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What Does 1GB of Mobile Data Cost in Every Country?

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What Does 1GB of Mobile Data Cost in Every Country?

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What Does 1GB of Mobile Data Cost in Every Country?

Billions of people around the world rely on their mobile phones every day.

Even in a saturated market, mobile networks have continued to expand their reach. In the last five years alone, almost one billion additional people have gained access to mobile data services.

Despite the growing prevalence of these networks worldwide, the cost of gaining access can vary greatly from country to country—particularly when it comes to the price of mobile data.

Today’s chart uses figures from Cable.co.uk to showcase the average cost of one gigabyte (GB) of mobile data in 155 different countries and jurisdictions. Despite the vast global reach of the mobile economy, it’s clear it still has a long way to go to reach true accessibility.

Discrepancies in Mobile Data Costs

Researchers have identified several key elements that help explain the cost variation for mobile data between countries:

  1. Existing infrastructure (or lack thereof): This might seem counterintuitive, but most mobile networks rely on a fixed-line connection. As a result, countries with existing infrastructure are able to offer mobile plans with more data, at a cheaper price. This is the case for India and Italy. Countries with minimal or no infrastructure rely on more costly connection alternatives like satellites, and the cost typically gets passed down to the consumer.
  2. Reliance on mobile data: When mobile data is the primary source of internet in a particular region, adoption can become nearly universal. This high demand typically leads to an increase in competing providers, which in turn lowers the cost. Kyrgyzstan is a good example of this.
  3. Low data consumption: Countries with poor infrastructure tend to use less data. With mobile plans that offer smaller data limits, the overall average cost per GB tends to skew higher. Countries like Malawi and Benin are examples of this phenomenon.
  4. Average income of consumer: Relatively wealthy nations tend to charge more for mobile services since the population can generally afford to pay more, and the cost of operating a network is higher. This is apparent in countries like Canada or Germany.

The Cheapest Countries for 1 GB of Data

Even among the cheapest countries for mobile data, the cost variation is significant. Here’s a look at the top five cheapest countries for 1 GB of data:

Overall RankCountryAverage price of 1GB (USD)
1🇮🇳 India
2🇮🇱 Israel11¢
3🇰🇬 Kyrgyzstan 21¢
4🇮🇹 Italy 43¢
5 🇺🇦 Ukraine46¢

India ranks the cheapest at $0.09 per GB, a 65% decrease in price compared to the country’s average cost in 2019.

Why is data so cheap in India? A significant factor is the country’s intense market competition, driven by Reliance Jio—a telecom company owned by Reliance Industries, one of the largest conglomerates in India. Reliance Jio launched in 2016, offering customers free trial periods and plans for less than a $1 a month. This forced other providers to drop their pricing, driving down the overall cost of data in the region.

Because these prices are likely unsustainable for the long term, India’s cheaper-than-usual prices may soon come to an end.

Another country worth highlighting is Kyrgyzstan, which ranks as the third cheapest at $0.21 per GB, ahead of Italy and Ukraine. This ranking is surprising, given the country’s minimal fixed-line infrastructure and large rural population. Researchers suspect the low cost is a result of Kyrgyzstan’s heavy reliance on mobile data as the population’s primary source of internet.

The Most Expensive Countries for 1 GB of Data

On the other end of the spectrum, here are the top five most expensive countries for one gigabyte of mobile data:

Overall RankCountryAverage price of 1GB (USD)
155🇲🇼 Malawi$27.41
154🇧🇯 Benin$27.22
153🇹🇩 Chad$23.33
152🇾🇪 Yemen$15.98
151🇧🇼 Botswana$13.87

A striking trend worth noting is that four out of five of the most expensive countries for mobile data are in Sub-Saharan Africa (SSA).

A significant factor behind the high cost of data in SSA is its lack of infrastructure. With overburdened networks, the data bundles offered in the region are generally smaller. This drives up the average cost per GB when compared to countries with unlimited packages.

Another element that contributes to SSA’s high costs is its lack of market competition. In countries with multiple competing networks, such as Nigeria, the cost of data skews lower.

The Full Breakdown

The below table has a full list of all 155 countries and jurisdictions included in the data set. It helps demonstrate the stark contrast in the cost of mobile data between the most expensive and cheapest countries globally.

