In-house AI vs. AIaaS: How Can Businesses Make the Best Decision?

As the playing field gets leveled and organizations irrespective of their size and domain enter the AI fray, the resultant technology growth is accelerating the already growing influence of AI in diverse applications and fields. 

The landscape of the services industry has been changing rapidly spurred by newer more effective business models promising greater value and RoI. One such driver is the transformation of traditional products and services sales into the “as a Service (aaS)” model. For organizations looking to begin, accelerate, or solidify their digital transformations, the “Anything as a Service” (XaaS) ecosystem promises flexibility, cost effectiveness and scalability while also leveling the playing field for organizations to benefit from the latest and greatest technological advances.

The most widely talked about examples of aaS are IaaS (Infrastructure as a service), PaaS (Platform as a service) and SaaS (Software as a Service). But as many businesses are realizing, the core value proposition of any aaS: to offer services on the go mostly through the cloud, is actually applicable beyond just traditional information services. AIaaS is one such example of XaaS which is an offering of Artificial Intelligence (AI) tools and frameworks as a service for a fee or on a subscription basis. Like all aaS offerings, AIaaS allows organizations looking to invest in AI technology to do so without upfront costs or resource commitments.

Although AIaaS is a fairly nascent field, the umbrella term AIaaS encompasses a wide variety of AI service offerings. On the one extreme, certain AIaaS solutions offer out of the box and ready to use pre-trained AI models for very specific but ubiquitous tasks such as voice to text, translation and object detection in the form of APIs (Application Programming Interfaces). Such offerings can be attractive for organizations with very specific AI needs aiming for rapid prototyping or deployment. On the other end of the spectrum, AIaaS can also refer to certain end-end ML development platforms which offer pre-trained and customizable models within an easy-to-use framework for application development. These frameworks are more applicable for organizations that have a team of developers working on building full-fledged AI applications to suit their specific needs.

To understand why AIaaS is gaining traction and is likely to grow rapidly in the next few years, it is pertinent to understand what it means and what it takes to build an AI solution. AI’s claim to fame really comes from its ability to learn complex patterns and relationships from large amounts of what is referred to as training data. Sounds innocuously  simple, but there are several steps in this process that can be tricky to get right. Assuming that the problem has been defined correctly, it is often not straightforward to find the right training data and the right AI model, not to mention access to the sometimes massive compute resources required for training. The more complex or niche the application, the longer and more arduous the process of developing a solution.

For many organizations whose core business is not technology, the time and effort required to develop an AI application in-house from scratch can be prohibitive. That is on top of the even more challenging task of hiring and retaining the right AI talent. AIaaS offers an attractive alternative approach, one that requires very little up-front commitment in terms of investment, resources or time to evaluate an AI solution. There is no doubt that for many different applications, AIaaS can be a great option for proof of concept or rapid prototyping. But if a specific application area also happens to intersect with currently active areas of AI research, AIaaS can in fact be a perfectly viable long-term option as well. The added advantage being that AIaaS offerings typically also provide regular updates in keeping with the state-of-the-art AI research work, thus guaranteeing that users of the service benefit from the best performing AI models.

While AIaaS sounds like a quick and easy solution, it is not necessarily a panacea for all AI needs. Niche applications outside of the more common areas of AI research such as NLP or computer vision are often underserved by existing AIaaS offerings. While customized solutions are offered today by several specialized AI consultancy organizations, any requirement for custom or proprietary data makes it a tricky proposition considering data privacy or security implications. The other factor to consider is the economics of AIaaS versus in-house AI development. On the one hand, AIaaS needs very little initial investment and allows for flexibility in usage with simple pay-as-you-go subscription models.

On the other hand, developing AI applications in-house may need capital investment in resources and even dedicated infrastructure but come with much smaller running costs in the long run. This difference then needs to be considered in the context of long-term cost vs value. For organizations looking to monetize AI solutions, the trade-offs between the two options needs to be balanced against the monetization potential of the application and the risk associated with such forecasts.

While the pros and cons of opting for AIaaS for many organizations continues to be a topic of debate, one thing is very clear. AI is no longer the prerogative of a few technology organizations with access to massive amounts of data. AIaaS with its very existence, is democratizing the use of AI in ways unfathomable just a few years back. As the playing field gets leveled and organizations irrespective of their size and domain enter the AI fray, the resultant technology growth is accelerating the already growing influence of AI in diverse applications and fields.

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