The cost of technical debt: a longitudinal study of 100 startups.
We analyzed the codebases of 100 startups that hit a scalability wall (*)
The goal was not to find the most exotic bug. The goal was to find the most common, expensive, and preventable patterns of failure.
The results were almost identical across 85% of them. Here is what the data says.
The Timeline to Failure
Months 1–6: Everything worked. Fast releases. Happy customers. No time for architecture.
Months 7–12: Progress slowed. Strange bugs appeared. "Fix it later" became the motto.
Months 13–18: Every new feature broke three existing ones. Deployments became stressful.
Months 19–24: Hired more engineers. They just maintained the mess. No new features shipped.
After 24 months, reality left only two choices: rewrite the system from scratch or watch the system die slowly .
The Foundational Problems (Found in about 85% of codebases)
The problems were not exotic. They were basic, preventable, and catastrophic.
At the database level:
89% had no database indexes. Every request scanned thousands of records .
At the infrastructure level:
76% bought 8x the cloud capacity they needed. Average utilization was 13%. Burned $3,000–$15,000 per month on nothing .
At the security level:
70% had authentication vulnerabilities that would give any security engineer a heart attack .
At the quality level:
91% had no automated tests. Every deployment was a gamble. No one could "click a button and confirm that nothing else is broken" .
The True Cost (Not Just Engineering Hours)
For a 4-person engineering team, the math is brutal.
42% of engineering time is spent dealing with bad code. Over three years, that is $600,000+ in wasted salary .
The cost of a full rewrite is $200,000–$400,000.
Add 6–12 months of lost revenue during the rewrite.
Total loss per company: $2–3 million .
The highest cost is not the engineering budget. It is taking engineers away from building new features to fix old systems .
The AI Factor: Accelerating the Problem
AI coding tools (Claude, Cursor, Copilot) have lowered the barrier to "getting something running" to an unprecedented level. They have also significantly advanced the arrival of "slow death."
The code generated by models often seems usable. It even works. But it accumulates technical debt faster and makes it harder to judge quality .
The creativity and destructiveness of LLMs coexist. They can quickly turn an idea into code, but they may also mistake a temporary scaffold for a foundation. The cost often does not become apparent until month 18
What Actually Works
Avoiding tech debt does not mean building for massive scale from day one. That can be wasteful and prevent you from finding product-market fit.
The most cost-effective investment is made before writing the first line of code: spend two weeks on architecture.
Scale mindset: Ask "what will break at 10,000 users?" not "can it run with 100 users."
Automated testing from day one: If you cannot "click a button to confirm nothing else is broken," every deployment is a gamble .
Boring technology stack: React, Node, Postgres are not exciting. But they are easy to recruit for, have answers on Stack Overflow, and will not die at 2 am .
External architecture review in week one: Do not wait until month 12. It will be too late .
The principle is simple. Most technical co-founders and early engineers are excellent at writing code. But many have never designed a scalable architecture. It is like being an excellent chef but never having managed a restaurant kitchen during the dinner rush .
What I am curious about
When did you last look at your database indexes? Do you have automated tests? And where will your system break when user volume increases by 10 times?
Imed Radhouani
Founder & CTO – Rankfender
Data first.
(*) : those 100 codebases were analyzed by me, Imed Radhouani, as the CTO of Rankfender. The data comes from a longitudinal study I conducted by reviewing the codebases of startups I encountered through my network, consulting work, and public post‑mortems from 2020 to 2025.
This analysis forms part of Rankfender’s internal research into engineering best practices, technical debt, and scaling failures, which directly influences how we design our own platform architecture. Since this is proprietary research, I cannot share the full dataset, but the patterns identified are publicly observable across the industry and supported by standard engineering principles.
Replies
Missing indexes feels basic, not advanced. Is this inexperience or just delayed prioritization?
Rankfender
@scarlett_hayes1 Delayed prioritization. Every time.
The teams knew indexes existed. They knew they should add them. They just had 50 other things that felt more urgent. Inexperience plays a role early on. A junior engineer might not know. But the startups in our study had senior people. They knew better. They still skipped it.
