An in-depth study of the application of freemium pricing practice in the cloud services space that Amazon EC2, Google Compute Engine, Microsoft Azure and Rackshare compete in. Submitted in partial fulfilment of the requirements of the Imperial MBA degree and the Diploma of Imperial College London. I was awarded a Distinction for this Pricing Strategy elective. Excluding appendixes: 2,497 words

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Is freemium a viable pricing strategy for cloud services?
An in-depth study of the application of freemium pricing practice in the cloud services space that Amazon EC2, Google Compute Engine, Microsoft Azure and Rackshare compete in
1 INTRODUCTION TO THE UTILITY COMPUTING SPACE
“Cloud Computing” is one of the hottest buzzwords in the IT industry at the moment, right alongside “Analytics”(also known as “Big Data”), “Mobile” and “Social”. Puri (2014) draws our attention to a future convergence of these four mega trends, explaining that cloud computing has the potential to “simultaneously reduce costs and make IT more agile”. But what exactly is “Cloud computing”?
In order to better understand this trend of “cloud computing”, we looked to a recent Association for Computing Machinery article which outlines some useful definitions:
‘Data center hardware and software is what we will call a cloud. When a cloud is made available in a pay-as-you-go manner to the general public, we call it a public cloud; the service being sold is utility computing.’
(Armbrust, M et al, 2010)
For the purpose of this paper, we delved in to this definition of “utility computing” and analysed the pricing strategies employed by different market participants competing in this space. Building on a framework by Marn, M. V. et al (2010: p.247) for analysing Software and Information Products, we have highlighted the following characteristics that bear further investigation:
 Marginal production costs are near zero and high upfront costs. The marginal cost to a utility computing service provider for serving one additional customer is practically zero, because most of the costs of providing the service have been sunk in the initial capital expenditure of investing in data centres, infrastructure and organisations. The incremental relevant costs of serving one marginal customer – if the incremental electricity and bandwidth consumption could be measured –is not material in comparison to these major capital costs.
 Potential for high switching costs. Each of the utility computing service providers try to differentiate their service offering by creating a unique selling proposition. In some cases this creates a proprietary lock-in and hence, increases the switching costs for users to migrate from one provider to another. For example, although Amazon’s Simple Storage Service (S3) makes it easy to add new storage capacity on demand to a user’s systems, the more data a user hosts in the Amazon cloud, the more difficult it becomes to later migrate out of Amazon’s cloud and to re-write cloud applications to use a different vendor’s storage technology platform. These high switching costs would be what Porter (1980) would refer to as “barriers to entry”.

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 Potential for winner-take all scenarios. Products that have a strong network effect create a potential for “winner-take-all” scenarios when businesses create a critical mass user base, creating another form of a barrier to entry for rivals. In the utility computing industry, this scenario is where one dominant player in the industry is able to sustain a large enough user base such that it effectively blocks out competitors. The dominant player can then sustain significant economies of scale to win itself the position of absolute cost leader and hence squeeze out rivals through low-ball pricing in a commoditised market.
The utility computing space therefore represents an interesting strategic dilemma. In much the same way that airlines sell “perishable products” in that once the plane has flown, the unsold seats are “wasted”, so too is unutilised cloud capacity that isn’t revenue generating considered a “wasted” resource. Utility computing’s high infrastructure and start-up costs are also similar to the high start-up costs in airlines.
It is in this context: of a “perishable” product, zero marginal cost of production and unique competitive pressures that we now introduce “freemium” pricing strategies as a way to balance price and utilization and hence, maximise profits for the utility computing service provider.
2 WHAT ARE FREEMIUM PRICING STRATEGIES?
Wilson (2006) was one of the early supporters of the “freemium” pricing model, blogging about it before the label “freemium” was coined. This was then explored further by Anderson (2009) where he introduces the “inexorable downward pressure on the prices of all things made of ideas”. Although originally written from the perspective of selling digital copies of copyrighted materials, Anderson predicts a future where the market price of information goods that have a marginal cost of production that is “close enough” to zero, will eventually be priced at zero.
Since Anderson’s seminal book on the subject, the practice of freemium pricing strategies have evolved, and Murphy (2010) gives a good summary of current practices. Murphy (2010) expands on Anderson’s work and explains that the freemium pricing model is where two pricing tiers are created – a “Free” tier and a “Premium” price. Consumers self-select one of the tiers and enjoy the service. This pricing strategy is employed in several online services as described by Wilson (2006) such as Skype and Flickr.
In a way then, freemium pricing strategies are a form of market segmentation. It’s not quite second degree price discrimination because although customers self-select based on a menu of prices, there are two different tiers of the service – “Free” vs. “Premium” with two different commensurate qualities of service. We would classify this as second degree price discrimination if it was the same product being offered at different price points, as in discounted rail travel offered for students or seniors.
I would argue that the key here is that the difference between the incremental relevant costs to serve one additional “Free” user as compared with one “Premium” user is not material. As stated in Section One this is because the majority of the operation costs are in the start-up costs. Utility computing service providers therefore fit the “information goods” label that Google’s Chief Economist, Hal Varian describes in his paper (Varian, 1997).

