Guide to AI as a Service (AIaaS): Benefits, Types, Companies

AI is developing fast and changing the business landscape. McKinsey explains that AI drives automation and estimates that tasks accounting for 30% of working hours could be automated by 2030. Many organizations want to embrace AI-powered tools but feel they don’t have the resources to develop or maintain their own systems. 

AI as a service (AIaaS) offers businesses a more affordable way to stay competitive in this environment by making data and AI tools more accessible. Third-party AIaaS vendors invest in building and maintaining the AI infrastructure, essentially letting businesses “rent” AI tools and services based on their needs. This business model is much more cost-effective for companies and offers numerous other benefits, including creating more efficient workflows and customized AI services. 

What Is AI as a Service (AIaaS)?

Artificial intelligence as a service, or AIaaS, is a cloud-based solution for individuals and companies that want to explore or adopt advanced AI into their workflows. The AI as a service business model offers a variety of AI tools and ready-to-go solutions as part of a subscription or pay-as-you-go plan, letting companies try out different AI services without having to invest a lot of resources up-front. And with a variety of platforms and tools to choose from, your business can experiment with different solutions to find the right fit.

With AIaaS, businesses of all sizes can access natural language processing (NLP), machine learning (ML) algorithms, predictive analytics, and more to automate tasks, analyze data, or improve business strategies and customer experience. You can use and benefit from these AI tools, even without a large team of developers or a huge budget, making it a lower-risk way to integrate AI into your business. Plus, as a cloud computing service, AIaaS is flexible and can easily scale as your needs grow without needing to update your hardware or infrastructure. 

Types of AI as a Service (AIaaS)

AI as a service companies offer different tools, so it’s important to understand your business needs before choosing an AIaaS platform. Here are the most common AI services available:

Bots

You’ve probably seen or interacted with chatbots, the most common bot type, while surfing the web. These conversational tools help businesses connect with customers, provide support, or answer frequently asked questions.

When to use it: You want to provide 24/7 customer service, automate routine customer support tasks, or improve customer satisfaction levels.

How it should work: Bots use NLP algorithms to understand and communicate with people in human language.

What to expect from it: Customers can more easily find answers on their schedule, and your human service representatives can free up their time to handle more complex tasks.

Application Programming Interface (API)  

Think of an application programming interface (API) as a middle-man between two different services, allowing separate software applications to communicate and interact with each other. 

When to use it: If you need to connect multiple apps or AI solutions, want to translate text, use conversational AI, use computer vision models, or use NLP for sentiment or urgency analysis.

How it should work: The API can pull text, images, or other data from multiple sources together for better analysis and understanding.

What to expect from it: Tools that work together instead of separately.

Machine Learning (ML)

Typically, developers build and train machine learning models to analyze data and predict outcomes. AIaaS often offers pre-built ML models so businesses can use and manage models without needing any prior technical expertise. 

When to use it: If you want to find trends in your data, optimize your business, or forecast future outcomes.

How it should work: The model analyzes data to find patterns and make predictions without needing programming. It also learns more with every use to refine its methods and produce better results.

What to expect from it: Your business can run pre-trained models with little to no human intervention, but you will still need to manage it properly to avoid bias and bad predictions.

No-Code or Low-Code ML Services 

Some AIaaS platforms have no-code or low-code ML tools, where a visual interface lets users build models without writing computer code. 

When to use it: If you don’t have a big team of developers or are a non-technical user who wants to benefit from AI and ML tools.

How it should work: The entire process is automated from data collection to deployment, using pre-built algorithms to train ML models. 

What to expect from it: An easier way to adopt AI, as it requires very little hands-on effort.

Data Labeling 

Also known as data annotation, data labeling pre-processes data for ML models. It can organize, categorize, and assess the quality of raw data (text, images, and video) and provide context for your models. 

When to use it: For training your AI or ML models.

How it should work: It adds meaning and information to data so ML models can learn from it.

What to expect from it: Once the ML model understands meaning from one data set, it can then find the same meaning when it comes across other similar, relevant data.

Data Classification 

Data classification further categorizes data into different types. Structured and unstructured data is tagged based on its characteristics, including content, context, and user. 

When to use it: If you need to classify different types of documents, customer data, images, or other information.

How it should work: Once your business creates classification outlines and criteria, AI automatically categorizes data into separate classifications. 

What to expect from it: Data categorizing organizes information more effectively and can help refine business operations.

AI as a Service: Benefits

AI as a service makes incorporating AI tools into your business easier, regardless of your organization’s size or resources. With AIaaS, your company can increase productivity through AI automation, eliminating repetitive, low-level tasks to reduce errors and letting workers focus on more revenue-generating efforts. 

AIaaS solutions can transform your business strategies in every department, from increasing personalization in customer service, marketing, and sales to enhancing research and development, product design, data analysis, and more. 

Other benefits include:

• Decreased need for tech skills: With low- and no-code infrastructure options, you don’t have to employ a full team of developers or be a tech expert to use AI tools.
• Reduced costs: AIaaS offers significant savings over developing your own AI solutions. You get access to AI infrastructure without purchasing or managing it yourself, and the transparent pricing of AIaaS services lets you compare costs for different tools and usage amounts.
• Saves time: Having ready-to-deploy ML models and other AI services at your fingertips is much more efficient than developing AI solutions independently. AIaaS accelerates the adoption and implementation of these tools.
• Access to advanced infrastructure: AI and ML models need high-tech infrastructure and significant computing power to operate, and many businesses simply don’t have the resources needed to develop AI solutions. AIaaS companies provide this infrastructure at a much more affordable price.
• Offers scalability: AIaaS is built for scaling with features like data classification and automation. It offers the adaptability to grow and perform new tasks as your needs change, providing long-term value.

