4 Reasons why AI/ML is not the new SaaS?

AI is predicted to be one of the top segments in cloud computing. However, AIaaS and SaaS could not have been more different.

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Over 40% of companies plan to deploy AI solutions by the end of this year, according to the recent Gartner Survey. However, the high infrastructure costs and needs for AI/ML expertise are daunting for most organizations. That’s why AIaaS (AI-as-a-service) or MLaaS began to emerge.

Over 40% of companies plan to deploy AI solutions by the end of this year — Gartner.

Popularized by Salesforce, SaaS refers to a licensing model in which software is centrally hosted for customers to access via a browser. SaaS companies also sometimes offer free trials (freemium) to encourage adoption.

Recurring revenue and decreasing costs make the SaaS business model more predictable, profitable and scalable. Therefore, it has become an extremely attractive model for both entrepreneurs and investors.

Since Salesforce went public in 2004, there have been over 70 SaaS IPOs. On average, SaaS companies have significantly outperformed the market. SaaS companies tend to have higher gross margins and lower R&D expenses because they don’t need to support multiple versions or technology stacks.

Similarly, AIaaS allows companies to utilize off-the-shelf AI solutions instead of building their own teams and infrastructures from scratch. AIaaS provides better scalability and flexibility to users by minimizing the upfront investment.

AI will increase 5X from 2019 by the year 2023.

AI enthusiasts also expect that AIaaS business model can help drive adoption and profitability. Gartner’s prediction that cloud-based AI will increase 5X from 2019 by the year 2023 seems to confirm the trend.

Companies from tech giants like Amazon and Microsoft to startups started to offer AIaaS including chatbots, digital assistants, cognitive APIs, and machine learning frameworks. Leveraging these services can simplify the complicated process and lessen the computation burden of deploying AI.

AIaaS seems to be a magic pill to solve all your problems. It enables a rapid and cost-saving AI deployment so companies don’t need to depend on in-house AI experts inhouse who are currently lacking everywhere. Hosting models in the cloud so they can continuously improve with more data also brings out the biggest benefits of ML.

However, the reality is, deploying AIaaS poses many more challenges than SaaS. And here’s why.