Technical debt is the compounding cost of shortcuts and aging code that slows feature delivery and increases downtime long before customers can see the cause. One widely cited estimate suggests fixing U.S. technical debt would cost $1.52 trillion, which shows the scale of the problem and why vendor evaluation must include debt signals, not just features.
Key Takeaways
- Technical debt is not “bad code.” It is accumulated change friction that gets more expensive over time.
- SaaS delivery pressure makes debt compound faster because change never stops.
- You can measure debt risk from the outside using DORA-style delivery and reliability signals.
- Downtime is not abstract. ITIC reports 90% of firms put hourly downtime above $300k, and 41% at $1M–$5M+.
- A buyer can reduce debt risk by demanding evidence: incident patterns, release discipline, and clear versioning/deprecation rules.
What is technical debt, and why does it matter in SaaS specifically?
Technical debt is the accumulated cost of software shortcuts and aging design decisions that make future changes slower and riskier. One widely cited estimate puts the U.S. bill for addressing technical debt at $1.52T, which is useful as a scale marker, not as proof about any single vendor.
Technical debt forms when teams ship by trading long-term maintainability for short-term speed. That can mean rushed development, legacy code kept “because it works,” skipped testing, or complex workarounds added just to make integrations behave. If the shortcut stays in place, the next change costs more than the last one.
SaaS intensifies this because the product is never “done.” The provider hosts the software and pushes updates continuously, which keeps pressure on delivery cycles. Continuous delivery turns every weak foundation into repeated rework. That rework shows up as slower releases and more fragile deployments.
From the provider side, debt limits agility and forces “cleanup work” that steals budget from innovation. It also increases reputational risk when outages and glitches become visible to customers. A high-debt codebase pushes teams into firefighting and away from product progress.
From the customer side, debt becomes a business constraint even if the customer never sees the code. Delayed features, unreliable performance, and rising support burden are common symptoms. Your vendor’s technical debt becomes your roadmap delay.
How do you measure technical debt risk without seeing the code?
You cannot measure a vendor’s technical debt directly from the outside, but you can measure debt risk using delivery and reliability signals like DORA metrics. DORA defines change fail rate as the ratio of deployments requiring immediate intervention, and it tracks recovery speed after failures as a stability signal.
Start with a simple rule: if changes frequently break production, the system is fragile. If recovery is slow, teams are buried in rework. High change failure rate plus slow recovery is a concrete external smell of compounding debt.
Next, validate what the vendor makes public. Status pages and incident histories show patterns: recurrence, time to restore service, and whether fixes stick. Release notes and changelogs show whether “emergency patches” dominate normal delivery. Transparency creates evidence you can audit without code access.
Tie this to business impact with real numbers. ITIC’s 2024 survey reports that 90% of firms say hourly downtime exceeds $300,000, and 41% report $1M to over $5M per hour. Treat these as survey benchmarks and ranges, not universal constants. Even one hour of downtime can exceed a year of “savings” from picking the cheapest vendor.
If you need one “outside-in score,” use two buckets. Bucket one is throughput: deployment frequency and lead time for changes. Bucket two is stability: change fail rate and recovery time. A vendor that improves throughput while stability degrades is buying speed with debt interest.
How can you reduce technical debt—and what should you ask before choosing a SaaS vendor?
Reduce technical debt by funding it like a real workstream: schedule refactoring, enforce quality gates, and prove outcomes with delivery stability metrics over time. As a buyer, reduce risk by demanding evidence of release discipline, operational transparency, and clear deprecation/versioning practices.
Here is the buyer-side logic. Vendors rarely advertise “technical debt,” but they will talk about how they prevent regressions, how they recover from incidents, and how they handle breaking changes. Ask for concrete artifacts: a sample incident report, a changelog slice, and the policy for API versioning and deprecations. If the vendor cannot show any of these, you are buying blind.
Now the product-side logic. A vendor that budgets refactoring and uses CI/CD quality gates can ship frequently without collapsing stability. DORA metrics give you a neutral language: how often they deploy, how long changes take, how often deployments fail, and how fast they recover. Stable delivery is the visible footprint of internal debt management.
A short independent-journalist note on why some delivery partners stand out. Teams that operate with modern practices tend to default to API-first design, strong documentation, and explicit handover paths, which reduces vendor lock-in. In practice, that means fewer “mystery integrations” and fewer irreversible dependencies. A partner’s discipline shows up in what they publish and how they explain their process.
In EdTech builds where interoperability and long-term maintainability matter, a Selleo E-learning software development company perspective emphasizes modern, API-first delivery and documentation that reduces vendor-lock-in risk over time.
What are the types of technical debt, with concrete examples buyers can recognize?
Types of technical debt include code debt, architecture debt, test debt, documentation debt, and dependency or infrastructure debt, and each creates external symptoms like regressions, slow releases, brittle integrations, and chaotic deprecations.
Code debt shows up as frequent hotfixes and “small changes that take weeks.” Architecture debt shows up as big features that require rewrites instead of extensions. Test debt shows up as releases that break core workflows. Documentation debt shows up as support tickets for basic usage and integration confusion. Dependency and infrastructure debt shows up as outages during upgrades and long incident recovery. Each type becomes visible as either change failure or slow recovery.
FAQ
1. What is technical debt in software development, in plain English?
Technical debt is the future cost you create when you ship software using shortcuts that make later changes slower and riskier.
2. How do you measure technical debt if you can’t see the code?
Measure debt risk with delivery and stability signals such as change fail rate and recovery time, plus public artifacts like incident histories and changelogs.
3. What are the most common technical debt examples customers actually feel?
Customers feel slow performance, recurring bugs after updates, delays in promised features, and downtime that interrupts work.