AI as a Service: Companies

With so many AI service options, how do you know you’re making the right choice? Businesses first need to establish what their biggest needs are and the type of solution they want. A company interested in chatbots to improve customer service has different needs than a business looking for ML models to predict inventory trends. After determining your requirements, you can compare AIaaS companies.

Selecting the right AIaaS service provider is key for successfully implementing AI tools into your business operations. We’ve rounded up a list of AIaaS companies, reviewing their services so you can find an AI solution that matches your needs.

Domo 

Domo’s comprehensive analytics and business intelligence platform is made even more powerful by its AI Service Layer. With this technology, your company can access AI tools for data preparation and analysis, automation, forecasting, and more, paired with Domo’s robust data governance and security. 

Domo’s AI capabilities include generative AI — using natural language processing (NLP) and large language models (LLMs) — machine learning, and predictive analytics, to name a few. Domo’s AI framework and no-code option make it easy for business users, data scientists, and developers to manage and deploy models and draw meaningful insights through visualizations. The flexible, customizable AI solutions ensure you get the best fit for your business.

Microsoft Azure AI

Azure AI is Microsoft’s public cloud platform with a full suite of AI services. Businesses can build, train, and deploy models using Azure’s Machine Learning and complete lifecycle management or create custom chatbots in Bot Services for customer service or personal assistant needs. 

Azure’s Cognitive Services offer more advanced AI capabilities, letting developers add computer vision or language understanding into apps through APIs. Microsoft Azure AI offers both pre-built and fully customizable models and managed API services so you can get started fast and use AI tools responsibly. 

AWS

Amazon Web Services (AWS) offers a wide range of AI solutions, including a cloud-based, fully managed ML service called Sagemaker for data teams to build, train, and deploy ML models. It also offers Rekognition, a computer vision service that can add image and video analysis capabilities to applications, along with text-based tools in Lex and Polly. Lex uses NLP to build chatbots and other conversational AI tools, while Polly uses text-to-speech technology to enable voice-enabled applications. AWS also provides specialized AI infrastructure that’s optimized for your needs.

Google AI

Google Cloud AI’s suite of tools includes many machine learning services, including AutoML. This tool lets developers train custom ML models with minimal coding and supports TensorFlow, the open-source ML framework found in many Google products. 

The platform also supports data classification, sentiment analysis, and computer vision using its Natural Language API and Cloud Vision AI tools. Businesses that want to build chatbots are in luck because Google’s Dialogflow offers advanced AI capabilities for building conversational interfaces.

MonkeyLearn

MonkeyLearn helps businesses clean, label, and visualize data in one place. This text analytics AI platform is simple to use, featuring intuitive, no-code tools and pre-trained models. It also offers easy ML model customization for sentiment analysis, data classification by topic, and entity extractors. With a simple point-and-click interface, users can quickly import data, define tags, and create dashboards to track model performance and gain insights.

SAP Business Technology Platform

The SAP Business Technology Platform (SAP BTP) includes low-code app builders and automation solutions for developers and business experts. SAP’s Integration Suite offers pre-built integrations, connectors, and APIs that help connect your tools for more efficient automation and data analytics. 

SAP’s Business AI copilot, Joule, assists organizations with core business processes such as human resources, procurement and supply chain, finance and ERP, sales and marketing, and IT. You’ll find AI solutions to help your business identify new growth opportunities within the market, boost sales, and enhance customer service.

OracleAI

Oracle offers numerous AI services through its data platform and cloud applications, including document analysis, forecasting/predictive analytics, anomaly detection, and digital assistants. Business users can access Oracle’s built-in database tools and algorithms for building and deploying ML models and creating more accurate and relevant generative AI responses. 

Oracle’s digital assistant lets businesses choose from pre-built conversational agents with the option of customizing it for text, voice, or chat-based interfaces. It also has speech-to-text capabilities to convert spoken language into text for greater accessibility. 

SAS

SAS Viya is the company’s data and AI platform, providing end-to-end solutions for every stage of the data lifecycle. Its centralized platform and intuitive interface make it accessible to more members of your organization, enhancing collaboration. 

With Viya, you can quickly integrate and prep data from numerous sources and develop models using automated engineering. SAS helps businesses deploy and manage analytics and AI-powered models, offering features like forecasting to predict outcomes and SAS Econometrics to simulate different business scenarios using data. 

IBM Watson

Businesses can choose from a comprehensive set of AI tools with IBM Watson to automate business processes, build virtual assistants, or predict outcomes. This AI platform is very accessible, with no prior coding or tech experience needed to build, train, and deploy ML models in IBM Watson Studio. 

It also offers companies several pre-built chatbot and conversational interface options, like Watson Assistant, for fast and easy bot implementation on websites, apps, and other channels. IBM Watson is applicable to businesses looking to perform more advanced text analytics with Watson Natural Language Understanding (NLU). This AI tool uses deep learning to extract sentiment, classification, categories, keywords, syntax, and other meaning from unstructured text.

H2O.ai

H2O.ai is designed for enterprise-level businesses and offers both on-premise and cloud-based AI solutions. Driverless AI, its automated ML platform, helps data scientists work more efficiently on projects by reducing complexity and increasing accuracy across the entire data lifecycle. The platform automates model deployment, validation, and documentation, along with visualizations to improve performance and interpretation. 

H2O also offers user-friendly interfaces so those without data science expertise can still access its AI capabilities. However, some beginner tech knowledge is still needed. 

Are you interested in seeing how big data and AI can improve your business? Discover how Domo.AI combines AI innovations with our existing BI platform for powerful analysis and meaningful business insights.