IaaS Business Model

IaaS stands for infrastructure as a service. Together with other “as-a-service” models, the basic premise of this model is to offer a solution to the final customer without having to host it on-premise, with complex implementations and large overhead. The IaaS model provides virtualization, storage, network, and servers where the final user/customer will handle applications, data, operating systems, and run times.

Breaking down the IaaS business model

IaaS vs PaaS vs SaaS
Source: Red Hat, What Is IaaS?
Above the core difference between IaaS, on-side, PaaS and SaaS.
Infrastructure as a Service — IaaS includes servers and storage, networking firewalls and security, and datacenter (physical plant/building). PaaS includes IaaS elements plus operating systems, development tools, database management, and business analytics. SaaS includes PaaS elements plus hosted apps.
Source: Microsoft Azure, What Is IaaS?
The different layers of companies built on top of the various “as-a-service” model. From IaaS which is the the bottom, to PaaS and SaaS.
Example of a SaaS model built on top of IaaS and PaaS (Source: C3.ai)

Where in an on-premise solution the company will have to control, manage and implement the full stack:

  • Applications.
  • Data.
  • Runtime.
  • Middleware.
  • O/S.
  • Virtualizations.
  • Servers.
  • Storage.
  • Networking.

In IaaS the company/customer will have to worry only about:

  • Applications.
  • Data.
  • Runtime.
  • Middleware.
  • O/S.

Where the IaaS provider will take care only of:

  • Virtualizations.
  • Servers.
  • Storage.
  • Networking.

Why do companies leverage on IaaS?

Usually, businesses leverage IaaS for testing and faster deployment, hosting, web apps development and more. And some of the benefits for using IaaS is the need for companies to reduce the capital expenditure coming with setting up a complex platform on premise, before seeing its benefits, in the first place.

Therefore, this might help companies to innovate faster, and with lower expenses, more security, scalability, faster deployments, expanded use cases across the organization.

Of course, IaaS also comes with less control. In a market skewed toward rapid innovation, more and more companies, also at the enterprise level are leveraging on that.

Connected Business Frameworks

Customer Lifetime Value

One of the first mentions of customer lifetime value was in the 1988 book Database Marketing: Strategy and Implementation written by Robert Shaw and Merlin Stone. Customer lifetime value (CLV) represents the value of a customer to a company over a period of time. It represents a critical business metric, especially for SaaS or recurring revenue-based businesses.


AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.

Machine Learning

Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.

Continuous Intelligence

The business intelligence models have transitioned to continuous intelligence, where dynamic technology infrastructure is coupled with continuous deployment and delivery to provide continuous intelligence. In short, the software offered in the cloud will integrate with the company’s data, leveraging on AI/ML to provide answers in real-time to current issues the organization might be experiencing.

Continuous Innovation

That is a process that requires a continuous feedback loop to develop a valuable product and build a viable business model. Continuous innovation is a mindset where products and services are designed and delivered to tune them around the customers’ problems and not the technical solution of its founders.

Technological Modeling

Technological modeling is a discipline to provide the basis for companies to sustain innovation, thus developing incremental products. While also looking at breakthrough innovative products that can pave the way for long-term success. In a sort of Barbell Strategy, technological modeling suggests having a two-sided approach, on the one hand, to keep sustaining continuous innovation as a core part of the business model. On the other hand, it places bets on future developments that have the potential to break through and take a leap forward.

Business Engineering


Tech Business Model Template

A tech business model is made of four main components: value model (value propositions, missionvision), technological model (R&D management), distribution model (sales and marketing organizational structure), and financial model (revenue modeling, cost structure, profitability and cash generation/management). Those elements coming together can serve as the basis to build a solid tech business model.

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