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The initial cost of jumping into generative AI can be prohibitive. Whether developing a proprietary large language model (LLM) or investing in APIs to connect with a major vendor's LLM, the numbers add up. Enter artificial intelligence-as-a-service (AIaaS).
AIaaS is a cloud-based service offering artificial intelligence (AI) outsourcing. As with other software "as-a-service" offerings, AIaaS removes the up-front investment for businesses and provides access to AI for experimentation or production for large-scale use cases, with nominal risk for the business licensing the service.
The State of Generative AI Today
However, that the current expense involved with generative AI is normal and that, for the moment, the industry is focused on the application of generative AI to existing and current tech investments, said David Moyers, a VP of AI, Analytics and Legal Tech Engineering at OpenText.
A long learning curve tends to hinder any technology as it transitions from early adoption to mainstream acceptance, he continued, and generative AI is no exception.
“With generative AI, the first step is for vendors to incorporate AI to bring real-life working applications to the next level,” he said. He noted that in the first wave, businesses will turn to software vendors to determine how they have incorporated generative AI capabilities into the products consumers use and trust daily.
“You'll see generative AI added to cloud-based CRM, content management, human resources solutions, competitive intelligence,” Moyers said. “In fact, we've already begun to see more generative AI-focused applications and utilities available via SaaS.”
The enhancements improve ease of use and lower entry costs and barriers to full, mainstream adoption, he added. In turn, generative AI features make software more desirable and competitive. In later waves, generative AI will be as accepted as spell-checker and auto-save capabilities of applications we use today.
Related Article: Thinking of Building an LLM? You Might Need a Vector Database
AIaaS, Democratizing Access to Generative AI
AIaaS is revolutionizing the way we approach technology adoption, said Spectrum Search CTO Peter Wood.
Historically, implementing an AI-based system required a significant capital investment, a team of dedicated experts, and time — luxuries many businesses cannot afford. AIaaS democratizes access to AI technologies by offering scalable, cost-effective solutions, he said.
“AIaaS enables companies to test the waters without committing to a full-scale, in-house AI operation. You get the technology as a service, pay for what you use, and can scale up or down based on your needs,” Wood said. "This flexibility can be a game-changer for small and medium-sized businesses, but also for larger enterprises looking to pivot quickly."
AIaaS can be particularly useful in bringing generative AI to a wider audience. Wood believes the potential of generative AI to create entirely new content, be it text, images or complex data structures can be outweighed by the significant compute power and expertise required to run such models. AIaaS providers are therefore enabling businesses to tap into capabilities that they otherwise could not afford or maintain. "It also offers an environment to experiment and innovate," he continued.
But Wood tempered his enthusiasm with a note of caution: "While it offers significant benefits, it can also introduce challenges around data security, governance and ethical considerations. If the AI model you are accessing through AIaaS is trained on biased or flawed data, it can perpetuate harmful stereotypes or make incorrect assessments."
Businesses must take a measured approach, being mindful of the ethical and security considerations that come with adopting any AI solution, he added.
Related Article: AWS's Diya Wynn: Embed Responsible AI Into How We Work
AIaaS Advantages
AIaaS provides a range of compelling advantages, said Ivan Chiklikchi, a software engineering and resource manager at EPAM.
The obvious gains, he said, are that AIaaS removes the formidable obstacles for both individuals and businesses eager to use AI's potential. It facilitates the exploration and implementation of AI without the need for significant upfront investments in hardware, infrastructure or specialized AI expertise.
It also enables the adoption of subscription-based pay-as-you-go models, which reduces the costs associated with integrating AI into diverse processes. This is particularly advantageous for small businesses and startups with constrained budgets. But it provides other advantages that aren't cost related too, he said, namely:
1. Seamless Scalability
AIaaS solutions are typically scalable, allowing organizations to expand their AI capabilities as their requirements grow, Chiklikchi said. This scalability sidesteps the complexities of procuring and maintaining additional resources.
2. Customizable Flexibility
AIaaS platforms also offer a diverse array of AI services, encompassing natural language processing, computer vision, machine learning and predictive analytics, he said. This adaptability enables organizations to cherry-pick and tailor AI solutions to align precisely with their unique needs.
3. Access to Expertise
This is one of the biggest challenges facing organizations interested in incorporating LLMs. AIaaS provides invaluable access to the specialized expertise of AI service providers, particularly for organizations lacking in-house AI specialists. It permits them to harness the knowledge and experience of established AI vendors.
“AIaaS emerges as a pivotal enabler in democratizing and extending the application of generative AI. Generative AI, exemplified by models like GPT-3, has highlighted its potential across various domains, including content generation, chatbots, and natural language comprehension,” Chiklikchi said.
Through AIaaS, generative AI can be seamlessly integrated into a multitude of sectors, spanning content creation, customer support, data analysis and automation. This diversity enables new use cases that might otherwise remain unrealized without the accessibility of AIaaS.
Finally, these platforms expedite the prototyping and experimentation of generative AI solutions. Organizations can swiftly develop and assess these solutions' suitability for specific use cases, without the commitment of extensive long-term resources.
“AIaaS represents a transformative force that not only diminishes entry barriers but also drives down costs, enhances flexibility, and offers access to valuable expertise,” he added. ‘Moreover, it democratizes the realm of generative AI, paving the way for broader and more innovative applications across industries.”
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Fine-Tuning LLMs
One less obvious advantage comes from an important feature of these platforms known as fine-tuning, said Interzoid founder and CEO Bob Brauer. This allows the base linguistic models to be supplemented with additional, task-specific training data, enhancing their performance in specialized areas or where standard models might not fully meet an organization's needs.
Fine-tuning augments the foundational architecture of a LLM with a custom layer of training data to achieve more nuanced, AI-generated results for a given use case. This allows organizations to refine pre-trained models for specific purposes without the need to build or overhaul expansive models, Brauer continued. With this process the organization introduces their own custom training data to the existing powerful linguistic foundational models, which makes it especially ideal for short-term projects where constructing a new model from the ground up would be impractical, he said.
AIaaS solutions are also commercially available for more resource-intensive AI applications, he said, such as those in the medical field, visual recognition or autonomous vehicle customization, which often demand specialized hardware or detailed model modifications. While some of these offerings, like those offered by AWS, have a broad application that can then be customized, the industry-specific alternatives can be useful in unique, or specialized use cases.
Like Wood, Brauer acknowledges AIaaS does have its challenges, particularly in more-regulated sectors where data privacy is important. In those cases, using external AI services can make compliance difficult, while shared models can create obstacles to those seeking to obtain a competitive advantage.
“AI-as-a-Service provides a fast and cost-effective route for organizations to tap into AI capabilities,” Brauer said. “It is ideal for those seeking immediate solutions without the need for exhaustive customizations. However, organizations with vast resources, or those facing strict customization or compliance demands, might find it more advantageous to develop their own in-house AI models and solutions.”