RankCountryAverage price of 1GB (USD)
1India
2Israel11¢
3Kyrgyzstan21¢
4Italy43¢
5Ukraine46¢
6Kazakhstan46¢
7Somalia50¢
8Sri Lanka51¢
9Russian Federation52¢
10Vietnam57¢
11China61¢
12Sudan63¢
13Indonesia64¢
14Algeria65¢
15Australia68¢
16Pakistan69¢
17Poland70¢
18Bangladesh70¢
19Chile71¢
20Turkey72¢
21Tanzania73¢
22Dominican Republic74¢
23Mongolia74¢
24Iran75¢
25Kuwait77¢
26Myanmar78¢
27Denmark80¢
28France81¢
29Nepal86¢
30Belarus89¢
31Georgia93¢
32Ghana94¢
33Monaco98¢
34Western Sahara99¢
35Morocco99¢
36Brazil$1.01
37Romania$1.03
38Jordan$1.03
39Kenya$1.05
40Armenia$1.05
41Austria$1.08
42Egypt$1.09
43Moldova$1.12
44Malaysia$1.12
45Thailand$1.23
46Estonia$1.27
47Uzbekistan$1.34
48Ireland$1.36
49Zambia$1.36
50Tunisia$1.37
51Nigeria$1.39
52United Kingdom$1.39
53Philippines$1.42
54El Salvador$1.45
55Argentina$1.45
56Rwanda$1.48
57Slovenia$1.48
58Cambodia$1.50
59Afghanistan$1.55
60Uruguay$1.58
61Serbia$1.60
62Uganda$1.62
63Nicaragua$1.71
64Macedonia$1.75
65Spain$1.81
66Lithuania$1.85
67Azerbaijan$1.86
68Congo$1.94
69Sweden$2.07
70Guinea$2.08
71Timor-Leste$2.08
72Saudi Arabia$2.12
73Burundi$2.12
74Peru$2.13
75Lesotho$2.13
76Finland$2.14
77Guatemala$2.17
78Bulgaria$2.22
79Bahrain$2.27
80Paraguay$2.30
81Ethiopia$2.44
82Singapore$2.47
83Burkina Faso$2.47
84Croatia$2.48
85Mauritius$2.48
86Hong Kong$2.55
87Haiti$2.74
88Costa Rica$2.74
89Cameroon$2.75
90Albania$2.83
91Netherlands$2.98
92Bosnia and Herzegovina$3.04
93Honduras$3.12
94Côte d'Ivoire$3.20
95Ecuador$3.24
96Liberia$3.25
97Palestine$3.26
98Niger$3.30
99Senegal$3.30
100Mozambique$3.33
101Colombia$3.46
102Sierra Leone$3.69
103United Arab Emirates$3.78
104Latvia$3.79
105Lebanon$3.82
106Slovakia$3.84
107Jamaica$3.88
108Japan$3.91
109Germany$4.06
110Qatar$4.12
111Guinea-Bissau$4.12
112Mali$4.12
113Lao PDR$4.16
114Iraq$4.20
115South Africa$4.30
116Togo$4.50
117Oman$4.58
118Mauritania$4.63
119Tajikistan$4.65
120Libya$4.73
121Mexico$4.77
122Namibia$4.78
123Belgium$4.88
124Gabon$4.89
125Portugal$4.97
126Bolivia$5.09
127Gambia$5.10
128Norway$5.28
129Angola$5.29
130Hungary$5.32
131Papua New Guinea$5.40
132Taiwan$5.91
133Trinidad and Tobago$5.92
134New Zealand$6.06
135Syria$6.55
136Panama$6.69
137Czech Republic$7.95
138United States$8.00
139Central African Republic$8.25
140Switzerland$8.38
141Madagascar$8.81
142Puerto Rico$9.17
143South Korea$10.94
144Turkmenistan$11.44
145Greece$12.06
146Canada$12.55
147Equatorial Guinea$12.78
148Eswatini$13.31
149Cuba$13.33
150Cyprus$13.56
151Botswana$13.87
152Yemen$15.98
153Chad$23.33
154Benin$27.22
155Malawi$27.41

Interestingly, the highest average cost is 30,000% more than the cheapest average price.

The Technology Gap

Will we reach a point of equal accessibility across the globe, or will the technology gap between countries continue to widen?

