AI Platform as a Service vs AI as a Service: Which and when to choose?

AI Platform as a Service vs AI as a Service: Which and when to choose?

Sorcha Sheridan

Social Bee

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AI isn’t just the future, it’s happening right now.

Businesses can implement AI in different ways, two of these being AI as a Service, or an AI Platform as a Service. The difference in name is small, but it is important to differentiate between the two.

We recently published an article all about AIaaS in which we talk about what it involves and the benefits it has. In this article, we will build on the topic as well as discuss the ins and outs of AIPaaS and how it compares.

A quick recap of AI as a Service

In short, AI as a Service is a term used to describe a third party that provides advanced AI functionalities to companies for a one-time payment or subscription fee.

Although both AIaaS and AIPaaS solutions can be implemented with less human intervention, AI as a service is more for automating day-to-day, repetitive tasks like data management. We’ll get into more differences between the two terms later in the article.

What is AI Platform as a Service?

The strategic shift to cloud-first technology puts pressure on businesses to look for cloud platforms to automate their processes. In this hustle, around 30% of a business’ cloud budget typically goes to waste.

Why? Because businesses either don’t know how to use their financial resources best on cloud solutions, or their workforce isn’t informed enough to make the most of these solutions.

Worst case? It’s both.

To reduce cloud waste, minimize costs, and optimize processes, businesses are now turning to Artificial Intelligence Platforms as a Service (AIPaaS) to solve their cloud problems.

AIPaaS consolidates AI-powered business applications and services such as image, text, and speech recognition, in one interface. Easy-to-use, intuitive, and customizable AIPaaS solutions give you the power to automate in a way that best suits your company’s size, structure, and needs.

What’s more, you can leverage advanced artificial intelligence technology to better manage and autoscale your processes over time.

So what actually is it? AI Platform as a Service is an integrated technology including end-to-end and pre-packaged Machine Learning (ML) and Deep Learning (DL) solutions. It offers AI-driven services on a pay-per-use or pay-per-service basis.

AIPaaS comes in handy for developers who are building, training, and deploying AI-focused applications. It combines Artificial Intelligence (AI) and Platform as a Service (PaaS), powering cloud platforms to provide more user-friendly, pre-built, comprehensive, and automated AI solutions.

With AIPaaS, you get robust AI services without setting up your own independent physical cloud infrastructure. Additionally, there are other benefits such as:

  • Using a unified artificial intelligence platform
  • Saving infrastructure costs
  • Building your own AI capabilities with intelligent Machine Learning and AI models
  • Paying only for the services you use
  • Scaling your business on the go

AIPaaS fundamentals

Data management can be tedious for most businesses, especially those that process data in large volumes. You need expertise to glean, clean, and use data to uncover insights critical to your day-to-day functioning.

Both AIaaS and AIPaaS simplify data management practices with more affordable and hosted data management using pre-trained ML models and AI APIs. Let's analyze these two fundamental components.

Pre-trained Machine Learning models

Machine Learning models take years to build and may take many more to master. They’re time and resource-intensive and require a lot of technical know-how. What if you can't build an ML model from scratch, but still need one? You use pre-trained models.

Often, these solutions have pre-trained Machine Learning models and integrations to ensure ease of use and seamless functionality. They offer at least some functionality that is no-code or low-code and give you access to complex Machine Learning algorithms, however for AIPaaS solutions, a developer is often still required.

Some features these models typically offer include:

AI APIs

Application programming interfaces (APIs) are at the core of AIPaaS, with a significant amount of usage of AIPaaS coming via APIs. Cloud platforms implement APIs as an easy way to interact with AI models. These come in all sizes and forms, and some platforms like Microsoft Azure, Vertex AI, and Amazon Web Services (AWS) provide high-quality APIs for the best possible AI solutions.

Some common functionalities of AI APIs are:

The difference between AIaaS and AIPaaS

When talking about AI, it’s not uncommon to confuse the various AI technologies. Artificial Intelligence as a Service (AIaaS) and Artificial Intelligence Platform as a Service often hit this confusion radar, and you may see cases when they are used interchangeably.  However, there are ways in which AIaaS and AIPaaS are different.

As we mentioned earlier in the blog, we’ve previously published a guide to AIaaS, such as Levity. AIaaS solutions are much easier to integrate and use, and they’re ideal for small or midsized businesses with limited customization needs. They come with pre-built ML, DL, and AI algorithms that simplify workflows for end-users.

