Artificial Intelligence as-a-service or AIaaS refers to a cloud-based model in which third-party providers offer ready-to-use AI tools and services to organizations so they can deploy, develop, train and manage AI models. It enables organizations of every size to implement and scale AI capabilities for a fraction of the cost of an in-house AI team.
AIaaS makes AI more accessible to companies – especially smaller ones – for many use cases, including process automation, customer service, sales and marketing, data analysis, and more. It also helps create a more level playing field, where smaller companies can effectively compete with their larger peers by taking advantage of the power of AI.
Between 2021 and 2028, the global AIaaS market will likely grow from $5.9 billion to a whopping $52.8 billion at a CAGR of 44.71%. This expected growth is unsurprising, considering the benefits of AIaaS, including greater accessibility to AI, lower cost, and easier customization. This article explores these benefits in detail.
Let's start by exploring the basics: what is Artificial Intelligence as-a-Service?
What is AI as-a-Service?
Most people are already familiar with the term Software-as-a-Service. To fully understand AI-as-a-service, we first need to introduce two more terms, so that the basic list we will build off looks like this:
- Software-as-a-service (SaaS)
- Platform-as-a-service (PaaS)
- Infrastructure-as-a-service (IaaS)
All these three categories are ways to use the cloud for business applications and use cases.
For example, SaaS is usually targeted at various types of business end users. PaaS is targeted at software developers with readymade tools to build, test, deploy, and maintain enterprise applications. Finally, IaaS products allow organizations to access infrastructure services such as storage and networking over the cloud, so they don’t have to invest in their own (costly) hardware.
AIaaS joins the as-a-service ecosystem with its offerings available in all three areas, SaaS PaaS and IaaS.
With SaaS AI offerings, users interact with an interface or app to upload data, access the available models, and generate insights.
In contrast, there is no interface with PaaS. Instead, users – who are usually developers – interact with PaaS AI through a set-up environment on one hand and an application programming interface (API) on the other to incorporate AI functionalities into their own apps. In effect, PaaS allows developers to add AI to their apps using built-in resources and without reinventing the wheel, that is, without having to build their own functions.
IaaS provides computing power to run demanding AI applications, most often on powerful GPU chips.
With AIaaS, any organization can access a wide range of ready-to-use AI products from third-party AI service providers or easily customize and scale their own solutions.
This is in line with the general as-a-service cloud model that enables organizations to access advanced technologies and tools on demand. The access is enabled by a cloud service provider (CSP) who is responsible for hosting and maintaining the underlying infrastructure. The CSP usually delivers the required service to the organization based on a pay-as-you-go monthly or yearly subscription.
This differentiates cloud-native services from on-premises implementations and allows companies to reduce their capital expenses, control their ongoing operational expenses, and also minimize their infrastructure management burden.
Let’s dive into more AIaaS benefits in the next section.
What are the 6 Benefits of AIaaS over In-house AI Development?
AIaaS is an excellent alternative to avoid all the challenges created by in-house AI development (see this section). It offers advantages for organizations looking to access AI capabilities without breaking the bank. These include:
1. Access to advanced technology at lower costs
AIaaS has made AI more easily accessible, thus levelling the playing field for smaller businesses. In the past, ML models required expensive machines with multiple fast GPUs in complex data centers.
But now, advances in cloud computing allow organizations of all sizes to harness powerful AI capabilities and advanced AI solutions at significantly lower costs. Moreover, with AIaaS, the investments required to build, test and deploy ML models are much lower than in in-house development.
2. Less need for advanced technical skills
Large vendors like Amazon, Google, or Microsoft as well as smaller niche vendors provide a wide range of tools and pre-trained models for many different use cases or even specific industries like financial services or healthcare. This means less need for machine learning expertise.
For instance, Flowable is an AIaaS offering to automate daily repetitive tasks in text, document, and image workflows. Another AIaaS solution is Ultimate AI, a chatbot service with a human-like AI that enables companies to send automated messages and make automated calls. Similarly, Viz.ai provides an AI-based platform to improve the coordination between frontline healthcare professionals and specialists and ultimately improve the quality of care provided to patients.