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When evaluating freemium pricing strategies, Varian (1997) would say that what we have here is a form
of third degree price discrimination where the producers segment the market based on quality
discrimination or versioning. His paper makes the case that where a producer is unable to identify the
willingness to pay (“WTP”) of a given consumer, one feasible strategy is to segment the market and to
base price on an endogenous characteristic of the product. In this case – through offering two different
versions of service quality of the same product. The result is that consumers self-select into their high-
WTP and low-WTP groups in a way that maximises revenues, and therefore the producer maximises
profits.
Therefore, per economic theory we can say that freemium pricing strategies are sound when they
perform third degree price discrimination.
3 THE ECONOMICS & STRATEGY OF FREEMIUM PRICING
Now that we have sketched out a picture of the utility computing space and introduced some of the key
economic concepts of a freemium pricing model, we will now apply some analytical frameworks to
better understand different aspects of pricing in the utility computing space.
3.1 ECONOMIC VALUE TO THE CUSTOMER
Nagle, T et al (2011) provide a framework for understanding the Economic Value to the Customer (EVC),
which we can use to analyse the “consumer surplus” enjoyed by customers. Schadler (2009) gives us
some data to apply the EVC framework to when comparing the relative economic value of different
offerings. Although this data applies to Google Apps (part of Google’s Cloud Platform) nevertheless the
learnings are applicable also to the utility computing segment which we are focusing on in this paper.
-
5.00
10.00
15.00
20.00
25.00
30.00
On-premises Google
US$25.18 per user on-premises vs. US$8.47 for Google
Subscription Server hardware and OS
Server software Client software
Storage Filtering
Source: Schadler (2009)
Figure One: EVC from Google Apps
Here – the reference product, the on-premises service, has a much higher price point than our
comparison, the Google service. If we assume that the perceived value of Google’s offering is at least as

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high as the on-premises services then we could see that there is significant inducement being offered here – being at the most US$16.71 per user.
Applying the EVC framework to this pricing comparison then suggests two things: Firstly, that Google are pricing to create significant inducement to encourage users to switch. Secondly, Google’s incremental relevant costs are zero, and hence earns a full contribution margin that is equal to the subscription price set.
3.2 WHAT CAN GAME THEORY PREDICT ABOUT COMPETITIVE REALITY?
Seufert (2014) likens the situation in utility computing to the Prisoner’s Dilemma. In this – utility providers will think that price cuts stimulate demand and that the demand for their products are price elastic. Reducing prices will therefore win market share from the competition – this results in the dominant strategy to be to cut prices irrespective of the other player’s move. The end result is that prices eventually converge on the marginal costs of production and distribution.
I would argue that this does not consider deeper consequences of a price cut. Simply looking to the impact on a price cut as below:
1. To stimulate quantity demanded, and win market share from the competition
2. Resulting in increased revenues, for a short period of competitive advantage
3. Provoking the other player to drop prices too, resulting in a lose-lose situation for both players.
This ignores the fourth and fifth order effects – for the provider who can cut the most and offer the lowest absolute price through innovative engineering that delivers better-than-average cost structure, he can afford to win at the competitor’s expense and to push ever greater economies of scale and exploit the experience curve effect.
Applying game theory reinforces the point in Section 1 – that the utility computing space bears some “winner takes all” characteristics. As the industry leaders build ever greater customer bases, game theory predicts that prices will race and the market evolves into a race to the bottom in a low margin, commoditised space.
We can therefore predict that utility computing will follow this “Tit For Tat” strategy form of the Prisoner’s Dilemma.
4 PRICING IN THE UTILITY COMPUTING SPACE
RightScale (2014) in their annual cloud pricing analysis observe that Amazon have been aggressive in their strategy of waging a pricing war and trying to keep undercutting the competition’s price cuts. They conclude that, following the pricing war of 2012 and 2013 that:

