Cloud Pricing Models

Analysis, Comparison and Trends for IaaS and SaaS vendors

Introduction

The current pricing of Cloud based services offers a wide range of configuration, choices and price points for the users. This, in turn creates lot of choice (and confusion) to the buyer as it is difficult to measure the value. 

The objective of this article is to analyze the existing pricing models and reasons behind the pricing diversity. In the following 4 sections, this article attempts to:

1)    Compare approx. 27 models of pricing derived from the scholarly article

2)    Recommend a derived pricing model relevant to the cloud service.

3)    Validate the model by mapping it to the pricing of services by few key vendors

4)    Analyze reasons behind the pricing in use by various vendors and future trends.

1. Generic Pricing Models

• Osterwalder (2004) classified the pricing models around fixed, differential and market based pricing. He suggested that the fixed and differential pricing mechanisms produce prices that depend on customer/ product / Service characteristics, volume, but are not based on real-time market conditions. Market based pricing stands for pricing mechanisms that produce prices based on real-time market conditions.
• Harmon et al. (2009) suggested a factor based pricing mechanism (cost + value).
• Denne (2007) discussed various advanced ways to implement pay per use pricing mechanism.
• Paleologo (2004) suggested that traditional pricing mechanisms such as cost-plus pricing may be inadequate in on-demand services environment due to dynamic factors in cloud computing as shorter contracts, reduced switching costs, customer lock-in, uncertain demand, and shorter life cycles etc.
• Some authors have also suggested other ways of classification such as resource based Vs. Feature based or Operational (Pay per Use) Vs. Availability (subscription).

2. A Derived Pricing Model for Cloud Computing 

From this article’s perspective, we classify the pricing models which derived from the discussed pricing mechanisms.

1. Usage Based Pricing (Pay per use) with factor based variations.
2. Subscription Pricing (Commitment / Reserve / Availability Pricing) with factor based variations.
3. Market Based Pricing
4. Pricing Strategy (More relevant from a marketing perspective)

Following table summarizes the derived pricing model:

3. Validation of the Derived Pricing Model

The table below provides a mapping of the derived pricing model to the pricing of 9 cloud offerings by few key vendors.

4. Analysis and Future Trends  

• Pricing matrix is more complex for IaaS compared to SaaS as service offering can be better differentiated from an end user perspective.
• Within IaaS, compute pricing has more variety compared to storage or bandwidth. The reason for this variation is the fact that storage and bandwidth offer very less flexibility in terms of configuration as compared to compute service offering which is used to create differentiation.
• In compute services, companies are trying to offer pseudo differentiation by packaging / configuring the service features in various ways. This is leading to more confusion for the buyers as it difficult to measure and value the offering in a meaningful way. Measuring and comparing CPU, memory, speed etc. is difficult for an offering and to also to make a decision since the compute instance is a vendor specific configuration in most cases.  
• As the market matures and buyers eventually become smarter, the pricing of the compute services will become smarter and simpler for the buyer. The differentiation will be done on more tangible parameters such as guaranteed SLA, support, processor speed, performance etc. rather than the configuration and packaging which look almost the same (or ‘different’ from the vendor’s perspective).
• The trend for compute services at the moment is lead by Amazon (being the first movers) and all others following them with  more or less same kind of pricing and configuration models. They were the first to bring market based pricing (spot instances) which is yet to be copied by other vendors. This trend should continue.
• As services become commoditized, service brokers / exchanges or ecosystems will emerge (many already in place as jam cracker, thinkgrid etc). This will be the starting point of more sophisticated market based complex pricing models for the exchanges and the brokers. The complexity will however be shielded from the end customer / buyer. Pricing definition will shift from the service provider to the marketplace requirements. Outside the market place, companies will deploy more established pricing strategies to gain market share including competitive pricing, discount schemes, customer loyalty, volume based discounts, yield management the etc. which is mainly the usp of enterprise services at the moment.
• SaaS market is highly feature / functionality based and pricing is simpler and easier for the user as they can easily relate the value to the features / functionality. As the competition increases, companies will deploy new pricing strategies to gain market share including competitive pricing, discount schemes, customer loyalty bonuses, volume based discounts etc.
• PaaS needs a different treatment and is still in infancy stage.
• With multiple providers competing to deliver very similar commoditized services in a high price sensitive environment, the market will able soon approach a perfect competition like situation and the “Me Too” type of offering is not going to work. This will trigger : 

         a) Price war (And possibly the “Race to Bottom”) unless the vendors leverage  
             economies of scale, shared infrastructures, reduced deployment costs, and free and 
             open source versions etc to beat the competition. 
         
         b) Value Added / differentiated offering that can be tangibly defined, measured and can 
              be  paid in terms of support, maintenance, SLAs, and performance etc. will charge a 
              premium.

 

References

[1] Osterwalder, A. (2004). The Business Model Ontology - A Proposition In A Design Science Approach. Doctoral thesis. University of Lausanne.

[2]   Denne, M. (2007). Pricing utility computing services. International Journal of Web Services Research, Vol. 4, No. 2, pp. 114—127.

[3]  Harmon, R., Demirkan, H., Hefley, B. & Auseklies, N. (2009). Pricing Strategies for Information Technology Services: A Value-Based Approach. Proceedings of 42nd Hawaii International Conference on System Sciences, pp. 1—10.

[4]  Paleologo, G. (2004). Price-at-Risk: A methodology for pricing utility computing services. IBM Systems Journal, Vol. 43, No. 1, pp. 20—31

About Me

    

Sunil has over 15 years experience in Information Technology providing leadership, management, planning, system development and engineering, training, people development, methodologies, and process support. He has worked in the area of internet based technology solutions, e-commerce applications, product development, product maintenance, e-business platforms, portals, collaboration, content management, and business intelligence. He currently works for Colt Technologies Services, Bangalore, India as Head of Systems Development and Support. Sunil holds an Executive MBA (IIM Calcutta), Masters in Technology (IIT Kanpur) and a Bachelor of Engineering in Electrical Engineering. He also holds a diploma in entrepreneurship from EDI Ahmedabad.

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