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) | 172.71.232.163 |
| CDG | Paris | France (FR) | Turkey (TR) | 172.71.232.164 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.165 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.22 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.23 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.26 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.42 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.43 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.50 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.51 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.58 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.60 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.61 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.72 |
| CDG | Paris | France (FR) | United Arab Emirates (AE) | 172.71.232.78 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.79 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.93 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.98 |
| CDG | Paris | France (FR) | France (FR) | 172.71.232.99 |
| LYS | Lyon | France (FR) | France (FR) | 162.158.13.132 |
| LYS | Lyon | France (FR) | France (FR) | 162.158.13.133 |
| LYS | Lyon | France (FR) | Switzerland (CH) | 162.158.13.134 |
| LYS | Lyon | France (FR) | Switzerland (CH) | 162.158.13.135 |
| LYS | Lyon | France (FR) | Switzerland (CH) | 162.158.13.154 |
| LYS | Lyon | France (FR) | France (FR) | 162.158.13.155 |
Parse Time: 105.520 ms
Is WooCommerce Checkout Speed a Structural Issue?
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.
Why checkout feels different
Shop homepages and product pages often benefit from caching and CDN delivery.Checkout is different: it is stateful, sensitive, and frequently excluded from page caching for safety. That exposes the true cost of backend 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.
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 makes checkout heavy
Checkout triggers validation, session/cart handling, payment gateway interactions, tax/shipping calculations, and often third-party scripts.It can also wake up many plugins: analytics, marketing automation, fraud tools, and theme components.
In WordPress specifically, small design choices—autoloaded options, hook priority, filesystem checks—can have outsized impact because they occur on nearly every request.
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 decomposition
Split the problem into: (1) backend computation time, (2) external dependency time, and (3) front-end rendering and scripts.A slow checkout can be dominated by any one of these, and the fix depends on which dominates.
Try to avoid all-in narratives. Most sites need a combination of techniques; the useful part is knowing which technique addresses which bottleneck.
In WordPress specifically, small design choices—autoloaded options, hook priority, filesystem checks—can have outsized impact because they occur on nearly every request.
The plugin bootstrap effect
Even when the checkout template is simple, WordPress still loads the plugin ecosystem unless something prevents it.If dozens of plugins hook into initialization, the baseline cost of “just reaching checkout” can be significant.
Try to avoid all-in narratives. Most sites need a combination of techniques; the useful part is knowing which technique addresses which bottleneck.
In WordPress specifically, small design choices—autoloaded options, hook priority, filesystem checks—can have outsized impact because they occur on nearly every request.
Why ‘optimization’ may miss the point
Asset minification helps less when the page is already excluded from cache and is waiting on server-side operations or gateways.Database cleanup helps less if the main cost is PHP initialization and plugin 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?
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.
Measurement without drama
Instrument the backend. Log where time is spent: PHP bootstrap, database queries, external calls.On the client, measure whether delays are waiting for the HTML, waiting for scripts, or waiting for async calls after the page loads.
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 prevention could matter
If checkout must remain dynamic, the question becomes: can you reduce what executes during checkout by preventing unrelated plugins from running?That is not a claim; it is a hypothesis. The measurable output would be reduced server time and improved reliability under load.
Try to avoid all-in narratives. Most sites need a combination of techniques; the useful part is knowing which technique addresses which bottleneck.
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.
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.
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.
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 plugins must run on checkout, and which run there by accident?
Do you treat checkout speed as a technical metric or as a conversion risk?
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.