On top of that, many AIaaS even from the platform-as-a-service space offer no-code or low-code solutions, allowing companies to add AI capabilities to their own applications and make changes as required, even if they don’t have AI-skilled developers.
For example, BotX is a no-code AI platform for developing various types of AI solutions that build on technologies such as deep learning, NLP, or computer vision.
Since these platforms and tools are cloud-based, organizations don’t have to build or manage their own AI infrastructure so the need for infrastructure management talent or MLOps is reduced as well.
3. Faster development and ease of use
Due to simpler development, the time-to-market has also gone down. Pretrained models, no-code or low-code solutions paired with drag-and-drop interfaces or fully functional out-of-the-box solutions have taken development times from years to months and in some cases even weeks. All that with minimal training and onboarding of involved staff. But even with AI-as-a-service, there is a shorter (less customized) way of adopting AI and there is a longer (more customized) way.
4. Scalability and customization
Scaling AI solutions is a very different problem from developing a working AI model and requires different skillsets end expertise.
Thankfully, most AIaaS platforms are also built to scale. This means companies can implement and scale AI techniques as their needs, demands, or requirements change. Even better, they can scale up (or down) very quickly and at a fraction of the cost of a full in-house AI team.
The scalability of AIaaS solutions is one reason AIaaS is believed to “democratize” the AI landscape. Simply put, these solutions allow smaller companies to access the powerful, scalable capabilities of AI at low cost to develop their own solutions and compete with larger rivals in their industry.
5. Unlimited computing power
Many ML models require large amounts of training data to be processed by very complex algorithms so powerful CPUs and GPUs are needed to carry out the task. With AIaaS, cloud systems can quickly provide vast amounts of computing power and critical ML services like serverless computing and batch processing for data processing and model training. For example, the startup Stability AI uses 4000 Nvidia A100 GPUs running in AWS to train its AI models.
6. Transparent pricing and cost control
All large CSPs deliver AIaaS through a “pay-as-you-go” pricing model. Since organizations only pay for the AI resources they use, they can better control their AI spending.
4 Concerns with using AI-as-a-service
1. Security
Although cloud services are usually well secured and compared to some in-house operations that do have dedicated cybersecurity staff, the fact that you must rely on a 3rd party in terms of security and transfer your data to them raises security concerns that need to be addressed during the adoption and contracting processes. Companies should also focus on selecting companies that follow or are certified based on ISO 27001 or SOC 2.
2. Long-run costs
Companies need to not only evaluate set up and monthly costs of running an outsourced AI service but also networking costs for transferring large volumes of data which can in the long run create new cost barriers. For some applications, these costs became the primary driver of why large companies decided to scale back their use of AIaaS.
3. 3rd party dependency and lock-in
Like with most B2B software, also with AIaaS, there will always be vendor lock-in to some extent. Vendor lock-in means that a vendor might have some power over a client organization in terms of increasing prices because if the client decided to switch providers, the client would inevitably have to incur additional costs to do that. With AIaaS providers, these costs would include for example learning new PaaS environment and repetitive coding and set-up of APIs or even retraining of models.
4. Less control
Some companies, primarily those that have AI deeply embedded in their core operating models might need to maintain a higher degree of flexibility and control across the entire AI value chain from infrastructure to the platform to end-user software. For such companies, less control over this value stream might be prohibitive to adopting an AIaaS solution.
Examples and case studies of AI-as-a-service
Case study 1
PureTech Global, which offers telecommunications services in multiple countries, accesses advanced AIaaS technology from AWS to develop impactful, revenue-generating apps. AIaaS allows them to embed AI into their native workflows even without a dedicated data team or prior AI experience. The AIaaS project has generated more revenues for the company and allowed them to provide value-added services to PureTech’s customers.
Case study 2
FintechOS uses AIaaS from Microsoft Azure to make its Fintech services more accessible to its non-technical clients. With the help of AIaaS, FintechOS delivers a low-code digital creation platform that enables its clients to build end-to-end digital products in just a few weeks. They can automate existing processes and develop bespoke and cost-effective financial services using FintechOS’ AIaaS-enabled low-code personalization engine.