The problem is that a missing index does not break anything at 100 users. The query runs in 50ms. Fine. At 10,000 users, the same query takes 5 seconds. The database cries. The team scrambles. And the fix is one line of SQL.
The cost of adding an index on day one is zero. The cost of adding it in production during an outage is a pager going off at 3am. The prioritization is not technical. It is emotional. The pain is not real until it is happening.
What is the simplest fix you've seen take down a production system?
Two weeks on architecture sounds simple but rare. Is urgency or overconfidence the real blocker?
Rankfender
@charlotte_reed1 Both. Urgency says "we need to ship now, we will fix it later." Overconfidence says "we are smart enough to figure it out as we go."
Urgency is honest. You have customers waiting. Investors watching. A launch date looming. Architecture feels like a luxury you cannot afford.
Overconfidence is more dangerous. You have built things before. You have shipped before. You think the rules do not apply to you. The debt will be different this time. It is never different.
The startups that failed in our study had both. The urgency was real. The overconfidence was fatal. They did not lack time. They lacked the humility to admit that two weeks of planning would have saved six months of pain.
What is the most expensive "we will figure it out" moment you have lived through?
@charlotte_reed1 @imed_radhouani I'm still figuring it out!
Interesting stats and a good set of questions to reflect on. On my end: database indexes are something I think about every time I add a new field or table, not as an afterthought. We do have automated tests and the rule is simple, every change ships with a test. As for where the system breaks under load, that's the harder one. We have monitoring in place that helps, but I wouldn't claim we have full visibility there. The broader point I keep coming back to is that even with LLMs helping ship features faster, the manual review process hasn't gone away. If anything it's more important, because the speed of output has increased but the responsibility for what goes into production hasn't.
UPD: we aren't a startup, but I guess that the same rules apply everywhere
Rankfender
@sk_uxpin That's the key insight. The speed of output has increased. The responsibility hasn't moved. If anything, it's heavier now because the code looks more polished but can be more subtly wrong.
The "every change ships with a test" rule is simple but rare. Most teams know they should. Most don't. The ones that do move faster over 18 months, not slower. The test debt compounds just like code debt.
The monitoring gap you mentioned is where most teams get surprised. They know where they've looked. They don't know where they haven't. That's the blind spot. The system doesn't break where you expect. It breaks where you forgot to look.
Not being a startup doesn't change the math. The cost of bad code is the same. The time to fix it is the same. The only difference is that bigger companies have more money to waste before they notice.
What's the most expensive "we didn't see that coming" moment you've had?
@imed_radhouani absolutely agreed! With regard to "Not being a startup doesn't change the math." - you're right, I just felt the need to mention the fact that we aren't a startup because setting up a healthy and strong process is taking some time, and startups usually don't have that time. And with regard to the most expensive "we didn't see that coming" moment - unfortunately, I don't have any interesting story to share, because I'm mostly working on building things and I'm not always in touch with the product teams or devops :)
Rankfender
@sk_uxpin Fair enough. The "not having time" part is real. Startups don't have the luxury of process. But that's exactly why the cheap, high-impact stuff matters more. Indexes. Tests for critical paths. A dead man's switch. Not a full QA department. Just the 5 things that break most often.
Not being in touch with product or devops is the quietest risk. You're building, but you don't know where the system is fragile. That's not a criticism. That's just how most teams work. The person writing the code is not the person getting paged at 3am.
The most expensive "we didn't see that coming" moments usually happen because of that gap. The code works locally. The tests pass. The review looks fine. Then it hits production and something breaks in a way no one anticipated. Not because the code was wrong. Because the environment was different.
@sk_uxpin, I have had trouble getting the LLM to keep the tests it runs before returning from the prompt when I can see the whole picture.
This is compounded by LLMs that don't use a version control system to track changes.
