Cloud Computing vs AI Technology and Business Models

One of a Three Part Series

This blog post was written by DougL. Edits provided by friends. AI was not used to write or edit it. Browser-based technology with generative AI and ChatGPT was used to fact check.

Although the latest wave of artificial intelligence (AI) technology is in its infancy, I think it’s instructive to compare this emerging segment with the existing cloud computing business model.

Cloud computing customers pay only for the services used and there are no additional costs or termination fees when they stop using these cloud services. Typically pay-as-you-go options are paired with customized pricing options. Cloud services provide internet access to software apps — typically Software or Platform as a Service (SaaS or PaaS) - or online storage. The cloud services provider upgrades and maintains the software that runs the service, and that cost of which is built into their fees.

Cloud computing offers several advantages:

• lower upfront costs

• scalability

• the ability to access the software from anywhere with an Internet connection

• reasonable cost-of-ownership based on demand/use (i.e., low CapEx)

As you know, Amazon Web Services (AWS), Microsoft Azure and Google Cloud (GCS) are the three leading cloud computing providers. Examples of SaaS companies and technologies include: Adobe Creative Cloud, Dropbox, QuickBooks, Microsoft Office 365, Salesforce, Shopify, and Zoom, among many others.

The SaaS business model is measured through several Key Performance Indicators (KPI):

• Annual Recurring Revenue (ARR)

• Monthly Recurring Revenue (MRR)

• Customer Acquisition Cost (CAC)

• Customer Lifetime Value (CLV)

• Churn Rate

• Net Promoter Score (NPS)

• Average Revenue Per User (ARPU)

SaaS businesses use these KPIs to track performance and make data-driven decisions to aid growth.

The AI business model leverages AI technologies to deliver value to customers and has broad implications for business, from improving efficiency and productivity to creating new products and services.

AI business models include:

• AI as a service (AIaaS): Allows paid subscription access to AI-powered services like chatbots, image recognition, or natural language processing. Examples: Google Cloud AI Platform, AWS, Microsoft Azure, IBM Watson, and H2O.ai, among others.

• AI-powered products: Includes virtual assistants, personal robots, or autonomous vehicles, which are then sold directly to customers. Examples:

  • personal assistants liks Siri, Alexa, and Google Assistant; self-driving cars, trucks, and other autonomous vehicles (AVs)

  • smart home and healthcare devices

  • home and industrial robots

• AI-powered platforms: Search engines or recommendation engines offering AI-based services to customers or other businesses, including:

  • category leaders like Amazon, eBay, and Alibaba

  • social media platforms like Facebook, Twitter, and LinkedIn

  • search engines like Google and Bing, cloud services, for example, Azure, GCS, and AWS

  • healthcare platforms

• AI-powered data analytics platforms: Leverages AI to provide analytics for business insights into their own data, help them detect patterns, and ultimately make better decisions. These platforms include:

  • Tableau, Power BI, and Qlik,

  • predictive analytics tools such as RapidMiner, DataRobot, and H2O

  • natural language processing such as IBM Watson Natural Language Understanding and Google Cloud Natural Language AP

  • fraud detection and collaboration platforms like FiVerity

  • customer analytics tools such as Adobe Analytics and Google Analytics, which examine customer behavior and preferences to enable personalized customer experiences.

• AI-powered optimization: Optimize business processes like supply chain management, logistics, energy consumption, advertising, customer retention, or resource allocation. These platforms improve efficiency and reduce costs. Example: StormForge applied to Kubernetes.

ChatGPT has a couple of similar business model. (This will be the subject of a future blog post.) OpenAI has very successfully monetized their AI R+D in several ways, including technology licensing to other companies, particularly Microsoft and Bloomberg. They also offer a premium version called “ChatGPT Plus” with a larger and more powerful language model and additional features. Access to GPT’s API service requires a paid subscription.

AI businesses KPIs are established but are not as well-known as SaaS KPIs. Here are a few that are good measures of performance and growth potential:

• Revenue: Derived from sales of AI products and services including licenses sold

• Time-to-Market (TTM): The time it takes to develop and bring AI products and services to market

• Return on Investment (ROI): This can include revenue generated, cost savings, and other benefits resulting from AI implementation.

• Cost per inference (CPI) & Total Model Cost (TMC): Cloud providers typically charge for large language model (LLM) model usage based on the number of inferences or predictions made and their lifespan. The total cost of running the LLM model in the cloud divided by the number of inferences made over a specific period of time, such as a day, week, or month, is the CPI.

• Infrastructure, data transfer and operational costs are useful measures for the ongoing cost of LLM model cloud computing costs.

• R+D Cost: To remain competitive, AI businesses need to continually invent new products and improve established ones, the cost of which can be significant.

• Cost of Innovation: Measured by the number of new products or services launched, patents filed, and research papers published.

• Customer Acquisition Cost (CAC)

• Customer Retention

While there is some overlap between the AI / LLMs like ChatGPT and cloud computing / SaaS business models, they are very different. While SaaS companies have significant marketing and customer acquisition costs, we know that the path to profitability for AI / LLM / ChatGPT companies is more challenging thanks to significant R+D and operational costs.

Moreover and in sum, businesses moved to cloud computing / SaaS for agility and low CapEx; businesses are moving rapidly to AI / LLMs / ChatGPT because these models and large datasets have the potential to revolutionize businesses operations and customer interactions because they can process massive amounts of data and natural language input, and produce human-like responses.

Next week’s blog will focus on ChatGPT business models and subsequently we will post a blog on AI investment models in light of these technology and business model differences.

---