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Artificial intelligence as a service, often referred to as AIaaS, is a service that artificial intelligence companies provide to customers, offering them access to AI technologies and AI-powered business operations through the cloud without requiring them to invest in their own AI infrastructure.
AIaaS is a cost-effective pathway to AI and machine learning (ML) adoption for many users, as the initial investment for in-house infrastructure and ongoing implementation requirements can quickly get expensive and complicated. In contrast, working with an expert AIaaS provider can decrease the learning curve and setup timeline.
Although AIaaS is early in its life compared to many other as-a-service models, it has already proven itself highly scalable for a variety of artificial intelligence and machine learning use cases, including generative AI.
Read on to learn more about AIaaS, the types of AI that are available as a service, and how businesses can benefit from outsourcing AI operations to AIaaS providers.
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How Does AI-as-a-Service Work?
When a company is interested in starting with artificial intelligence but doesn’t have the in-house resources, budget, and/or expertise to build and manage its own AI technology, it’s time to invest in AIaaS.
Artificial intelligence as a service is an outsourced service model for AI that cloud-based companies provide to other businesses, giving them access to different AI models, machine learning algorithms, and other resources directly through a cloud computing platform; this access is usually managed through an API or SDK connection.
Although users may opt to self-host or self-manage their individual instances of these AIaaS tools and services, much of the work that goes into hosting, maintaining, securing, and upgrading artificial intelligence tools is handled by the AIaaS provider.
Let’s look at ChatGPT, a popular generative AI chatbot, as an example of how AIaaS works. Individual companies could, in theory, build their own large language models (LLMs) and then build their own chatbots off of that infrastructure. However, few companies have the in-house teams and skills, data access, compute power, finances, and other resources necessary to build a generative AI chatbot that can handle complex and diverse user queries.
Instead of scraping together an unreliable, small-scale generative AI tool with limited resources, interested organizations can work with OpenAI’s prebuilt and fine-tuning models or their APIs, which include GPT-4, GPT-4 Turbo, GPT-3.5 Turbo, Assistants API, and the gpt-3.5-turbo fine-tuning model.
These models are built on internal OpenAI infrastructure but are accessible to outside users through low baseline pricing that focuses on inputs, outputs, and training in small increments. For example, the gpt-3.5-turbo fine-tuning model costs $0.0080 per 1,000 training tokens, $0.0030 per 1,000 input tokens, and $0.0060 per 1,000 out tokens.
With this modest pricing approach, users are only responsible for the costs of content rather than the cost of the infrastructure, software, hardware, and other technical resources that go into the development and maintenance of generative AI technology.
Especially in this instance, AIaaS offers quick access, flexibility, scalability, and customizability opportunities for users who want a mature model but aren’t able to or interested in building one themselves.
For a full understanding of today’s generative AI tools, see our guide: Top 20 Generative AI Tools & Applications.
8 Examples of AI as a Service
Especially as today’s generative AI models grow more mature and expand into different industries’ specialized niches, AI as a service use cases continue to evolve. These examples may be available in managed AI platforms, fine-tuning or embedding models, or an application programming interface (API).
In general, these are the most common services, solutions, and categorical types of AI that AIaaS providers offer today:
- Chatbots and other bots: A vendor offers the foundation model or fine-tuning model necessary for users to create their own AI assistants, chatbots, or coding bots. For example, OpenAI customers might use gpt-3.5-turbo to fine-tune a chatbot that is tailored to their particular audience.
- Conversational AI and natural language processing: With the advanced AI solutions available through AIaaS, customers can complete complex NLP-based tasks, including sentiment analysis and text analytics, content summaries, and new content generation. OpenAI, Microsoft, Google, and AWS all offer solutions that can help users parse existing content or generate new content at scale.
- Machine learning models and deployment frameworks: Depending on the AIaaS partner and selected subscription, users may be able to leverage a prebuilt machine learning model that’s hosted in the provider’s cloud or create and deploy their own models through the provider’s ML platform. Microsoft Azure is an example of an AIaaS provider that offers both.