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“AWS (Amazon Web Services) is setting the standard … in driving prices down. It [Amazon] has been leading the way in terms of both frequency and size of reductions, and forcing others to follow.”
RightScale (2014)
In recent months, Amazon has been adopting a “Tit for Tat” strategy in mimicking price reductions announced by its rivals. Hosseini (2014A) outlines Google’s price cut in early 2014 that reduced the prices for cloud computing services by 32%, storage by 68% and database by 85%. Google’s new pricing structure significantly undercut Amazon’s “spot” prices by 21% to 60%, depending on service tiers and utilisation patterns. However, Amazon were still cheaper in the “bulk purchase” prices by between 3% to 19% as compared to Google. These bulk purchase (“Heavy Reserved Instances” as they’re termed by Amazon) contracts lock in customers to 3-year commitments
However, immediately after suffering this pricing disadvantage, Amazon followed suit by matching some of Google’s pricing structure as described by Hosseini (2014B). Amazon cut its base prices at the standard tier, but kept a 6% to 16% price premium for its higher tier of service, probably accounting for the slightly higher quality of service as compared to Google’s. And, as predicted in Section 3.3 above – Amazon appear to be leveraging on the effect of trading lower prices for upfront commitments to create lock-in.
Based on Hosseini (2014B), one might therefore conclude that Amazon is simply adopting this “Tit for Tat” strategy in an effort to low-ball and undercut Google, and to therefore protect its market share.
I would argue that yes, this is an effort to protect market share – but the underlying economics are more revealing than this simplistic explanation. I would argue that – in the same way that airlines practice yield management – the key to profitability in industries with a high incidence of sunk costs (like car rental and other infrastructure-heavy industries) and in particular, utility computing is to balance utilisation with an adequate price that draws sufficient customers to enable the business to operate at scale and at economically profitable levels.
To further complicate things – there is the matter of “Spot” prices. Amazon (2014) suggests that Amazon set “spot” prices for utility computing based on market demand and spare capacity, however recent research published by Yehuda et al (2013) suggest that this isn’t the case. In this study the authors hypothesise that actually, Amazon have significant levels of spare unutilised capacity and through a randomised hidden reserve price, create the impression of constant flux in demand and supply.
This hidden reserve price dynamically fluctuates over time. Yehuda et al (2013) suggest that Amazon might be using this randomised hidden reserve price pricing mechanism to either:
1. mask the fact that there are significant variability in demand and that in fact, Amazon’s cloud is running at low utilisation; or that
2. there is in fact a shortfall in supply (and utilisation is too high) and that setting a high hidden reserve price would prohibit the low bids from winning precious capacity that needs to go to the “reserved” tier users.
Whatever the case may be – research suggests that Amazon are not setting spot prices in reference to market demand, and this may be skewing some of the public perception regarding utility computing.