With 5G networks on the rise, just seven countries are expected to make up the majority of 5G related investments. Time will tell what this means for adoption worldwide.

Editor’s Note: The methodology used by Cable.co.uk represents a region’s national average, based on both pre-paid and post-paid plans. While the data correctly represents each region’s average cost on 1 GB based on the chosen methodology, Cable.co.uk acknowledges that it may not reflect the way most people in a country consume data.

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AI

Charted: The Exponential Growth in AI Computation

In eight decades, artificial intelligence has moved from purview of science fiction to reality. Here’s a quick history of AI computation.

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A cropped version of the time series chart showing the creation of machine learning systems on the x-axis and the amount of AI computation they used on the y-axis measured in FLOPs.

Charted: The Exponential Growth in AI Computation

Electronic computers had barely been around for a decade in the 1940s, before experiments with AI began. Now we have AI models that can write poetry and generate images from textual prompts. But what’s led to such exponential growth in such a short time?

This chart from Our World in Data tracks the history of AI through the amount of computation power used to train an AI model, using data from Epoch AI.

The Three Eras of AI Computation

In the 1950s, American mathematician Claude Shannon trained a robotic mouse called Theseus to navigate a maze and remember its course—the first apparent artificial learning of any kind.

Theseus was built on 40 floating point operations (FLOPs), a unit of measurement used to count the number of basic arithmetic operations (addition, subtraction, multiplication, or division) that a computer or processor can perform in one second.

ℹ️ FLOPs are often used as a metric to measure the computational performance of computer hardware. The higher the FLOP count, the higher computation, the more powerful the system.

Computation power, availability of training data, and algorithms are the three main ingredients to AI progress. And for the first few decades of AI advances, compute, which is the computational power needed to train an AI model, grew according to Moore’s Law.

PeriodEraCompute Doubling
1950–2010Pre-Deep Learning18–24 months
2010–2016Deep Learning5–7 months
2016–2022Large-scale models11 months

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

However, at the start of the Deep Learning Era, heralded by AlexNet (an image recognition AI) in 2012, that doubling timeframe shortened considerably to six months, as researchers invested more in computation and processors.

With the emergence of AlphaGo in 2015—a computer program that beat a human professional Go player—researchers have identified a third era: that of the large-scale AI models whose computation needs dwarf all previous AI systems.

Predicting AI Computation Progress

Looking back at the only the last decade itself, compute has grown so tremendously it’s difficult to comprehend.

For example, the compute used to train Minerva, an AI which can solve complex math problems, is nearly 6 million times that which was used to train AlexNet 10 years ago.

Here’s a list of important AI models through history and the amount of compute used to train them.

AIYearFLOPs
Theseus195040
Perceptron Mark I1957–58695,000
Neocognitron1980228 million
NetTalk198781 billion
TD-Gammon199218 trillion
NPLM20031.1 petaFLOPs
AlexNet2012470 petaFLOPs
AlphaGo20161.9 million petaFLOPs
GPT-32020314 million petaFLOPs
Minerva20222.7 billion petaFLOPs

Note: One petaFLOP = one quadrillion FLOPs. Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

The result of this growth in computation, along with the availability of massive data sets and better algorithms, has yielded a lot of AI progress in seemingly very little time. Now AI doesn’t just match, but also beats human performance in many areas.

It’s difficult to say if the same pace of computation growth will be maintained. Large-scale models require increasingly more compute power to train, and if computation doesn’t continue to ramp up it could slow down progress. Exhausting all the data currently available for training AI models could also impede the development and implementation of new models.

However with all the funding poured into AI recently, perhaps more breakthroughs are around the corner—like matching the computation power of the human brain.

Where Does This Data Come From?

Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.

Note: The time estimated to for computation to double can vary depending on different research attempts, including Amodei and Hernandez (2018) and Lyzhov (2021). This article is based on our source’s findings. Please see their full paper for further details. Furthermore, the authors are cognizant of the framing concerns with deeming an AI model “regular-sized” or “large-sized” and said further research is needed in the area.

Methodology: The authors of the paper used two methods to determine the amount of compute used to train AI Models: counting the number of operations and tracking GPU time. Both approaches have drawbacks, namely: a lack of transparency with training processes and severe complexity as ML models grow.

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