- Robotic process automation: This type of AIaaS provider combines AI technology with software or hardware-driven robotics technology, usually to automate business workflows and rule-based processes. UiPath is an example of a RPA provider that develops software-based robots that assist with data entry and other types of task automation.
- Smart analytics and data operations: With hosted and managed AI solutions that focus on data analysis, users can analyze more structured and unstructured big data quickly and accurately, even receiving smart recommendations for how to optimize this data in the future. DataRobot is a vendor that supports this use case, helping users build ML models that can handle advanced prescriptive and predictive analytics tasks.
- Machine and computer vision: Several AI vendors offer prebuilt or fine-tuning models for computer vision tasks like image and facial recognition. Using managed development environments like Google’s Vision AI, users can build computer vision applications and quickly assess and analyze imagery in both still and video content.
- Speech recognition and synthesis: Whether it’s text-to-speech or speech-to-text content generation, many users look to AIaaS to support voice applications and voice content generation. IBM Watson offers several solutions here, helping users transcribe speech into written text, generate realistic-sounding human voices, and improve speech recognition in multiple languages.
- Industry-specific AI models and use cases: Several AIaaS vendors extend their managed platforms or prebuilt applications to users who want to solve a very specific problem in their industry, whether that’s developing an AI contact center, setting up edge AI for manufacturing or IoT, or addressing risk and compliance requirements in a highly regulated industry. C3.ai is a solutions provider that offers prebuilt applications and tools to cover a range of enterprise use cases.
For a comprehensive list of today’s leading AI companies, see our guide: Top 150+ Artificial Intelligence (AI) Companies.
Benefits of Using AI as a Service
AI as a service lowers the barriers to entry for smaller and less-established organizations that want to invest in innovative AI technologies. These are a handful of the benefits that come from partnering with AIaaS providers:
Less Upfront Financial and Resource Investment
With AI as a service, organizations don’t need to research, build, or power their own AI technology and tools, other than for any additional customizations or use cases they’re looking to cover. Investing in another company’s AI solutions may sound expensive, but it’s actually much more affordable on most AI project scales and requires few native resources to get started.
In most cases, users simply pay a subscription fee, pay only for the tokens they use, and/or can opt out or scale up whenever their tooling requirements change. Because there is no infrastructure to maintain or finance, AIaaS customers can use their budgets to experiment with how they use third-party AI tools to meet their needs.
Transparent Pricing
Most AIaaS vendors price their solutions with subscription-based or unit-based pricing. This pricing may focus on inputs, outputs, training, time spent with the tools, or other units of measurement that are focused on usage. As long as users keep track of their usage and payment schedules, the cost of AIaaS should be transparent from start to finish.
If you’re unsure what your usage will look like — and, therefore, what your investment in AIaaS will need to be — communicate that uncertainty with prospective AIaaS vendors. They should be able to walk you through your particular use-case requirements and provide an estimate of costs for your project(s).
Limited AI Skill Requirements
Depending on the AI software and AIaaS provider you select, your team could have virtually no knowledge of how artificial intelligence tools work or need to be set up. There’s a wide range of AIaaS providers and levels of hands-on intervention.
Some AIaaS providers, such as Salesforce, simply require you to subscribe while they manage their infrastructure and your local AI implementation. Others, such as AWS, IBM Watson, Microsoft, and Google Cloud, give users the opportunity to be more involved in localized setup and usage.
Regardless, the majority of these providers handle basic setup and ongoing infrastructure maintenance for your team, and they can even support or help you strategize any customizations or specific use cases that you want to figure out. This quality of AIaaS alone is quickly democratizing access to artificial intelligence, even for highly specialized enterprise AI use cases.
Easier Deployment and Limited Maintenance Requirements
Even if your team has advanced AI knowledge and capabilities in-house, chances are you’re not interested in using their talents to constantly deploy and maintain the minutiae of AI models and solutions. With AIaaS, nearly all deployment and ongoing maintenance tasks are handled by the provider rather than your team, freeing up their time to experiment with the AI tools themselves.
