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. 

How AI as a Service Works

AI as a Service (AIaaS) operates by delivering artificial intelligence tools and functionalities through cloud platforms. Rather than developing and hosting AI solutions internally, companies can access these capabilities via the internet. This includes tools for machine learning, natural language processing, and computer vision—all provisioned on-demand by third-party providers. The result is a scalable, flexible way to experiment with and deploy AI across various departments without the burden of maintaining the infrastructure.

Core Features of AIaaS Platforms

AIaaS platforms offer a range of powerful features to help businesses harness the potential of artificial intelligence without the need for extensive in-house expertise. Key features include:

Machine Learning

Access pre-trained models or build customizable ones tailored to your needs. These models can handle tasks such as predictions, trend forecasting, anomaly detection, and complex pattern recognition. They enable businesses to derive insights from data with precision and scalability.

Natural Language Processing (NLP)

Leverage advanced tools for sentiment analysis, language translation, text summarization, and conversational AI. These features are ideal for improving customer interactions, creating chatbots, enhancing multilingual communication, and gaining insights into consumer sentiment.

Computer Vision

Tools for image and video analysis, object detection, facial recognition, and optical character recognition (OCR). These features are widely used in industries like retail, healthcare, and security for tasks ranging from inventory management to identity verification.

Data Analytics and Visualization

AI-driven analytics provide actionable insights by processing vast amounts of data quickly. Visualization tools help present these insights in an easy-to-understand format, enabling data-driven decisions.

Integration and Automation

Seamless integration with existing systems and automation of repetitive tasks help streamline workflows, reduce manual effort, and accelerate operations.

AIaaS platforms empower businesses to adopt AI solutions with ease, providing scalable and cost-effective tools to meet their unique goals.

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.

Common Applications of AIaaS

AIaaS is transforming industries by leveraging artificial intelligence to improve efficiency, drive innovation, and deliver value. Here are some of its most practical applications, along with how they create real-world impact:

Predictive Analytics

AIaaS is widely used for demand forecasting and sales optimization. By analyzing historical data and market trends, businesses can better predict customer behavior, optimize inventory levels, plan promotions, and make data-driven decisions to boost revenue.

Customer Service Automation

Chatbots and virtual assistants powered by AIaaS streamline customer interactions. These tools can handle routine queries, provide instant support, and even personalize recommendations, improving customer satisfaction while reducing operational costs.

Fraud Detection

In finance, ecommerce, and other industries, AIaaS utilizes anomaly detection models to identify fraudulent transactions or suspicious activities in real time. This helps safeguard businesses and customers against financial losses and security breaches.

Healthcare Diagnostics

AIaaS is revolutionizing healthcare by enabling diagnostics through advanced machine learning models. These systems analyze medical imaging, such as X-rays or MRIs, and health data to assist doctors in detecting conditions like cancer, heart disease, or neurological disorders earlier and more accurately.

Personalized Marketing

AIaaS helps marketers craft personalized campaigns by analyzing user behavior, preferences, and demographics. This ensures that customers receive tailored offers and experiences, leading to higher engagement and conversion rates.

Supply Chain Optimization

AIaaS solutions enhance supply chain management by analyzing logistics, identifying inefficiencies, and recommending ways to streamline operations. This is particularly valuable for industries like manufacturing and retail.

By offering these scalable, ready-to-use AI capabilities, AIaaS empowers businesses of all sizes to harness the power of artificial intelligence without the need for extensive in-house expertise or resources.

Benefits of AI as a Service

AI as a Service (AIaaS) makes adopting artificial intelligence easier and more affordable for businesses of all sizes. Instead of building costly in-house systems, companies can “rent” powerful AI tools—like machine learning models or natural language processing—on demand through cloud-based platforms.

By automating repetitive, low-level tasks, AIaaS increases team productivity, reduces human error, and frees employees to focus on higher-impact work. These tools also help enhance strategies across departments—from customer service and marketing to product development and data analysis.

Key benefits of AIaaS include:

Improved efficiency and automation

AIaaS streamlines business operations by handling routine processes, helping teams work faster and with fewer errors.

Better decision-making

AI-powered analytics uncover patterns and insights that support more informed, data-driven strategies.

Decreased need for technical expertise

With no-code and low-code AI tools, even non-technical users can deploy and manage AI models without extensive training or full dev teams.

Cost savings

Organizations gain access to enterprise-grade AI infrastructure without the cost of building or maintaining it in-house. Transparent pricing and pay-as-you-go options make it easy to budget and scale.

Faster innovation

Ready-to-use ML models and APIs allow businesses to experiment with AI features and integrate them quickly into existing systems.

Access to advanced infrastructure

AIaaS providers offer the compute power and cloud infrastructure needed to run AI models—removing the need for local servers or expensive hardware.

Scalability and flexibility

AIaaS adapts to growing needs with ease. As your business and data requirements evolve, cloud-based AI platforms can scale without disruption.

Reduced risk

Outsourcing complex AI infrastructure to experts helps reduce security, compliance, and development risks.

AIaaS provides a low-barrier, high-impact way for companies to explore and implement artificial intelligence—turning innovative technology into a practical advantage.

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.