@sasconsul1 yeah, fair point - LLMs that can't work with VCS feel kinda weird nowadays
I have abstracted away from looking at the code and database at all, these models are more well read and able to write much better code in many domains that would not be reasonably mastered. In fact, the entire codebase is routinely thrown away as we wind up down the exact road you speak about, the technical debt cliff. instead of resuming, we extract an updated nuance and structure plan from the existing repo, even pseudo code for pieces we want direct control over the implementation of. Then we write a brand new repo codebase off the updated lessons and principles from the last round. we iterate multiple times a day, not rewriting code, but writing the repo from scratch based off the updated plan each time.
Rankfender
@justin_ram That is a radical approach. Most teams are afraid to throw away code. You treat it as disposable. The technical debt cliff becomes irrelevant because you never let the debt accumulate past one iteration.
The key insight is that the plan survives. The code does not. You extract the structure, the lessons, the principles from the last repo. Then you rebuild from scratch. The models are good enough now that regenerating the entire codebase is cheaper than maintaining the old one.
The "multiple times a day" part is the extreme end. Most teams are not there. But the direction is clear. The cost of generating code is dropping. The cost of maintaining bad code is not. At some point, rewriting becomes cheaper than refactoring.
Do you have automated tests for the generated code, or do you rely on the next iteration to catch the bugs?
@imed_radhouani great question. We utilize zero shot validation against the fully decomposed plan at a function or method level with an implementation matrix that mutates as we go through the cycles. Similar to recursive learning, the plan grows and evolves after each run, the errors are catalogued and added as analysis structure for each future run. The main benefit is the loss of lockin, sunk cost and hubris biasing the repo. We don’t care what the code looks like as long as it follows all our rules and returns results. Yes automated, but nothing can replace human in the middle. Most of the major advances have come from watching the output, the llms continually go down their own biased code path replacing any instructions at their whim. So we lock it down, force iteration and continue tightening our plan scope and constraints.
Rankfender
@justin_ram The "loss of lockin, sunk cost, and hubris" is the real unlock. Most teams keep bad code because they wrote it. It works. They are attached. Throwing it away feels like admitting failure. You have removed that emotion entirely. The code is just an output. The plan is the asset.
The biased code path problem is real. Models have preferences. They will drift toward them. The only way to fight it is tightening constraints and forcing iteration. The implementation matrix mutating each run is a smart way to encode what failed so it does not fail the same way twice.
Human in the middle still matters. You catch what the plan missed. You see the pattern the model cannot articulate. The watchful eye over the output is where the real learning happens.
How do you prevent the plan itself from becoming legacy debt? Do you refactor it the same way you refactor code?
@imed_radhouani yes sir exactly. We utilize a validator for our colloquially domain, design and function drift. After each commit it validates the repo and the plan. Errors in the repo update the plan, errors in the plan updates the matrix then we regenerate the plan off the matrix to make sure task decomposition and updated nuance does not get crowded by prior bias.
Incredible insights, Imed! As a solo founder building my MVP with Google AI Studio, the part about AI turning 'temporary scaffolds into foundations' hits painfully close to home. I'm trying to find PMF before hitting that 18-month wall.
I've heard the advice to 'treat AI as a junior dev, not an architect' and to enforce strict static analysis (like SonarQube). But from your study of 100 startups, what is the number one architectural habit the successful ones enforced from Day 1 to survive this AI-accelerated tech debt?
Rankfender
@justinlee020617 That is the exact right question,Justin! and the data from the study points to one clear answer.
The number one architectural habit that separated the startups that survived from the ones that collapsed was enforcing a strict and automated interface between the core domain logic and everything that touches the outside world.
It is boring, unsexy, and it works. The successful startups built a clear wall from day one. The wall separated the "what the product actually does" from the "how it talks to the database, external APIs, file storage, and the UI."
They called it the core domain—the pure business logic. It has no dependencies. It does not import a database driver. It does not call an API. It does not know what a webhook is. You can test it by itself. You can reason about it by itself. You can change the database from Postgres to MySQL without touching a single line of business logic.