Case study 3
WildTrack, an environmental organization, uses cloud-based AI from SAS to monitor endangered species. AIaaS allows them to automatically track, identify, sort, and classify animal footprints and thus recreate some of the skills used by indigenous human trackers. Moreover, they can track these animals at scale and at a more rapid pace – both of which are essential for more efficient and effective animal conservation.
Case study 4
Youplus, an insurance company uses BotX to scour the internet and investment-related documentation to find mentions of the poor environmental behavior of some companies it has in their portfolios of investment products. It then automatically evaluates such mentions based on a set of criteria and either recommends or performs actions on behalf of compliance managers in the firm to improve Youplus’es compliance with EU regulations and the quality of their product offering.
All these case studies show the immense potential of AI products and solutions, and how these products and solutions can benefit enterprises and ultimately their key stakeholders.
What are the different popular categories of AI Services and Applications?
Many AIaaS services and products are currently available, so organizations have a wide option pool for their specific needs or applications. From final packaged products to tools and services that aid the AI development process, here are the most common categories.
Chatbots and Virtual Agents
As an example of a final packaged product, we can take probably the most common use case for AI that has been repackaged in hundreds of different products nowadays. These are chatbots and digital assistants which belong to the SaaS category as we have discussed above. Virtual agents are based on natural language processing (NLP) technology and understand spoken language and converse with customers in near-human ways. These bots, increasingly used for customer service applications, can learn from each conversation, and improve their capability to answer customer questions. Unlike human customer service agents, bots can work 24x7. They also enable companies to save time and resources and reassign their valuable human employees to more complicated, value-generating tasks while executing back-office tasks themselves automatically.
Cognitive Computing APIs
A category of products from the PaaS world that has gained high in popularity are various types of APIs that enable companies to incorporate AI capabilities into applications. These however require some level of coding capabilities. These API services allow developers to improve their applications with features such as text-to-speech, speech-to-tech, translations, entity recognition, sentiment analyses, question answering, computer vision, face APIs, personalizers, or for example anomaly detections.
ML Frameworks and Managed ML Services
Another popular group of ML frameworks enables companies to build custom models without the need to understand the complexities of very advanced algorithms that need to be involved. Frameworks such as TensorFlow, Keras, PyTorch, or Scikit With these services, companies can add rich ML capabilities to their business functions or processes using templates and pre-trained models.
Data Labelling and Classification
Our last example is outsourcing one of the key initial steps in most of AI development projects. Data labelling and classification are two of the most popular applications of AIaaS. Labelling data is essential for categorizing data and for training AI models. Similarly, content-based, context-based, or user-based data classification enables companies to organize and store data efficiently and discover critical insights.
Who are some of the advanced AIaaS providers and startups?
The three largest CSPs – Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) – offer some of the largest selections of AI tools and services through a subscription-based AIaaS model. Other large CSPs like IBM, SAP, Intel, Oracle, Hitachi, and Salesforce also offer many AI services and solutions for text recognition, the Internet of Things (IoT), computer vision, intelligent automation, and much more.
Many AI startups and smaller firms also operate in the global AIaaS space. This list of AIaaS vendors includes for example DataRobot, Flowbase, Clarif.ai, Obviously.ai, Element AI, ucfunnel, MindLayer, C3.ai, H20.ai, Assembly AI, Levity, Odus, or Viz.ai and many more. Many of these vendors not only build AI solutions, but also host them on their clouds, thus allowing their enterprise customers to tap into the AI landscape, and do so in a cost-effective, efficient, and always-on manner.
Together, these large and small players are shaping the future of the AI market, which is expected to grow rapidly in the coming years, reaching revenues of $126 billion by 2025 – an-almost a 2.5x increase from $51.27 billion in 2022.
Pros and Cons of In-house AI Development
Some companies develop their own AI applications in-house. These companies usually have teams with specialized AI skills and abilities, as well as the funds needed to buy pricey development tools and frameworks and to set up complex AI infrastructure with expensive hardware devices and software licenses. These resources and tools enable organizations to build custom algorithms, proprietary APIs, and ML models from scratch and train them per their business requirements.
