165 CloudFlare Nodes in 85 of totally 130 Countries
Visit this page regularly as it is updated daily. Last data update: 15-06-2026 07:46:24 UTC
Africa
Algeria (1|4)
Ethiopia (1|5)
Ivory Coast (1|5)
Kenya (2|7)
Mauritius (1|5)
Mozambique (1|2)
Reunion (1|3)
Rwanda (1|1)
South Africa (2|42)
Tanzania (1|6)
Zimbabwe (1|1)Asia
Armenia (1|5)
Azerbaijan (2|5)
Bahrain (1|1)
Bangladesh (1|33)
China (1|470)
Georgia (1|9)
India (4|111)
Indonesia (3|9)
Israel (2|19)
Japan (3|99)
Jordan (1|4)
Kuwait (1|3)
Laos (1|2)
Malaysia (1|35)
Mongolia (1|1)
Nepal (1|15)
Oman (1|9)
Pakistan (2|16)
Philippines (1|5)
Singapore (1|567)
South Korea (1|10)
Sri Lanka (1|1)
Taiwan (2|23)
Thailand (1|21)
Turkey (1|84)
United Arab Emirates (1|4)
Vietnam (2|4)Europe
Albania (1|1)
Austria (1|145)
Belgium (1|27)
Bulgaria (1|75)
Croatia (1|2)
Cyprus (1|9)
Czech Republic (1|45)
Denmark (1|74)
Estonia (1|18)
Finland (1|22)
France (3|550)
Germany (6|623)
Greece (1|38)
Hungary (1|9)
Iceland (1|2)
Ireland (1|18)
Italy (2|167)
Latvia (1|27)
Lithuania (1|7)
Luxembourg (1|2)
Moldova (1|1)
Netherlands (1|528)
Norway (1|29)
Poland (1|184)
Portugal (1|36)
Romania (1|80)
Russia (1|12)
Serbia (1|14)
Slovakia (1|12)
Spain (2|150)
Sweden (1|78)
Switzerland (2|49)
Ukraine (1|10)
United Kingdom (3|535)
Canada (4|115)
Costa Rica (1|3)
Guatemala (1|4)
Mexico (1|1)
United States (29|1741)
Australia (5|157)
French Polynesia (1|1)
New Caledonia (1|3)
New Zealand (1|10)
Argentina (1|24)
Brazil (5|273)
Chile (1|15)
Ecuador (2|9)| CDG | Paris | France (FR) | France (FR) | 172.68.151.103 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.104 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.11 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.117 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.118 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.119 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.12 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.13 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.139 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.151 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.153 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.160 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.161 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.164 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.28 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.29 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.41 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.42 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.43 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.44 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.45 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.46 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.57 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.61 |
| CDG | Paris | France (FR) | France (FR) | 172.68.151.70 |
Parse Time: 92.236 ms
Does “Dynamic” in WordPress Mean “Inevitable”?
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.
The default assumption
In WordPress, ‘dynamic’ often means: the page is generated by PHP on each request and may vary by user, cart state, or query parameters.That leads to the assumption that dynamic pages are inevitably slow and cannot be improved much beyond caching and database tuning.
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.
Dynamic is a spectrum
Not all dynamic pages vary meaningfully per request. Some vary rarely (a banner), some vary by locale, some vary only for logged-in users.Treating the entire page as fully dynamic can be a conservative but expensive choice.
In WordPress specifically, small design choices—autoloaded options, hook priority, filesystem checks—can have outsized impact because they occur on nearly every request.
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.
What makes something truly dynamic
A page is truly dynamic when serving the wrong version has unacceptable risk: incorrect personalization, security issues, or stale transactional state.Many pages are labeled dynamic for convenience rather than necessity.
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.
Try to avoid all-in narratives. Most sites need a combination of techniques; the useful part is knowing which technique addresses which bottleneck.
The role of query strings
Query parameters can explode the number of variants. Some are meaningful (filters), some are noise (tracking).If you do not normalize or classify them, your cache strategy becomes fragile and your backend work increases.
Try to avoid all-in narratives. Most sites need a combination of techniques; the useful part is knowing which technique addresses which bottleneck.
Try to avoid all-in narratives. Most sites need a combination of techniques; the useful part is knowing which technique addresses which bottleneck.
A neutral classification approach
Classify pages into: safely cacheable, cacheable with variation (by locale/device), and non-cacheable.Then within non-cacheable, ask: which parts must be dynamic and which parts are incidentally dynamic because of universal execution?
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.
Prevention as a question, not an answer
For ‘mostly stable’ pages that are treated as dynamic, a prevention approach would aim to reduce backend execution while keeping correctness.This is not a promise; it is a direction for experiments: isolate what truly varies and avoid executing unrelated components.
Try to avoid all-in narratives. Most sites need a combination of techniques; the useful part is knowing which technique addresses which bottleneck.
Try to avoid all-in narratives. Most sites need a combination of techniques; the useful part is knowing which technique addresses which bottleneck.
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.
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?
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.Which of your ‘dynamic’ pages would still be correct if some plugins did not run?
Do you have a policy for tracking parameters versus functional parameters?
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.
Suggested experiment
Pick one URL that matters to you and run a controlled A/B test.Hold cache state constant (either fully warm or fully cold) and compare backend timing with the same concurrency.
Then compare a simple user-centric metric (LCP or full load) from a consistent location.
- Measure baseline backend time and resource usage.
- Enable one change at a time.
- Repeat enough times to see variance.
- Decide based on the metric that aligns with your goal.