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Artificial intelligence as a service, often referred to as AIaaS, is a service 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.
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.
Also see: Top Generative AI Apps and Tools
Table of Contents: A Closer Look at Artificial Intelligence as a Service
- AIaaS Definition
- Types and Examples of AI as a Service
- Benefits of Using AI as a Service
- Disadvantages of Using AI as a Service
- Top AI as a Service Providers
- Bottom Line: AI as a Service for Your Organization
Also see: Generative AI Companies: Top 12 Leaders
AIaaS Definition
When a company is interested in working 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, or AIaaS, is an outsourced service model AI that cloud-based companies provide to other businesses, giving them access to different AI models, 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 expertise, data access, compute power, finances, and other resources necessary to build an AI chatbot.
Instead, organizations can invest in OpenAI subscriptions for GPT-4 Chat, Fine-tuning models, and/or Embedding models. These subscriptions offer 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.
Also see: 100+ Top AI Companies 2023
AIaaS vs. SaaS
Artificial intelligence as a service (AIaaS) and software as a service (SaaS) share many overlapping qualities.
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.
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.
Also see: Best Artificial Intelligence Software 2023
Types and Examples of AI as a Service
Especially as today’s generative models grow more mature and expand into different industries’ specialized niches, AI as a service use cases continue to evolve. In general, these are the most common services, solutions, and categorical types of AI that AIaaS providers offer today:
- Data sourcing, labeling, classification, and management.
- Automated bots.
- Chatbots, conversational AI, and natural language processing.
- Machine learning models and frameworks.
- Machine and computer vision.
- Cognitive computing.
- Robotic process automation.
- Smart security management.
- APIs.
- Smart analytics.
- Low-code/no-code AI operations.
Also see: Generative AI Startups
Benefits of Using AI as a Service
Lesser 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. Investing in another company’s AI solutions may sound expensive, but it’s actually much more affordable and requires few native resources to get started.
In most cases, users simply pay a subscription fee, pay only for what they use, and/or can opt out or scale up whenever their tooling requirements change.
Transparent pricing
Most AIaaS vendors price their solutions with subscription-based or unit-based pricing. As long as users keep track of their usage and payment schedules, the cost of AIaaS should be transparent from start to finish.
Limited AI skill requirements
Depending on the AI tools and AIaaS provider you select, your team could have virtually no knowledge of how artificial intelligence tools work or need to be set up.
The majority of these providers handle setup and ongoing maintenance for your team, and they can even support any customizations or specific use cases that you want to figure out. This quality of AIaaS alone is quickly democratizing access to artificial intelligence.
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.
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?
Whatever the case may be, AIaaS is typically sold through a flexible subscription 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.
Access to advanced tools and infrastructure
Today’s AIaaS vendors have built up infrastructure to manage everything from protein and drug design to marketing content writing.
The best part? Their tools 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 their labor, using advanced AI to solve for a variety of enterprise AI use cases.
Continuous improvement
Because so many instances of commercial AI are new and expanding their potential capabilities, nearly all AIaaS vendors are committed to continuous improvement of their tech stack. Their customers benefit from this commitment, receiving relevant updates to existing tools, access to new tools and use cases in beta, and much more as subscribing users.
On a related topic: The Future of Artificial Intelligence
Disadvantages of Using AI as a Service
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.
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 capabilities 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.
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.
Learn more: 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 to a specialized business use case.
Vendor lock-in
Once you get started with one AIaaS vendor, you can certainly off-board 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.
Many of these providers offer limited interoperability and integration opportunities, making it a challenge to truly integrate your AI tech stack and avoid vendor lock-in.
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. 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.
Also see: Generative AI Examples
Top AI as a Service Providers
Many smaller companies and AI startups also offer AIaaS to customers, but at this time, these are the top AI as a service providers in the market:
- AWS.
- Google (Google Cloud).
- Microsoft (Microsoft Azure).
- IBM.
- SAS.
- ServiceNow.
- Salesforce.
- Oracle.
- SAP.
Bottom Line: AI as a Service
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 true for a variety of reasons, including the massive financial and resource investment that’s usually required to build and continuously use these solutions.
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 initial investment.
But that still leaves the question: Is investing in AI as a service worthwhile for your organization?
It certainly offers competitive advantages to its users, but there are still some security, compliance, and general transparency concerns worth considering. If you choose to bring AIaaS into your daily workflow, be sure to consider AI ethics, best practices, and possible training and policies to keep everyone in your organization on the same page about what these tools can do and how they should be used.