AI as a Service (AIaaS): Top Things to Know

The widespread adoption of artificial intelligence (AI) technology is fueled by the growing pressure to discover solutions to automate repeating queries and expedite growth. Employed by marketing professionals, customer support (CS) teams, programmers, and other professionals, AI models demonstrated an ability to learn using datasets. However, implementing AI-driven solutions has proved to be a costly endeavor, especially for small and medium-sized enterprises (SMEs). The introduction of AI as a service (AIaaS) enabled them to leverage AI solutions by ordering the services offered by third-party providers.

Utilizing AIaaS, companies reduce expenses, optimize workflows, and expand capabilities to scale up without significant investments. Employing AIaaS facilitates testing out various machine learning (ML) algorithms, accessing dedicated software, and using expensive hardware to solve resource-intensive tasks. According to Statista, the AIaaS market capitalization is expected to exceed 50 billion U.S. dollars by 2028. In this article, we will explore the meaning of AIaaS, outline the key selling points of such services, and consider various types of cloud-based solutions available to SMEs and large companies.

What is AIaaS?

Artificial Intelligence as a Service is a term encompassing cloud-based platforms offering businesses an opportunity to minimize risks and integrate pre-designed AI products into their ecosystems without hiring teams of experienced developers. Achieving sustainable growth requires substantial investments, but outsourcing AI facilitates accessing AI-powered tools at a fraction of the cost.

Building and training AI models require time and advanced skills, but AIaaS fosters AI adoption, offering pre-tested products to SMEs and other entities. Trusted third-party vendors provide ready-made frameworks based on edgy technologies, from AI to robotics. AIaaS streamlines AI implementation, offering an easy way to utilize pre-trained models to increase a company’s operational efficiency and productivity.

The extreme adaptability of such tools and their cost-effective deployment in the cloud are the main factors behind the steady growth of the market. Microservices enable vendors to create AI solutions comprising a multitude of independently deployable services. AIaaS has many use cases, including CS, data analysis, and task automation.

Below, we have briefly outlined the main types of AIaaS utilized by firms of all sizes across various industries.

We are confident that we have what it takes to help you get your platform from the idea throughout design and development phases, all the way to successful deployment in a production environment!

Chatbots

AI-powered bots handle large volumes of queries even during peak hours, enabling CS teams to increase client satisfaction, enhance engagement, and foster stronger relationships with clients.

Serving as digital assistants, customer service chatbots improve every step of a client’s journey by providing context-relevant replies and personalized recommendations. Utilizing Natural Language Processing (NLP) algorithms, chatbots generate human-sounding replies 24/7, handling up to 92% of conversations annually.

Application Programming Interfaces (API)

An API facilitates communication between apps. For instance, aggregation sites enable users to access information collected from various platforms in one place. Conversational AI and Computer Vision tools also serve as API examples.

Machine learning (ML) frameworks

Employed by firms to analyze their datasets and unveil specific similarities, these products learn on their own. Besides using pre-trained models, companies employ custom frameworks built from scratch to achieve their strategic goals.

No-code solutions

Firms lacking resources to implement AI models order ML services with pre-built models and customizable templates. They adjust them to their liking without hiring experienced developers.

AI as a Service (AIaaS): Top Things to Know

Why Use AIaaS?

Until 2033, the AIaaS market is expected to grow by 36.8% annually. Gaining in-depth knowledge about AIaaS and its potential role in process optimization is a must for SMEs aiming to maintain a cutting edge.

Eliminating the necessity to hire top programmers knowledgeable about AI, AIaaS enables companies to stand out among their competitors without spending valuable resources on the deployment of AI-driven tools.

Here are the main upsides to utilizing such services:

Instant access to complex infrastructure: Running advanced AI and ML models is impossible without powerful GPUs. However, SMEs often lack the necessary resources to invest in high-end hardware, which prevents them from leveraging recent advancements.
Transparent pricing: Besides allowing companies to save money and avoid hiring experienced programmers, third-party service providers charge their clients only for the AIaaS they require. Customers select specific options they are interested in and pay fees depending on their chosen pricing models. They may inform providers about their interest in machine learning products.
Affordability: Building and maintaining infrastructure demands capital investments in hardware and software. However, when a firm orders AIaaS, building on-premise data centers is unnecessary, as it can use third-party cloud-based services instead. As AIaaS providers regularly update their systems, their clients save money on operational costs.
Extensive customization: Some cloud-based platforms enable users to adjust their pre-developed AI solutions and add humans to the loop to ensure the reliability of results. The approach facilitates training AI models based on the available feedback and improved datasets.
Intuitive functionality: Even if a company does not have a team of employees with extensive technical backgrounds and top-notch coding skills, it can still install and deploy AI software due to its user-friendly interface. Many ML services include pre-programmed and custom models. Their drag-and-drop functionality facilitates launching projects without professional assistance.
Scalability: SMEs aiming to expand their outreach face many obstacles in handling an increasing number of queries without hiring personnel. However, AIaaS allows companies to scale up operations at a low cost.

Despite numerous advantages, critics argue against using AIaaS platforms without finding a way to protect user data. The most common arguments against such services are the following:

Increased reliance on AI as a service provider: When a vendor faces issues, the quality of AI services may degrade. Besides, any adjustments in providers’ policies may cause unforeseen consequences.
Data storage: There is no way to ensure that cloud-based services will strictly follow data privacy guidelines, so businesses should take the necessary steps to guarantee that sensitive user data remains safe.
Customization issues: Unlike built-from-scratch AI models, pre-programmed products are hardly suitable for SMEs with specific requirements.

Nevertheless, despite possible security risks, AIaaS solutions remain a cost-effective alternative to custom products, allowing businesses to leverage AI to handle large volumes of customer queries and work with massive datasets.

Top AIaaS Vendors

Choosing reliable AI tools based in the cloud may be a cumbersome task unless a firm considers what improvements it needs to make to scale up without hiring teams of in-staff developers and other employees.

Among the powerhouses providing cloud computing services are the following:

Microsoft Azure: The company caters to the needs of developers and data scientists, providing a range of customizable AI models and APIs. Azure AI Search facilitates analyzing various types of data, while AI-driven virtual assistants improve the effectiveness of CS workflows by processing queries in multiple languages. By configuring Azure APIs to their liking, firms streamline content moderation. Azure ML services make it easier to create and train ML models.
Amazon Web Services (AWS): Sagemaker enables companies to access top-level ML services in the cloud. Lex facilitates the development of advanced chatbots and their integration with legacy systems, while Polly streamlines the utilization of speech-enabled digital products. Rekognition includes services for those who are interested in computer vision tools and want to deploy AI models trained on Amazon datasets. Clients can also utilize custom datasets.
Google Cloud: The AI platform stands out for the variety of services it provides. Enabling clients to create and deploy ML solutions at scale. The AI Hub enables users to access pre-made AI algorithms and pipelines. Conversational AI tools are designed to optimize CS procedures and provide assistance to users.

Securing assistance from trusted third-party services allows companies to expand their operations with minimal funding.

Final Thoughts

Finding the right AI as a service provider may become laborious if you are interested in custom AI models fully compliant with the existing regulations. At Global Cloud Team, we have a team of expert developers knowledgeable about all the intricacies of AI technology. Due to our experience with enterprises from different markets, we devise personalized AI integration roadmaps and assist our clients on every step of their journey.

Recognizing that the future of the AIaaS industry is connected to AI, the Internet of Things (IoT), ML, and algorithms, we build innovative AI models that seamlessly integrate into existing systems. We provide top-notch services to our clients, aiming to assist them with implementing various complex AI models to enhance their workflows’ efficiency.