you — and when will you have to pay?
📌 My Real Example: Modernizing a Monolith
For 20 years, our org built everything as monolithic apps.
Now, my product introduces REST APIs and helps decouple them into microservices.
Sounds good in theory, right?
But that “decoupling” is basically a rewrite. And rewrites are expensive. Especially when the value isn’t immediately visible to customers.
So… how do you make a case for it?
💡 Our Approach: One App as a Test Case
We picked one struggling app with serious tech debt and ran a proof of concept. Here’s what we looked at:
The Problem:
- 10 Production incidents/year
- High testing effort — 1 full week × 4 times/year
- Monthly manual upgrades that often fail
- Slow performance for end users
The Cost of Keeping It Running:
- Incidents: €7,750/year
10 × €775 = €7,750 (L1–L4 engineers, one day each)
- Manual Testing: €3,600/year
4 weeks tester (€3,000) + PM time (€600)
- Upgrades: €1,300/year (DevOps, QA, PM time)
- Slow Performance: €22,875/year
50 users × 5 mins/day × €25/hr = €22,875
Total yearly cost of technical debt: €35,525
🔁 Cost of Rebuilding
We estimated ~6 months of effort from a small team (PM, Backend, Frontend, QA, DevOps, Architect).
Estimated rebuild cost: ~€97,000
So...Is It Worth It?
If we keep this app more than 2–3 years, yes.
Because what we’re buying is:
- Fewer incidents
- Faster delivery
- Happier teams
- A future roadmap without legacy blockers
🧰 Other Ways to Calculate Tech Debt
You don’t need to run a full cost breakdown like we did. Here are other ways to assess:
- % of incidents caused by legacy code
- % of time spent on maintenance vs. new features
- Developer satisfaction or burnout signals
- Time-to-onboard new developers
Even directional metrics can make a strong case.
🤖 Use AI to Help You Make the Case
Want help analyzing your product’s technical debt?
Try these prompts:
“Act as a senior software architect. Our product has [describe context]. What technical debt is likely present, and what is the long-term cost if we don’t fix it?”
“Help me structure a business case to refactor or modernize a legacy product. Include cost, risk, and team impact.”
“What are ways to visualize the value of tech debt reduction to non-technical stakeholders?”
“In our case, would you prioritize refactoring or building new functionality? Why?”
💬 Your Challenge This Week:
Pick one piece of technical debt in your product. Ask:
- What is it costing us annually?
- What’s the cost to fix?
- Is it worth it?
Even if the answer is “not yet,” just having the numbers = power.
Until next week,
Maria