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From the analysis above, we can confirm that in fact, our prediction on “Tit For Tat” strategies are held up in reality.
5 CONCLUSION
This paper explored the game theory aspects of pricing, and how applying the freemium pricing model in utility computing could expose new insights in to this business.
When all of these trends are considered in aggregate, the key that we uncovered is that utility computing is a business that revolves around capacity utilisation. Key in this industry is to balance pricing at a level that encourages adoption while at the same time offering a free tier as a way to incentivise developers to take up a new platform and therefore lock in new users to that provider’s proprietary technologies.
One way to take this research further would be take surveys of users to understand the impact of the free price tier or how the lock-in effect of different proprietary technologies impact on pricing and to analyse pricing with an EVC framework.
One final prediction is this: the utility computing space will likely evolve to an oligopoly structure, with a concentration of market share within a handful of firms who leverage on their extreme economies of scale to erect near insurmountable barriers to entry. Furthermore, driven by a freemium pricing model, the oligopolists would eventually win over new users as they come on to the market for utility computing on the free tier and create significant barriers to entry for competitors.
One eventuality that this predicts is that – as per Anderson (2009) – eventually as technology advances, storage becomes cheaper and processors become faster, that the costs to serve the average users will approach zero. We are already seeing the effect of how freemium pricing has created a massive user base for Google with their Gmail service – we could therefore say that this trend will evolve to envelope free utility computing that satisfies the majority of users.

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6 APPENDIX ONE: BIBLIOGRAPHY
Agmon Ben-Yehuda, O., Ben-Yehuda, M., Schuster, A., & Tsafrir, D. (2013). Deconstructing Amazon EC2 spot instance pricing. ACM Transactions on Economics and Computation, 1(3), 16.
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58.
Amazon (2014) Amazon EC2 Spot Instances. [Online] Available from: http://aws.amazon.com/ec2/purchasing-options/spot-instances/ [Accessed 25 August 2014]
Anderson, C. (2009). Free: The future of a radical price. Random House.
Dhiman, A. (2011). Analysis of on-premise to cloud computing migration strategies for enterprises (Doctoral dissertation, Massachusetts Institute of Technology).
Durkee, D. (2010). Why cloud computing will never be free. Queue, 8(4), 20.
Google (2014) Google Cloud Platform [Online] Available from: https://cloud.google.com/products/compute-engine/ [Accessed 25 August 2014]
Hosseini, H. (2014A) Google Slashes Cloud Prices: Google vs AWS Price Comparison. [Online] Available from: http://www.rightscale.com/blog/cloud-cost-analysis/google-slashes-cloud-prices-google-vs-aws- price-comparison [Accessed 25 August 2014]
Hosseini, H. (2014B) AWS Responds with Price Cuts: Google vs AWS Pricing Round 2. [Online] Available from: http://www.rightscale.com/blog/cloud-cost-analysis/aws-responds-price-cuts-google-vs-aws- pricing-round-2 [Accessed 25 August 2014]
Puri, R. (2014) Cloud, Analytics, Mobile, And Social: Convergence Will Bring Even More Disruption, .Fobes Magazine [Online] Available from: http://www.forbes.com/sites/oracle/2014/05/06/cloud-analytics- mobile-and-social-convergence-will-bring-even-more-disruption/ [Accessed 25 August 2014].
Marn, M. V., Roegner, E. V., & Zawada, C. C. (2010). The price advantage. John Wiley & Sons.
Microsoft (2014A) How to buy Azure. [Online] Available from: https://azure.microsoft.com/en- us/pricing/purchase-options/ [Accessed 25 August 2014]
Microsoft (2014B) Azure vs. Amazon Web Services. [Online] Available from: http://azure.microsoft.com/en-us/campaigns/azure-vs-aws/ [Accessed 25 August 2014]
Murphy, L. (2010) The Reality of Freemium in SaaS. [Online] Available from: https://s3.amazonaws.com/16v/The-Reality-of-Freemium-in-SaaS.pdf [Accessed 25 August 2014]
Nagle, T. T., Hogan, J. E., & Zale, J. (2011). The strategy and tactics of pricing: A guide to growing more profitably. Pearson.
Porter, M. E. (1980). Competitive strategy: Techniques for analyzing industries and competitors. FreePress, New York.