Such in-house activities provide complete control over the AI development lifecycle and their training data. Further, in-house teams with the right skills and appropriate budgets can develop bespoke solutions that are flexible and customized for the organization’s specialized datasets and model requirements.
That said, in-house AI development also has several drawbacks:
• Higher cost: Developing bespoke AI solutions can be costly from technical and human resource perspectives.
• Longer lead times: Build times can be very long, with many AI solutions taking months and even years to build in-house, from scratch.
• Lack of talent or expertise: Internal teams may lack the expertise to develop solutions. Building an AI solution and deploying and scaling it also presents two different skill sets so it is even harder to hire the full spectrum of skill that are needed.
The cloud-based, available-on-demand AIaaS model effectively limits all these challenges.
What is the AI as-a-Service Business Model and Architecture?
Within the cloud, the AIaaS architecture consists of multiple elements. These are:
AI Compute
Compute refers to the hardware resources that are essential to delivering AIaaS. Compute services include serverless computing, which allows organizations to build and run AI applications without worrying about infrastructure-related tasks. These also include virtual machines (VMs), Containers, and batch processing that play a role in improving parallel processing and task automation.
AI Data
AI algorithms and ML models study large amounts of data to identify functional patterns and make data-driven predictions. The data that goes into these algorithms and models may come from a wide range of sources, such as relational databases or data lakes, and may be both structured and unstructured (e.g., textual, or image-based).
AI Developer Services
Most AI service providers provide wizards that make it easier to train ML models, integrated development environments (IDEs) that simplify model testing and management, and data prep tools for automated data extraction, transformation, and loading (ETL).
They also provide frameworks and templates that minimize the complexity of setting up the ML modeling and data analytics environment. Frameworks provide a high-level programming interface that enables organizations to architect, train, validate, and deploy AI models with minimal coding effort or expertise.
Popular AI and ML frameworks include Intel® Optimization for TensorFlow, Apache MXNet, Baidu’s PaddlePaddle, Microsoft CNTK, Torch, and XGBoost Optimized by Intel. Some of these frameworks are open-source and support popular programming languages like Python and C++, further improving the accessibility of AIaaS for smaller companies. Examples of such frameworks include Apache Mahout, TensorFlow, and Torch.
AI Software Services
The range of AI services provided by cloud providers is constantly increasing. Most of these services are pre-trained and often available through APIs, so organizations without advanced skills or experience also can use them.
For example, Amazon Web Services (AWS) and Microsoft Azure offer AI services for business automation, data analytics, chatbots, virtual agents, NLP, and much more. IBM Watson also includes a suite of AI tools and applications for everything from business automation and advertising to risk and compliance, IT operations, and more.
GCP’s AIaaS range is not as broad as AWS’. Nonetheless, developers and data scientists can still select from tools and services to:
• Build and deploy AI models
• Train custom ML models
• Derive valuable insights from unstructured data
• Add translations to content and applications
• Convert text into speech and speech into text
• Create conversational customer experiences across platforms and devices
Conclusion
According to one recent AI trends report, the AIaaS model is emerging as a critical growth-driving strategy for many AI vendors in the market. It is because more and more organizations are looking to achieve digital transformation and drive customer value with AI. But implementing AI in-house can be expensive and complex. To reduce costs and still harness the power of AI, numerous companies are turning to AI as-a-Service companies.
AIaaS will drive the growth of AI-enabled point solutions and use-case-based solutions in the coming years. It will also enable companies to adopt multimodal AI to unlock data potential and solve more real-world problems. Many companies will opt for AI that can effectively operate in edge environments. In these environments, computing and storage resources are distributed beyond the cloud and nearer to the locations where organizations conduct business and produce the data that supports the business. Others will demand microservices that can be independently deployed and tailored to their specific business needs. AIaaS will play a role in all these trends in the future.
AIaaS has made significant inroads into the fast-growing world of Artificial Intelligence and Machine Learning. It will continue to do so and eventually become as important as other “as-a-Service” offerings. When this happens, more organizations will be able to harness the power of AI. And that is when we can say that AI is truly democratized.