Beyond simplifying the work that your internal teams do on these AI tools, outsourcing deployment and maintenance work is a surprisingly affordable way to use high-quality AI. This type of taskwork is built into many AIaaS subscription plans, so after you’ve determined the cost of that subscription, any leftover internal resources can be dedicated to continued AI experimentation and future planning.
Scalability
Have your team’s AI tooling requirements or budget grown significantly? Are you having a rough quarter and need to scale down on third-party investments? Do you have a new project idea, but you’re not sure if the business case is there over the long term?
Whatever the case may be for your digital transformation efforts, AIaaS is typically sold through a flexible subscription or usage-based model, meaning you can scale up or scale down as your requirements change. Simply pay for a different subscription tier, sign up for or use a different number of tokens, or contact your provider to find out what your best options are for your current workload needs. The nature of AIaaS is elastic; these providers expect and have experience with helping their customers adjust their usage whenever necessary.
Access to Advanced Tools and Infrastructure
Today’s AIaaS vendors have built up infrastructure to manage everything from protein and drug design to smarter manufacturing to marketing content writing that actually sounds like it was written by a human marketer. Developing these kinds of AI tools requires robust investments, research, and ongoing support, all things that smaller companies may not be able to resource themselves. However, it is often these smaller companies that can most benefit from specialized AI innovations that match their industries’ growth goals.
With AIaaS, any business that’s willing to buy into an AIaaS plan can access these new AI innovations as soon as they are available. Even for the most experimental new use cases, AIaaS solutions have gone through extensive research and testing, giving them advanced capabilities that continue to improve over time. Through an AI as a service model, your team can access the fruits of these AIaaS providers’ labor and a superior customer experience, using advanced AI to solve for a variety of enterprise AI use cases.
Continuous Improvement
If your company decides to invest in AI development independently, it’s possible you’ll run into an issue where your funding runs out or your developers need to be diverted elsewhere in the business. You may have been able to get your tool up and running initially, but when this happens, the lack of ongoing financial and human involvement in the AI means it will soon become outdated, prone to error, or otherwise nonoperational, effectively wasting the initial investment.
Because so many instances of commercial AI are new and expanding their potential capabilities, nearly all AIaaS vendors are committed to continuous improvement of and dedication to their tech stack. Their customers benefit from this customer service and commitment, receiving relevant updates to existing tools, access to new tools and use cases in beta, and much more as subscribing users.
To understand the larger landscape of generative AI vendors, see our guide: Top Generative AI Companies.
Disadvantages of Using AI as a Service
While AI as a service is advantageous to a wide variety of business types and for dozens of enterprise use cases, there are certainly some limitations and areas where customers should practice caution:
Little Transparency in Training and Implementation
Although many AI vendors are working on improving their transparency, especially in the wake of looming AI regulations, there’s still work to be done.
It isn’t clear how most AI models are currently trained, what data is used, and how that data has been collected. This could pose some ethical use issues, as well as security and compliance issues, if your organization isn’t careful.
AIaaS transparency will become especially important as industry-specific, regional, and global regulatory bodies finalize some of the AI governance regulations that are already in the works. With some of these pieces of legislation, even if an error or illegal act is committed due to a lack of information on the customer’s part, the AIaaS customer could still be held liable.
For a deep dive into the issues involved with generative AI, see our guide: Generative AI Ethics: Concerns and Possible Solutions.
Data Governance and Security Concerns
AI as a service solutions are offered through third-party cloud platforms, each of which has its own built-in security and governance capabilities. These may be enough to complement your current security posture management and compliance strategies, but in many cases, will not match your in-house security and compliance standards. This is also not to mention any industry- or region-specific standards to which your organization is held.
