Introduction
This article exists as a structural reference point. Not a checklist, not a playbook, and not a reaction to any single algorithm change. It documents how large websites actually behave once they pass the stage where intuition and manual control are enough.
I am writing from the perspective of someone who has had to diagnose why indexing slowed down, why internal links stopped producing effects, and why updates that should have worked no longer did. The patterns described here are not theoretical. They emerge repeatedly once sites cross a certain size and operational complexity.
After working with sites that range from a few hundred pages to hundreds of thousands, I no longer think of URL structure as a cosmetic decision. At small scale, almost any structure survives. At scale, most of them don’t. Not because Google is hostile, but because the internal graph becomes expensive to crawl, expensive to interpret, and inconsistent over time.
The failure mode commonly described as a “flat URL structure” is rarely about folders or slashes. It is about the absence of a stable taxonomy and the absence of enforced page roles. Once those are missing, the site behaves as if it were flat, even if the URLs look hierarchical.
URL shape is not architecture
Search engines do not reward folders. They reward predictability. John Mueller has repeated this in different forms over the years: URL depth by itself is not a ranking factor. What matters is whether important pages are consistently discoverable and reinforced internally.
In practice, I see this mismatch constantly. Teams introduce deep URL paths assuming that hierarchy emerges automatically. It doesn’t. If navigation, internal linking, and content governance don’t follow the same logic, the folder structure becomes decorative. Crawlers still experience the site as a dense, noisy graph.
What actually breaks when a site stays flat
On sites above roughly 5–10k indexable URLs, three degradations appear with high regularity.
Role collision. Pages designed at different times start competing for the same query classes. In logs and Search Console this surfaces as ranking volatility and unstable impressions. Internally, it shows up as duplicated link patterns: multiple pages receiving similar anchor distributions without a clear hub.
Crawl inefficiency. In log samples from large content sites, I routinely see 20–40% of Googlebot requests spent on low-value pagination, parameterised URLs, or legacy templates. This is not because Google “wastes” crawl budget, but because the architecture does not signal priority clearly.
Signal dilution. Internal links accumulate faster than they are pruned. The median number of internal inlinks per page grows, but the variance collapses. Important pages stop standing out. Empirically, this correlates with slower reindexation after updates and weaker response to internal linking changes.
These are not theoretical issues. They are measurable in crawl stats, indexation latency, and link distribution graphs.
Flat vs hierarchical: operational differences
| Dimension | Flat publishing model | Hierarchical taxonomy model |
|---|---|---|
| Page roles | Implicit, overlapping | Explicit, mutually exclusive |
| Crawl paths | Long, variable | Short, repeatable |
| Internal links | Diffuse, opportunistic | Concentrated via hubs |
| Indexation latency | Highly uneven | More predictable |
| Governance cost | Low initially, high later | Higher initially, lower at scale |
The key difference is not aesthetics. It is cost distribution over time. Flat models are cheap early and expensive later. Hierarchical models front-load thinking and reduce long-term entropy.
Intent as an architectural primitive
Intent-driven architecture is often misunderstood as keyword mapping. That interpretation collapses under scale.
Operationally, intent is a mapping between:
- a recurring class of queries,
- a single page role responsible for resolving that class,
- and a defined position in the internal graph.
This framing aligns with classic information retrieval thinking. As Andrei Broder noted in his early work on query intent classification, different intents require different document types. Modern search systems still behave this way, even if the labels have changed.
When intent is not enforced architecturally, sites drift. New pages are added because a query exists, not because a role is missing. Over time, the site accumulates mirrors rather than extensions.
Hierarchy as routing, not depth
A hierarchical taxonomy functions as a routing layer. Its job is to constrain where new content can live and how it is discovered.
In practice, I look for a small number of durable top-level partitions. On technical and editorial sites, this is often fewer than teams expect — usually three to six. Beyond that, the taxonomy stops guiding behaviour and starts fragmenting it.
Whether this hierarchy is encoded in folders, breadcrumbs, or hub pages is secondary. What matters is that crawlers can reach core pages within a limited number of hops, and that those hops are consistent across sessions.
Crawl paths: what bots actually see
Architecture diagrams lie. Logs don’t.
When you inspect crawl paths over time, hierarchical sites show repetition. The same hubs are revisited frequently. Important URLs are discovered early in sessions. Flat sites show variance: long tails of rarely crawled URLs and bursts of activity around incidental surfaces.
In multiple audits, I’ve seen indexation delays of several weeks correlate directly with average crawl depth exceeding 6–7 hops for key pages. This is not a hard threshold, but it is a recurring pattern.
Authority flow and decay
Internal authority does not disappear. It diffuses.
As the number of internal links grows, contrast weakens. This is not speculative; it is visible in graph metrics. Pages that once sat clearly above the median in internal PageRank-like measures slide back toward it as new links are added indiscriminately.
Hierarchical hubs slow this decay by acting as signal concentrators. They do not eliminate it. Without ongoing discipline, even the best-designed taxonomy degrades.
Governance is where most architectures fail
Most architectures fail quietly. Not because the initial design was wrong, but because no mechanism exists to prevent drift.
New categories appear without deprecating old ones. Pages are kept “just in case”. Temporary landing pages become permanent. Within a year, the original hierarchy exists mostly in documentation.
This is why taxonomy design without governance is incomplete. Redirects help, but they are reactive. The real control point is publication rules: what roles exist, where they live, and what happens when they overlap.
Architecture inside a broader SEO system
Hierarchical taxonomy and intent-driven design are not SEO tactics. They are structural decisions that make other SEO work predictable.
If you want to see how this fits into a complete technical SEO setup — crawling, indexation, internal linking, and monitoring as one system — I’ve outlined that context in a broader piece on Website SEO optimisation, which frames taxonomy, crawlability, internal linking, and monitoring as parts of a single operational system.
The pattern, repeated across industries, is consistent. Large sites do not collapse because of algorithm updates. They collapse because their internal graph becomes noisy, expensive, and ambiguous. Hierarchical taxonomy does not guarantee success. It simply keeps the system legible long enough for everything else to work.
Conclusion
Flat structures fail at scale not because they are flat, but because they allow ambiguity to accumulate unchecked. Hierarchical, intent-driven architectures do not eliminate uncertainty, nor do they prevent mistakes. What they do is constrain where mistakes can propagate.
When page roles are explicit, crawl paths are short, and internal signals are routed through stable hubs, the site remains interpretable — for crawlers, for users, and for the teams maintaining it. When those constraints are missing, every new page slightly increases entropy.
This is why architecture should be treated as infrastructure rather than optimisation. You rarely notice it when it works. You notice it only when it quietly stops working, and everything downstream becomes harder to explain.