The startups that failed? Their business logic was tangled with their framework, their ORM, and their API clients from the first week. When they tried to scale, add features, or onboard new engineers, every change became a nightmare. The scaffold became the foundation.
This rule is even more critical with AI-assisted development. The AI will happily generate a controller that fetches data, validates it, runs calculations, updates the database, and calls an external API all in one file. It looks efficient. It is efficient, until it is not. The success comes from the human architect who draws the hard lines before the AI writes a single line of code.
It costs nothing to do on day one. It costs everything to retrofit later.
Rankfender
@blakeskrable Here is a list of tangible, actionable good practices that startups can implement immediately, based on the data from the study.
1. Database Indexes: Add them before you need them.
The rule: any column used in a WHERE, JOIN, or ORDER BY clause needs an index. Not after the query slows down. Before.
Good practice: After adding a new field, run EXPLAIN on your most common queries. If you see "Seq Scan" on a table with more than 1,000 rows, add an index.
Tangible action: Schedule a 30‑minute weekly session to review slow query logs. If you don't have slow query logs enabled, turn them on today.
2. Automated tests: One test per feature, minimum.
The rule: no feature ships without at least one test that would break if the feature broke.
Good practice: Start with a smoke test for every API endpoint. GET /api/users returns 200. POST /api/users creates a user. That is it. You can add edge cases later.
Tangible action: Run git diff --name-only on your last PR. For every file changed, write one test that covers the main behavior. No exceptions for "small" changes.
3. Monitoring: Know what you don't know.
The rule: you cannot fix what you are not measuring. But you also cannot measure everything. Prioritize.
Good practice: Track the 5 most important user journeys. Signup. Login. Core action (search, message, purchase). Billing. Logout. Alert when any of them fails for more than 1% of requests.
Tangible action: Set up a dead‑man's switch. A cron job that hits your critical endpoint every minute. If it fails for 5 minutes, page someone. Not a dashboard. A page.
4. Code review: The "two‑correction rule."
From our earlier Claude thread. If an AI (or a junior engineer) makes the same mistake twice, stop correcting and start fresh. The context is polluted. The problem is not the mistake. It is the process that allowed it to happen twice.
Good practice: Keep a shared document of "rules Claude keeps breaking." Hardcoded values. Missing error handling. Unclosed connections. Run the document against every PR. If the same rule appears more than twice in a month, automate the check.
Tangible action: Add a pre‑commit hook that scans for common mistakes. No hardcoded API keys. No console.log in production code. No missing try/catch on database calls.
5. Architecture review: External eyes in week one.
The rule: if you have never had someone outside your team review your architecture, you are building in a vacuum.
Good practice: Before writing code, draw your system on a whiteboard. Identify single points of failure. If the database goes down, does everything go down? If the auth service fails, can anyone log in? Fix those before you build anything else.
Tangible action: Offer a free consultation to another startup in exchange for reviewing each other's architecture. Two hours. One whiteboard. No code. Just boxes and arrows.
6. Cloud costs: Tag everything from day one.
The rule: if you cannot tell which feature or customer is driving your cloud bill, you cannot optimize it.
Good practice: Every AWS, GCP, or Azure resource gets a tag. project:rankfender, environment:production, feature:raive. From day one. Not when the bill surprises you.
Tangible action: Run a cost report today. Group by tag. Any untagged resources? Create a ticket to tag them by end of week.
7. Technical debt register: Write it down.
The rule: if you do not track technical debt, you will not pay it down.
Good practice: Keep a shared document or GitHub issue label for "tech debt." Every time you say "we will fix this later," write it down. Date, description, estimated effort, impact.
Tangible action: Review the debt register every sprint. Pick one item. Fix it. Remove it. Watch the list grow and shrink.
The one thing you can do today
Pick the cheapest, scariest, most ignored item from this list. Do it before you write another line of code.
For most startups, that is enabling slow query logs. It takes 5 minutes. The insights will save you weeks of pain later.
