168 CloudFlare Nodes in 85 of totally 130 Countries
Visit this page regularly as it is updated daily. Last data update: 31-08-2026 02:20:46 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)Manila: 1 Node with 5 IP Addresses
| MNL | Manila | Philippines (PH) | Philippines (PH) | 162.158.249.132 |
| MNL | Manila | Philippines (PH) | Philippines (PH) | 162.158.249.141 |
| MNL | Manila | Philippines (PH) | Philippines (PH) | 172.71.87.158 |
| MNL | Manila | Philippines (PH) | Philippines (PH) | 172.71.87.159 |
| MNL | Manila | Philippines (PH) | Philippines (PH) | 172.71.87.166 |
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WP-Rush: WordPress Performance by Prevention
When Adding More Servers Is Not the Real Answer
When Adding More Servers Is Not the Real Answer
Horizontal scaling solves capacity problems, but not necessarily inefficiency.Scaling Logic
More servers increase throughput.They do not reduce per-request cost.
Economic Consideration
Scaling infrastructure without reducing inefficiency increases operational expense.Architectural First
Reducing execution cost often yields more sustainable improvements.Load Testing Insight
Measure per-request CPU time before scaling decisions.Key Takeaways
- Scale after optimizing structure.
- Separate throughput from efficiency.
- Analyze cost per request.
- Infrastructure should amplify good architecture.