168 CloudFlare Nodes in 85 of totally 130 Countries
Visit this page regularly as it is updated daily. Last data update: 31-08-2026 12:22:02 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|34)
China (1|472)
Georgia (1|9)
India (4|111)
Indonesia (3|9)
Israel (2|20)
Japan (3|99)
Jordan (1|4)
Kuwait (1|3)
Laos (1|2)
Malaysia (1|36)
Mongolia (1|1)
Nepal (1|15)
Oman (1|9)
Pakistan (2|16)
Philippines (2|6)
Singapore (1|576)
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|4)
Austria (1|147)
Belgium (1|27)
Bulgaria (1|77)
Croatia (1|2)
Cyprus (1|10)
Czech Republic (1|46)
Denmark (1|76)
Estonia (1|18)
Finland (1|22)
France (3|558)
Germany (6|645)
Greece (1|38)
Hungary (1|9)
Iceland (1|2)
Ireland (1|18)
Italy (2|169)
Latvia (1|27)
Lithuania (1|7)
Luxembourg (1|2)
Moldova (1|1)
Netherlands (1|533)
Norway (1|29)
Poland (2|191)
Portugal (1|39)
Romania (1|82)
Russia (1|12)
Serbia (1|14)
Slovakia (1|12)
Spain (2|166)
Sweden (1|80)
Switzerland (2|49)
Ukraine (1|10)
United Kingdom (3|556)
Canada (4|125)
Costa Rica (1|3)
Guatemala (1|4)
Mexico (1|1)
United States (30|1840)
Australia (5|165)
French Polynesia (1|1)
New Caledonia (1|3)
New Zealand (1|10)
Argentina (1|24)
Brazil (5|275)
Chile (1|15)
Ecuador (2|10)United States: 30 Nodes with 1840 IP Addresses
| PDX | Portland, OR | United States (US) | United States (US) | 172.68.174.43 |
| PDX | Portland, OR | United States (US) | United States (US) | 172.68.174.56 |
| PDX | Portland, OR | United States (US) | United States (US) | 172.68.174.76 |
| PDX | Portland, OR | United States (US) | United States (US) | 172.68.174.77 |
| PDX | Portland, OR | United States (US) | United States (US) | 172.68.174.80 |
| PDX | Portland, OR | United States (US) | United States (US) | 172.68.174.81 |
| PDX | Portland, OR | United States (US) | United States (US) | 172.68.175.77 |
| PDX | Portland, OR | United States (US) | Taiwan (TW) | 172.68.175.81 |
| PDX | Portland, OR | United States (US) | United States (US) | 172.68.175.98 |
| PHL | Philadelphia, PA | United States (US) | United States (US) | 172.68.94.150 |
| 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 |
| SAN | San Diego, CA | United States (US) | United States (US) | 172.68.228.173 |
| SEA | Seattle, WA | United States (US) | United States (US) | 104.22.72.18 |
| SEA | Seattle, WA | United States (US) | United States (US) | 108.162.245.101 |
| SEA | Seattle, WA | United States (US) | Canada (CA) | 108.162.245.108 |
| SEA | Seattle, WA | United States (US) | United States (US) | 108.162.245.109 |
| SEA | Seattle, WA | United States (US) | Canada (CA) | 108.162.245.118 |
| SEA | Seattle, WA | United States (US) | Canada (CA) | 108.162.245.127 |
| SEA | Seattle, WA | United States (US) | United States (US) | 108.162.245.141 |
| SEA | Seattle, WA | United States (US) | United States (US) | 108.162.245.149 |
Parse Time: 59.096 ms
WP-Rush: WordPress Performance by Prevention
The Mathematical Overhead of Late Optimization
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.
A toy model
Consider page generation cost as G and optimization cost as O. If optimization runs after generation, total backend cost is G + O.If optimization reduces payload and improves client time by ΔC, the net effect depends on how you value backend time versus client time.
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.
When O is small
If O is negligible compared to G, post-processing is easy to justify. Many optimizations attempt to live in this regime.But O is not always negligible, especially when it involves regex-heavy HTML manipulation or large string buffers.
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.
Queueing makes it nonlinear
Backend time is not just a linear cost. Under concurrency, additional per-request time reduces throughput and increases wait times.Even a small increase in processing time can amplify tail latency when workers are saturated.
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.
What this suggests
An optimization that adds O might still help a single user on an empty server, but hurt the average user under load.This is why ‘it feels faster’ can disagree with load tests and production metrics.
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.
A neutral measurement plan
Measure both single-user latency and throughput under realistic concurrency.If you can only measure one, choose the one that matches your business risk: for shops, tail latency during bursts; for content sites, average experience.
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.
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.
Where prevention changes the equation
Prevention aims to reduce G itself by avoiding work, rather than adding O to compensate.In the toy model, if you can reduce generation cost by ΔG through earlier decisions, the total cost becomes (G − ΔG) + O', where O' may be near zero if you do not post-process.
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.
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.
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.
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 of your optimizations increases backend time but decreases payload?
Do you test under concurrency, or only with single-run tools?
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.