Should You Use Vibe Coding in Production? Technical Debt, MVP Reality, and a Practical Playbook

TL;DR

Vibe coding can be great for MVPs and internal tools, but shipping vibe-coded systems to production without engineering discipline usually creates technical debt that shows up later as test gaps, fragile auth, messy architecture, risky dependencies, and rising maintenance cost. A consistent theme in community debates is that “landing pages are easy, auth and edge cases are hard.”
If you want vibe coding in prod, treat AI output as a draft, lock down guardrails, add tests early, and plan a deliberate refactor phase before scale.

What “vibe coding in production” actually means

In practice, “vibe coding in prod” usually means one of these:

1. MVP shipped directly to paying users with minimal refactoring
2. Prototype that becomes the foundation of the real product
3. AI-assisted code merged into an existing codebase by a team

The danger is not that AI writes code. The danger is that early code gets promoted into production without the usual quality gates, then the team is stuck paying interest on that decision.

Why MVPs feel easy, then production gets expensive

Vibe coding optimizes for momentum: you ask for a feature, you get something that runs, you iterate. That workflow is genuinely useful for prototypes. The trouble begins when the prototype becomes “the product.”

Community discussions often describe a steep difficulty curve after the simple parts. One Hacker News commenter summarizes it as “exponential levels of difficulty past the simple landing page,” citing basic auth as a common sticking point.

his is also where technical debt starts compounding.

The technical debt cliff: where it comes from

Here are the usual debt sources when an MVP is vibe-coded end-to-end:

1. Tests arrive late or never
   When you build by iterating until it “works,” the system tends to lack unit tests, integration tests, and regression coverage. That makes every future change risky and slow.
2. Architecture becomes accidental
   AI can generate plausible structure, but it does not reliably align with your long-term domain model, boundaries, and performance needs. The result is often duplication and inconsistent patterns. Nucamp’s overview points to duplication and hidden vulnerabilities as drivers of technical debt and higher long-term cost.
3. Auth and authorization are not “feature work,” they are product work
   Login is not just a UI. It is session handling, token hygiene, role checks, edge-case flows, password resets, rate limits, and secure defaults. That is why teams repeatedly feel the “landing page easy, auth hard” split.
4. Dependencies sprawl and maintenance cost grows
   Vibe coding encourages grabbing libraries to make the feature work now. Over time, you inherit version conflicts, security updates, and transitive dependencies. Nucamp highlights long-term maintainability concerns and recommends pairing AI output with coding standards and automated checks.
5. The code becomes the spec, and the spec becomes hard to reason about
   A sharp point from the HN thread is that once you accept generated code, future changes are negotiated with that code, not with a stable specification. One commenter notes that the generated code effectively becomes part of the spec, which can make iterative changes less predictable than no-code workflows.

“Is it prod-ready?” A decision framework that avoids wishful thinking

Ask these questions before you put vibe-coded software in front of real users.

1) What is the blast radius if something breaks?

Low blast radius: internal tooling, one-off scripts, personal automations
High blast radius: payments, user data, auth, regulated workflows

The higher the blast radius, the more you must treat AI output as a draft and enforce review, testing, and security controls.

2) Who owns maintenance?

A recurring concern in the HN discussion is maintenance handoff: a quick proof of concept gets credit, then another team inherits the remaining hard work to make it usable.
If you do not have clear ownership for fixes, updates, and incidents, do not ship it.

3) Are you paying for speed now with cost later?

If you skip tests, refactoring, and architecture planning, you are taking a loan. The interest is paid in:

• slower feature delivery
• higher incident frequency
• harder onboarding
• expensive rewrites

A practical playbook: how to use vibe coding without letting debt explode

If you want the benefits of AI-assisted building and still reach production responsibly, use a two-phase approach.

Phase 1: Vibe-coded MVP with guardrails

Goal: validate problem and solution quickly, but avoid irreversible mess.

Do these from day one:

• Add a thin testing spine: smoke tests, basic integration tests, and auth tests
• Pick a small set of approved dependencies and pin versions
• Enforce formatting, linting, and type checks in CI
• Document key decisions: data model, auth approach, deployment flow

Phase 2: “Production hardening” before scale

Goal: convert the MVP into something maintainable.

Hardening checklist:

• Refactor duplication and establish clear module boundaries
• Add regression tests around critical flows and edge cases
• Introduce observability: structured logs, metrics, tracing
• Threat model auth and data flows
• Do dependency hygiene: remove unused libraries, verify licenses, set update policy

Nucamp’s best-practices section aligns with this, recommending coding standards, automated checks, and treating AI output like a first draft with human oversight.

Common failure mode: “It works” becomes the acceptance criteria

One of the most useful mental shifts is this: working code is not the same as production-ready code.

Production-ready means:

• predictable behavior under load
• safe failure modes
• test coverage that prevents regressions
• maintainable structure
• clear ownership and runbooks

If your acceptance criteria are only “demo success,” the debt is already forming.

Conclusion

Yes, vibe coding can be used in production, but only if you keep the production discipline. The MVP phase is where vibe coding shines. The danger is letting MVP code graduate into production without a planned hardening cycle. Community debates regularly converge on the same line: simple surfaces are easy, but auth, edge cases, and long-term maintenance are where things get real.
If you want the speed benefits without the debt spiral, do it intentionally: guardrails in the MVP, then a dedicated production-hardening phase with tests, refactoring, dependency control, and clear operational ownership.

FAQ

Is vibe coding only for non-developers?

No. Many experienced developers use it to move faster, but they still review, test, and refactor. Skepticism often comes from what happens when teams accept suggestions without scrutiny and accumulate hidden debt.

What parts should not be vibe-coded for production?

High-risk areas like authentication, authorization, payments, and sensitive data handling should have strict review, strong tests, and often a more deliberate design process. The “70% problem” described in AI coding discussions is that the last 30% is where complexity and risk concentrate.

What is the safest way to adopt vibe coding in a product team?

Start with internal tools or low-blast-radius services, standardize a checklist and CI gates, and require a production-hardening step before scale. This keeps the upside of rapid prototyping while preventing the codebase from becoming a maintenance trap.