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Rackspace (2014) Cloud Servers Pricing. [Online] Available from: http://www.rackspace.co.uk/cloud/servers/pricing [Accessed 25 August 2014]
RightScale (2014) Cloud Pricing Trends. [Online] Available from: http://assets.rightscale.com/uploads/pdfs/Cloud-Pricing-Trends-White-Paper-by-RightScale.pdf [Accessed 25 August 2014]
Schadler, T. (2009). Should Your Email Live In The Cloud? A Comparative Cost Analysis. Forrester Research.
Seufert, E. B. (2013). Freemium Economics: Leveraging Analytics and User Segmentation to Drive Revenue. Elsevier.
Stadil, S. (2013) By the numbers: How Google Compute Engine stacks up to Amazon EC2 [Online] Available from: http://gigaom.com/2013/03/15/by-the-numbers-how-google-compute-engine-stacks- up-to-amazon-ec2/ [Accessed 25 August 2014]
Teksystems (2013) Windows Azure vs. Amazon AWS [Online] Available from: http://www.slideshare.net/tekcraft/azure-vsamazon [Accessed 25 August 2014]
Varian, H. R. (1997). Versioning information goods. [Online] Available from: http://people.ischool.berkeley.edu/~hal/Papers/version.pdf [Accessed 25 August 2014]
Wilson, F. (2006) My Favorite Business Model. [Online] AVC Blog, available from: http://avc.com/2006/03/my_favorite_bus/ [Accessed 25 August 2014]

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7 APPENDIX TWO: DETAILED ANALYSIS OF INDUSTRY PLAYERS
To help us better get a feel for the industry, we compiled the following table after a careful analysis of industry reports. Amazon EC2 Google Compute Engine (GCE) Microsoft Azure Rackspace Unique Selling Proposition Being able to bid on spare capacity in the Amazon cloud through their “spot” rates. Transparent pricing – Google took the Amazon EC2 pricing model and simplified it Focus is on helping customers build mixed public- private clouds. Differentiated through the support services offered, they call it “fanatical support”. Advantages
Mix of different tiers of quality of service allows users to design cloud applications that span multiple tiers and therefore use the cheapest quality of service for the lowest priority work, optimising their resource utilisation and reducing running costs.
Google bills in minute-level increments (Amazon uses hourly increments) and gives automatic discounts (Amazon requires up front payments to secure the best discounts).
GCE has many technical advantages over EC2 and other rivals,
Makes sense for developers and organisations that have an existing Microsoft-based infrastructure.
Rackspace offer a value-added advisory service to help customers get up to speed in using utility computing at scale. Disadvantages Amazon’s pricing structure requires an up front payment on their “Reserved Instances” tier to access the lowest rates, and this locks in users over 1 to 3 year periods. Unlike Amazon, Google does not sell capacity through auctions. May not scale as well as other solutions. Organisations with a clean slate may want to look at other solutions if they do not have legacy issues. Amazon and Google provide platforms that have large “ready made” pieces that make it easier to construct big applications.

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Amazon EC2 Google Compute
Engine (GCE)
Microsoft Azure Rackspace
Free tier? Yes – for low
utilisation
Yes – for GCE’s
sister product,
the Google App
Engine.
Yes – there is a
free tier for low
utilisation sites.
Also, startups that
qualify get free
monthly credit
under the
“BizSpark”
program
No.
Sources Amazon (2014) Google (2014)
Stadil (2013)
Microsoft (2014A)
Microsoft (2014B)
Rackspace (2014)
Other references: Dhiman (2011), Durkee (2010) and Teksystems (2013)
Cloud computing value proposition
US$ cost per user per month
On-premises
Google
Reference value
Subscription - 4.17
Server hardw are and OS 0.56 -
Server sof tw are 3.61 -
Client sof tw are 3.49 -
Storage 1.23 -
Filtering 2.99 -
Archiving 8.89 3.75
Staf f ing 4.41 0.55
Total 25.18 8.47
Source: Schadler (2009)

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8 COPYRIGHT NOTICE
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.