To protect your data while using AIaaS, it’s a good idea to use tools like cloud security posture management and third-party risk management software to secure these areas of your organization’s attack surface. It may also be worth researching and determining which platforms include built-in governance and security workflows and checklists that make management easier.
To learn more about the intersection of AI and enterprise security, see our guide: Generative AI and Cybersecurity.
Reliance on Third-Party AIaaS Vendors
AIaaS vendors offer users a lot of flexibility, but subscribers are still beholden to the schedules, release roadmaps, and support availability and responsiveness of these vendors. This reliance can become tedious, particularly if your team is struggling to scale or customize an AI tool for a specialized business use case.
When looking for AIaaS vendors to partner with, pay attention to the following indicators of how easy it will be to get the help you need: customer reviews, customer support channels and hours, dedicated customer support specialists, self-service resources, and user communities and forums.
Vendor Lock-In
Once you get started with one AIaaS vendor, you can certainly offboard and work with another, though the transfer process can be difficult. Not to mention, it’s incredibly difficult if you’re interested in using one type of tool from one AIaaS vendor and another type from another vendor; these tools don’t always play nicely with each other’s features and may require significant manipulation or manual entry to function properly.
Because so many of these AIaaS providers offer limited interoperability and integration opportunities, it can be a challenge to truly integrate your AI tech stack and avoid vendor lock-in. However, a growing number of these tools are offering direct integrations with common project management and collaboration tools, as well as detailed documentation and assistance for how to set up your own integrations.
Limited Customization Opportunities
While some AIaaS options, like fine-tuning models, offer you plenty of flexible customization opportunities, other tools make it difficult to customize and add features that meet your operational requirements. So while a tool you select may align with your initial expectations, if your team identifies another potential use case that isn’t currently operable, it may not be possible to customize the underlying code — and the vendor may not allow you to even try.
The best way to get ultimate levels of customization is to build and manage your own AI tools, but that can quickly become too expensive and difficult to handle in-house. If you want the balance of AIaaS with self-service customization, pay close attention to fine-tuning models, APIs, and open-source solutions that are highly configurable.
AIaaS vs. SaaS
Artificial intelligence as a service (AIaaS) and software as a service (SaaS) share many overlapping qualities, but they are entirely different terms that should not be used interchangeably.
In fact, AIaaS is often considered a specialized type of SaaS. SaaS is an umbrella term that covers any type of third-party software users can access for a subscription or other service fee via a cloud computing interface over the internet. AIaaS, then, is a third-party AI service that offers platform access and tools for AI development and usage.
Common examples of SaaS solutions include ERP software implementation and management, CRM implementation and management, web hosting, and more. In contrast, AIaaS is a narrower term that covers any kind of artificial intelligence service, technology, or capability that is outsourced to a service provider.
Top AI as a Service Providers
Many smaller companies and AI startups offer AIaaS to customers, but at this time, these are the top AI as a service providers in the market:
- AWS: As a cloud-computing giant, AWS’s AIaaS solutions support a wide variety of business cases for AI, especially for companies that want NLP, deep learning, or computer vision solutions. Customers often partner with Amazon Web Services for access to pre-trained models and managed platforms where they can train and deploy AI or ML models at scale.
- Google (Google Cloud): Another cloud computing giant that is also one of the earliest and most established AI innovators, Google Cloud gives AIaaS customers access to advanced data analytics solutions, including for sentiment analysis, platforms for custom AI model development, NLP solutions, and tools for computer vision and image recognition.
- Microsoft Azure: A cloud computing leader with an established enterprise customer base, Microsoft focuses on custom model development platforms and tools, solutions to develop enterprise AI assistants and copilots, and tools for NLP and text analytics. Its AIaaS is also a great option for machine learning and cognitive computing projects.
- IBM: Primarily through IBM Watson, IBM AIaaS users can build and fine-tune foundational models, prepare their data for AI projects, and manage risk through AI governance solutions. IBM also offers AI services to help with customer service, talent management, and digital transformation projects.
