Comparisons
Pairs that get conflated in real conversations and in real pull requests — coupling and cohesion, abstraction and indirection, refactoring and rewriting, debt and mess. Neither column wins; what decides is the requirement. Each record leads with the confusion, because the confusion is the reason the record exists.
Transaction Script vs Domain Model
These are usually argued as maturity levels, with transaction script as the thing beginners write and a domain model as the thing you graduate to. That framing is wrong in both directions. Plenty of successful systems are transaction scripts on purpose, because their rules genuinely are thin, and a domain model imposed on a CRUD feature produces entities with no behaviour, a service layer that does all the work anyway, and three files where one procedure would have said everything. The reverse mistake is more expensive but slower to appear: a domain with real interacting rules, expressed as scripts, ends up with the rules copied into every use case that touches them, so "can this subscription be paused" is answered slightly differently in six handlers and the differences are all bugs. The signal that you have crossed the line is not the size of the codebase or the number of tables — it is that you find yourself re-deriving the same eligibility question in more than one place, or that a change to one rule requires you to read every handler to be sure. And the crossing is not a rewrite: extracting one aggregate with its invariants, and leaving the rest as scripts, is a legitimate permanent state.
CRUD-shaped features, reporting, integrations, admin tools, and anything where the rules are thin and mostly amount to validation plus a write. Also the right first shape for a feature whose rules you do not understand yet.
When the same handful of concepts carry rules that interact — eligibility depending on state depending on history — and those rules change often enough that finding all their sites is the expensive part of every change.
| Dimension | Transaction script — a procedure per use case, top to bottom | Domain model — behaviour and invariants living on objects that represent the business |
|---|---|---|
| Where the rules live | Inline in the procedure that needs them | On the types that own the concept |
| Reading one use case | Trivially easy — it is one function, top to bottom | Requires knowing the model; the flow is spread across objects |
| Adding a use case | Add a procedure; nothing else moves | Usually reuses existing behaviour; sometimes needs model change |
| Changing a shared rule | Find every procedure that encodes it | One place, and callers cannot bypass it |
| Invariant enforcement | By convention, in each caller | Structural — the object refuses invalid states |
| Onboarding cost | Near zero | Real: the model is a language that has to be learned |
| Typical failure | The same rule, six subtly different versions | An anemic model — objects with getters, logic still in services |
| Migration between them | Extract one concept at a time; both can coexist indefinitely | Dissolving a model back into scripts is rarely worth doing |
The same question, five structures
Layered, hexagonal, clean, vertical slice and modular monolith — compared without naming a winner, and with the block that says where the comparison stops being true.
A tidy table implies an equivalence that does not exist. These are not five points on one axis: layered, hexagonal and clean are statements about dependency direction; vertical slice is a statement about directory grouping; modular monolith is a statement about deployment and module visibility. Most real systems combine several. The where this comparison misleads block on every row is the part worth reading, and it is the reason this page names no winner — none of these is mandatory, and a team that adopts one because a diagram was pretty has skipped the only question that decides it.
These are not five points on one axis. Layered, hexagonal and clean are all statements about *dependency direction*; vertical slice is a statement about *directory grouping*; and modular monolith is a statement about *deployment and module visibility*. You can — and most real systems do — combine several of them: a modular monolith whose modules are vertical slices, each with a hexagonal boundary at its edges. Comparing them as alternatives is the single most common way this table is misread.
The word complexity is doing two jobs here. Layered and vertical slice are cheap to *set up* and can be expensive to *live in* once the codebase is large; clean and hexagonal are expensive up front and their cost is roughly flat afterwards. Any comparison made at week one inverts the ranking you would get at year three, and neither reading is dishonest — they are answering different questions.
Locality is a property of whether the boundaries match the change history, not of the style name. A vertical slice cut along the wrong capability lines has terrible locality, and a layered codebase with only one real feature has perfect locality. The only honest way to compare these columns is to open the last thirty merged changes in your own repository and count the directories each one touched.
Every column here is a claim about *fast tests without infrastructure*, and any of the five achieves that as soon as dependencies are injected rather than constructed — which is a separate decision none of these styles owns. What differs is the default test boundary each one nudges you toward, and that matters more than the theoretical maximum: layered nudges toward class-level tests with mocks, vertical slice toward feature-level tests, and the difference shows up in how much your suite has to change during a refactor.
The columns are answering to different pressures: hexagonal responds to *external* volatility, clean to *domain* richness, vertical slice to *feature count*, and modular monolith to *team count*. A system can score high on one pressure and low on the rest, which is why picking a style from a comparison table rather than from your own pressures is how teams end up with four rings around a CRUD application.
Team fit is not a tiebreaker, it is often the deciding factor, and it is the one this table cannot capture. A structurally superior design that the team will not maintain under deadline degrades into the worst version of itself — half-applied clean architecture, with some code respecting the ring rule and some not, is harder to work in than consistent layering. The right question is which of these your team will still be following in eighteen months.
This row compares familiarity, not intrinsic difficulty, and familiarity is a property of the industry at a moment in time rather than of the design. Layered wins here largely because it is what most people have seen, which is an argument for it and also the reason it is over-applied. It is also worth separating cost-to-read from cost-to-contribute-correctly: vertical slice inverts on those two, and the table's single number hides it.
Ceremony is only waste when the feature did not need it, and every column here is right for some features and wrong for others in the same codebase. That is the actual finding of this row: a uniform ceremony level applied to every feature guarantees you are overpaying on the simple ones or underpaying on the complex ones. Allowing different features to carry different amounts of structure is more valuable than choosing which column to standardise on.
shared/ directory that grows back under a new name, or slices that each reimplement infrastructure slightly differently.Every one of these degradations is the style's own strength taken past the point where it repays — which is why none of them can be called wrong, and why §138 forbids teaching any as mandatory. What makes a codebase bad is not the column it started in but the absence of anyone asking whether the structure still matches the changes arriving. The right comparison to make is between your current structure and your last thirty changes, not between two names on a page.