165 CloudFlare Nodes in 85 of totally 130 Countries

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IATA Code PHX: 1 Node with 6 IP Addresses

CF Code	City	Country	CF IP-Country	Node IP
PHX	Phoenix, AZ	United States (US)	United States (US)	104.23.195.101
PHX	Phoenix, AZ	United States (US)	United States (US)	172.69.74.222
PHX	Phoenix, AZ	United States (US)	United States (US)	172.69.74.223
PHX	Phoenix, AZ	United States (US)	United States (US)	172.69.74.238
PHX	Phoenix, AZ	United States (US)	United States (US)	172.69.74.239
PHX	Phoenix, AZ	United States (US)	United States (US)	172.69.74.245

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Does Caching Hide Architectural Inefficiency?

This post aims to unpack a common WordPress performance question using a neutral, first-principles lens. It is not a product recommendation; it is an attempt to separate layers, costs, and trade-offs so the discussion can be more precise.

Caching as a lens

Caching is one of the most effective techniques in web performance. It can turn repeated expensive computations into cheap lookups.
At the same time, caching can make it harder to see where the cost actually comes from.
A practical way to keep the debate grounded is to define what you mean by “faster.” For some teams, the business metric is conversion; for others, it is crawl efficiency or editorial workflow. Different goals favor different interventions.
When someone reports a big improvement, it helps to ask: did they reduce CPU work, reduce I/O, reduce network transfer, or simply change what was measured?

What caching changes

With a page cache, the primary path for many requests becomes: HTTP request → cache lookup → response.
The expensive WordPress lifecycle is skipped most of the time. That is the point.
Try to avoid all-in narratives. Most sites need a combination of techniques; the useful part is knowing which technique addresses which bottleneck.
A practical way to keep the debate grounded is to define what you mean by “faster.” For some teams, the business metric is conversion; for others, it is crawl efficiency or editorial workflow. Different goals favor different interventions.

What caching does not change

Caching does not remove the complexity of the uncached path. It shifts it into fewer, but still critical, moments: cache misses, cache invalidation, personalization, and edge cases.
If your stack cannot handle those moments reliably, the site may feel “fast” until it suddenly does not.
When someone reports a big improvement, it helps to ask: did they reduce CPU work, reduce I/O, reduce network transfer, or simply change what was measured?
A practical way to keep the debate grounded is to define what you mean by “faster.” For some teams, the business metric is conversion; for others, it is crawl efficiency or editorial workflow. Different goals favor different interventions.

Two common misunderstandings

First: assuming a fast cached homepage means the server is healthy. It may simply mean the cache is warm and the hit rate is high.
Second: assuming low backend load during normal traffic means the architecture is efficient. It may mean the cache is masking a high per-request cost that would appear during a burst of misses.
If you are comparing approaches, control what you can: same origin server state, same test location, same cache state, and multiple samples. Otherwise, you are mostly measuring randomness.
When someone reports a big improvement, it helps to ask: did they reduce CPU work, reduce I/O, reduce network transfer, or simply change what was measured?

A neutral way to separate effects

Think in two numbers: the cost of a cache hit and the cost of a cache miss. Both matter.
A system is resilient when cache misses are not catastrophic and cache hits are cheap enough that you still have headroom.
A practical way to keep the debate grounded is to define what you mean by “faster.” For some teams, the business metric is conversion; for others, it is crawl efficiency or editorial workflow. Different goals favor different interventions.
When someone reports a big improvement, it helps to ask: did they reduce CPU work, reduce I/O, reduce network transfer, or simply change what was measured?

Where inefficiency shows up

Architectural inefficiency often shows up as long tail latency: a small fraction of requests take much longer than the average.
In WordPress, this can be caused by heavy plugin bootstrapping, expensive queries on specific pages, external API calls, or complex template logic.
When someone reports a big improvement, it helps to ask: did they reduce CPU work, reduce I/O, reduce network transfer, or simply change what was measured?
Try to avoid all-in narratives. Most sites need a combination of techniques; the useful part is knowing which technique addresses which bottleneck.

Why page builders and e-commerce feel different

Page builders can inflate HTML and assets, which increases client work and transfer sizes. That can be cached, but the payload remains heavy.
E-commerce introduces user-specific states (cart, checkout, account). Those flows are harder to cache and therefore expose backend inefficiencies more directly.
A practical way to keep the debate grounded is to define what you mean by “faster.” For some teams, the business metric is conversion; for others, it is crawl efficiency or editorial workflow. Different goals favor different interventions.
If you are comparing approaches, control what you can: same origin server state, same test location, same cache state, and multiple samples. Otherwise, you are mostly measuring randomness.

The classification angle

If you can classify which requests are truly cacheable and which should remain dynamic, you can decide where to invest effort.
The goal is not “cache everything,” but “avoid waking the expensive stack when you can deliver safely without it.”
A practical way to keep the debate grounded is to define what you mean by “faster.” For some teams, the business metric is conversion; for others, it is crawl efficiency or editorial workflow. Different goals favor different interventions.
When someone reports a big improvement, it helps to ask: did they reduce CPU work, reduce I/O, reduce network transfer, or simply change what was measured?

Where this shows up in practice

In day-to-day troubleshooting, the fastest path to clarity is often to pick one representative URL and follow it end to end: request in, code executed, data fetched, HTML produced, assets requested, pixels painted.
If the conversation stays at the level of plugin brands and scores, it is easy to miss the actual bottleneck. A single trace or profile can often replace pages of speculation.
If you are comparing approaches, control what you can: same origin server state, same test location, same cache state, and multiple samples. Otherwise, you are mostly measuring randomness.
In WordPress specifically, small design choices—autoloaded options, hook priority, filesystem checks—can have outsized impact because they occur on nearly every request.
Neutral framing does not mean indecision. It means you can make a decision based on observed constraints rather than inherited slogans.

Discussion prompts

If you reply, consider sharing measurements and constraints. Clear context tends to produce better answers than generic declarations.
Do you know your miss cost? Not in theory—measured on your own server under realistic concurrency.
Which pages are fundamentally hard to cache, and what is your strategy for keeping them fast?

Key takeaways

Separate backend generation time from frontend rendering time; they respond to different interventions.
Ask whether a change reduces work, shifts work, or adds work after the fact.
Treat caching as a powerful tool, but not a substitute for understanding miss-path cost.
Consider request classification as a neutral framing for deciding what must execute.