- OpenAI: The biggest name in generative AI right now, OpenAI offers access to its solutions through chatbots, APIs, and fine-tuning and embedding models. Its tools are primarily for text generation, text analysis, and image generation, making it a less comprehensive AIaaS option than many of these providers.
- ServiceNow: Mostly through Its Now Intelligence Platform, ServiceNow customers can access generative AI solutions, virtual agents, performance analytics, and AI-powered search solutions.
- Salesforce: Salesforce offers several business, marketing, and sales-focused AI services, including Sales AI, Customer Service AI, Marketing AI, Commerce AI, Low Code AI Builders, and Einstein Copilot.
- DataRobot: DataRobot gives users AIaaS services that focus on generative AI and predictive analytics. Users can benefit from its open ecosystem and custom-fit AI operations, governance, and deployment solutions to their unique needs.
- SAP: SAP offers multiple specialized AI services for specific industries and sectors, including finance, supply chain and procurement, marketing and sales, human resources, and IT and platform support.
- C3.ai: C3.ai’s AIaaS options focus on enterprise use cases, with an app development platform as well as several prebuilt AI applications. Users can select individual applications or invest in one of the following AI suites: Reliability, Supply Chain, Sustainability, AI CRM, Financial Services, Defense & Intelligence, or State & Local Government.
For a comprehensive report on today’s AIaaS leaders, read our guide: Top 11 AI as a Service Companies.
Things to Consider Before Using AIaaS
Before getting started with an AI as a service product or provider, it’s helpful to think about your specific wants and needs and how well that available AIaaS solutions meet those needs. Here are some important things to consider before making your decision:
Available Budget
What is your available budget now? Is there a good chance that your budget will increase or decrease in the coming months or years? Is your available cash flow steady or variable from month to month? Asking these questions now will help you determine if you can afford your preferred solutions now and in the future.
In-House AI Skills and Development Expertise
Do you have a large and highly skilled tech team that already has experience with AI and related development projects, or do you have limited in-house AI expert knowledge? Assessing your team’s skills and proactively identifying their AI roles and responsibilities can help you decide if you need an AIaaS that manages the full AI lifecycle or one that gives customers more customization and self-service opportunities.
Compliance and Security Requirements
Do you work in an industry or region that has strict data security or other compliance requirements? Do you frequently work with PII or other data that is especially vulnerable during a cyberattack? If so, you’ll want to find an AIaaS provider that offers built-in or easy-to-integrate security and compliance features, or at the very least, one that is compatible with your cybersecurity and compliance management frameworks.
AI Goals and Specific Outcomes
While the AIaaS vendors you partner with can offer advice on how to use their tools and what direction you might want to take them over the long haul, your organization needs to go into this investment with specific goals that you’re hoping to accomplish.
Are you looking to automate processes that are currently time-consuming or erroneous? Are you hoping to fill in the gaps in your workforce? Do you need a solution that can help you scale your global operations or a new product? Put together an internal committee to answer these kinds of questions and more so you go into AIaaS with a clear picture of what you want. This will also help you select a service provider that can effectively scale to meet your longterm goals.
Bottom Line: The Future of AIaaS
Global enterprises, small businesses, and individual consumers alike are currently interested in AI tools and the advantages they offer. Historically, however, artificial intelligence tools have not been accessible to all of these groups. This is true for a variety of reasons, including the massive financial and resource investment that’s usually required to build and continuously use AI models.
Practices like artificial intelligence as a service have bridged that resource gap, making it possible for all kinds of users to benefit from AI without much AI expertise or upfront capital investment. In the future, expect to see more growth in this particular area of AI, especially for specialized use cases in marketing, healthcare, customer service, retail, manufacturing, and other industries that benefit from AI automation, streamlining, and assistance. As AIaaS providers grow more established in their AI offerings, even the most hesitant of businesses are likely to adopt AI into their business workflows to keep up with their competition.
For a in-depth understanding of generative AI, which is a key component of AIaaS, see our guide: What is Generative AI: Ultimate Guide.