Vibe Coding vs Traditional Development: When to Use Each
Vibe coding is not a replacement for traditional software engineering. It is an additional mode of working. Here is when each approach makes sense and how to choose.
Two modes, not two camps
The online discourse around vibe coding tends toward extremes. One side says it will replace traditional development entirely. The other dismisses it as toy-level prototyping that cannot produce real software. Both positions are wrong. The reality is that these are two modes of working, and competent developers switch between them depending on the task at hand.
I use both every day. Some tasks are better suited to vibe coding. Others demand the slower, more deliberate approach of writing code by hand with full mental engagement. Knowing when to use which is the actual skill.
When vibe coding wins
Vibe coding excels in situations where the pattern is well-understood and the primary cost is typing time rather than thinking time.
CRUD endpoints and boilerplate. If you have built fifty REST endpoints in your career, the fifty-first is not intellectually challenging. You know what it should look like. Describing it to an AI and reviewing the output is faster than typing it yourself.
Standard UI components. A settings page with form fields, validation, and a submit handler. A data table with sorting and pagination. A modal with confirmation logic. These follow established patterns. Vibe coding handles them in minutes.
Configuration and infrastructure. Writing a GitHub Actions workflow, a Dockerfile, a Caddy configuration, or a database migration. The patterns are documented, the AI knows them well, and the risk of subtle bugs is low because the output is either correct or it fails immediately.
Prototyping and exploration. When you are not sure what the right approach is, vibe coding lets you try three different implementations in the time it would take to carefully build one. You throw away the bad ones and refine the good one.
When traditional development wins
Traditional development is the better choice when the cost of a subtle bug is high, when the problem requires novel thinking, or when you need a deep mental model of the code to maintain it later.
Security-sensitive code. Authentication flows, payment processing, cryptographic operations, input sanitization. These demand line-by-line attention because the failure modes are invisible until exploited. AI agents do not think adversarially, and a single missed check can create a vulnerability.
Complex state management. Systems with intricate state transitions, race conditions, or concurrent access patterns. The kind of code where you need to hold the entire state machine in your head to reason about correctness. AI tends to produce code that works for the happy path but misses edge cases in concurrent scenarios.
Performance-critical paths. Hot loops, memory-sensitive algorithms, systems that need to handle thousands of operations per second. These require profiling, benchmarking, and careful optimization that AI cannot meaningfully contribute to yet.
Novel algorithms or architecture. If you are solving a problem nobody has solved before (or solving it in a fundamentally new way), the AI has no training data to draw from. You are on your own, and that is fine.
A practical heuristic
The decision framework I use is simple. I ask myself one question: “If the AI gets this 90% right, is the remaining 10% dangerous or just annoying?”
If the remaining 10% is merely annoying (a wrong variable name, a slightly suboptimal query, a missing null check that tests will catch), I vibe code it and fix the edges during review.
If the remaining 10% could be dangerous (a security hole, a data corruption scenario, a race condition that only manifests under load), I write it by hand with full attention.
The hybrid approach in practice
On a typical workday, my split is roughly 60% vibe coding and 40% traditional development. Here is what that looks like for a concrete example.
Last week I added a webhook system to one of my products. The breakdown:
- Vibe coded: The HTTP handler, request validation schema, the database table for storing webhook configurations, the admin UI for managing webhooks, the retry logic with exponential backoff. All well-understood patterns.
- Hand-written: The signature verification (HMAC-SHA256), the concurrency limits to prevent a single webhook from consuming all worker threads, and the error handling for partial failures in batch deliveries.
Total time: about 4 hours. Without vibe coding, the same feature would have taken 10-12 hours. Without the careful hand-written parts, it would have shipped with at least two security issues and a potential denial-of-service vector.
Common mistakes
Using vibe coding for everything. This leads to codebases where the developer does not fully understand their own system. When something breaks in a novel way, debugging becomes significantly harder because you lack the mental model that comes from having written the code deliberately.
Refusing to use vibe coding at all. This is just leaving productivity on the table. If you have built the same pattern dozens of times, typing it out manually is not adding value. It is just slow.
Not reviewing AI output carefully enough. The biggest risk with vibe coding is treating it as “correct by default.” AI-generated code needs the same (arguably more) scrutiny as code from a junior developer on your team. Read it. Question it. Test it.
The real skill
The developers who get the most from AI tooling are not the ones who use it for everything. They are the ones who have developed good judgment about when it helps and when it hurts. That judgment comes from experience with both modes of working.
If you are new to vibe coding, start with the easy wins: boilerplate, configuration, standard UI. As you build confidence in reviewing AI output, gradually expand the scope. Keep the high-stakes code manual until you trust your review process completely.
More on how I combine AI tooling with traditional engineering: X.