The database index stat stood out 🤔 89 percent is surprisingly high, and it shows how often performance basics get skipped in early devoplment
Rankfender
@susie_johns That stat surprised me too. 89% is not a small oversight. It's a pattern. Most teams I've talked to say the same thing: "we will add indexes when we need them." Then traffic grows. Queries slow down. And suddenly every page load takes 3 seconds.
The cost of adding an index later is the same as adding it now. The cost of a slow query in production is much higher. But the urgency is not there until it hurts.
What's the most expensive performance fix you've had to make after launch?
the cost breakdown is painful but believable. Technical debt is rarely visible until it starts eating engineering time instead of product progress
Rankfender
@lara_bishop That is the quiet part. Technical debt is invisible until you notice that every new feature takes twice as long as the last one. Then you look back and realize you spent 6 months paying for shortcuts you took in the first 6 weeks.
The cost breakdown is not about the money. It is about the lost features you never got to build. The customers you never acquired. The competitors who passed you while you were debugging.
The most expensive code is not the code that broke. It is the code that worked just well enough to keep around while it slowly drained your team's energy.
Have you ever had to kill a feature because the debt underneath was too expensive to fix?
Great study — and the failure pattern maps almost perfectly onto financial models too.
The first 6 months of a model build look clean. Then deal teams start hardcoding overrides before credit committee. Circularity switches get turned off "temporarily." Someone adds a VBA macro because the iterative calculation is slow. By month 18, there are 40 hidden sheets, three different discount-rate assumptions living in different cells, and the DSCR calc depends on a value someone deleted two months ago — the error masked by a stale copy-paste.
The expensive part is not the rewrite. It is that the outputs were wrong on the last two deals that relied on the model, and nobody will know unless someone diligences the workbook line by line. I have seen 8-figure valuation errors surface exactly this way.
The structural fixes are cheap if you do them on day one: no VBA, explicit circularity switches with convergence tracking, one assumption cell per assumption (named ranges, never hardcoded references), DSCR/LLCR/IRR stacked in a clean outputs block separate from calculation sheets, and color-coded inputs (blue = assumption, black = formula, red = link) so stale cells reveal themselves at a glance.
"Fix it later" is the same failure mode whether you are shipping code or sizing debt — and the rewrite decision always comes at exactly the wrong moment, usually 48 hours before a credit committee.
Rankfender
@samir_asadov This is the most unexpected but perfectly aligned example I have seen. The 40 hidden sheets. The stale copy-paste. The DSCR depending on a deleted value. That is not a financial model. That is a time bomb that no one knows is ticking.
The 8-figure valuation error is the kind of mistake that never makes it into a post-mortem because the people who made it have already moved on. The cost shows up somewhere else. A bad deal. A missed opportunity. A partnership that falls through for reasons no one can explain.
The structural fixes being cheap on day one is the same in code and finance. Naming conventions. Separation of concerns. One source of truth. It costs nothing upfront. It saves everything later.
The "fix it later" moment always comes at the worst time. Right before a launch. Right before a funding round. Right before a committee. And the people who said "we will fix it later" are the same ones who now say "we do not have time to fix it now."
What is the one structural rule you refuse to break now because of a past failure?
This is painfully accurate. In many early-stage teams, the problem is not that the first version is “bad,” it is that nobody defines where the temporary code ends and the real foundation begins.
For me, the biggest red flag is when there are no automated tests and no clear ownership of database performance. Indexes, basic monitoring, and simple regression tests are not overengineering — they are survival tools.
AI coding tools make this even more important. They help you move fast, but without architecture discipline, they can also make the mess grow 10x faster.
Rankfender
@alpertayfurr That is the key distinction. Temporary code is fine. The problem is when no one marks where the temporary ends.
The teams that survived in our study had a clear rule. Any code written without a test had a ticket tracking it. Any query without an index was logged. They did not need to be perfect on day one. They needed to know where the debt was.
AI tools are accelerators. They will accelerate good practices or bad practices. The tool does not decide. The discipline does.
The teams that failed were not the ones with bad code. They were the ones with no visibility into how bad the code was.
What is the first signal you look for that tells you a team is building on sand?