AIPaaS, on the other hand, is more complex than AIaaS and works for businesses of all sizes looking for more customization options. It goes a level beyond day-to-day data management needs to help develop, run, and manage applications.

Simply put, AIPaaS provides a space for developers to build on and usually requires a significant amount of technical knowledge for setup. Whereas AIaaS is less complex and is a term best suited to describe smaller players, who have narrowed down their offering and focus on providing a service for out-of-the-box AI technology benefits.

Here's a more comprehensive breakdown of the critical differences.

AIaaS versus AIPaaS

Benefits of AI Platform as a Service

Cloud platforms with AI capabilities offer a range of benefits to businesses throughout the development lifecycle. You can hardly go wrong with an integrated solution that makes creating and managing intelligent products accessible and affordable.

Here's a quick look at the main benefits of AIPaaS.

Doesn't require advanced IT infrastructure or technical know-how

AIPaaS gives you the flexibility to manage all your workflows in the cloud. This is much cheaper and less resource-intensive than building an advanced IT infrastructure. Although they do require more technical knowledge than AIaaS, they are still more accessible than building this infrastructure from scratch.

Pre-built algorithms, ready to use

Another major benefit of using AIPaaS solutions is access to a secure and pre-built infrastructure and environment. As discussed above, all your work takes place in the cloud, and you don't have to set up any advanced infrastructure.

This saves time and money and is comparatively safer. Developers can leverage pre-built AI, ML, and DL algorithms to develop, launch, and maintain apps and use the saved time to improve or create new products.

In-depth analytics and insights

The first step to making smart business decisions is to analyze your data and processes and use the derived insights for informed decision-making. With AIPaaS, you can do just that.

AI analytics in business intelligence uses Machine Learning algorithms and techniques to extract key insights, identify patterns, and establish relationships between different data sets.

This is extremely valuable for business analysts looking to perform deeper and more advanced analyses.

Augmented analytics is another notable AI capability making rapid strides into the future of automating analytics without the active involvement of data scientists.

Automation saves analysts’ time and brings them up to speed, especially when dealing with huge volumes of data.

Highly scalable

Most organizations use AIPaaS solutions to familiarize themselves with how they work and how they can benefit from them. No matter what level you start at, AIPaaS is a highly-scalable technology. You can easily grow your business without thinking about infrastructure resources or a large technical workforce.

Challenges of using AIPaaS

Despite the many benefits, AIPaaS is not without its challenges. Business users can face a whole host of complexities related to customization, security, and data quality. Let's dive a little deeper into these roadblocks.

1. Security compliance concerns

60% of all corporate data is stored in the cloud, but is it secure? Data governance, privacy, and security are major concerns related to AIPaaS that you shouldn't overlook.

A ready-made environment can sometimes be a lure for malicious intent. A simple data breach can expose you to vulnerabilities and severely damage your business. Additionally, with AIPaaS, you have no choice but to rely on your vendor's security framework.

Before choosing your solution, make sure its provider has—and follows—the necessary safety regulations and standards. For example, GDPR compliance and SOC 2 Type I certification, meaning your data is in safe hands.

2. Finding high-quality data sources

Most AIPaaS solutions rely on data quality to produce accurate results. Now, your data can be in any form and size. Chances are it’s highly unstructured. Unstructured data is growing at a staggering rate of 55-65% every year, and it’s difficult to process and manage.

Finding high-quality data sources and ensuring that the data you feed into AIPaaS systems is clean and actionable is a considerable challenge. But, smart solutions bridge even this gap.

Top AI Platform as a Service providers

In cloud computing, providers such as Amazon, Google, IBM, and Microsoft are topping the AI charts. They offer ready-to-use, high-quality AI solutions for developers and data scientists to create best-in-class ML models.

To read more about top AI as service providers, take a look at our previous AIaaS guide.

Amazon

Amazon Web Services (AWS) provides a highly scalable PaaS framework that eliminates the need for organizations to manage the underlying infrastructure so you can focus on deploying and managing your applications.

AWS also gives developers access to out-of-the-box tools and services, so they don't have to worry about resource sourcing, software maintenance, capacity planning, patching, or any other complexities with running applications.

Amazon SageMaker is one such AWS AI service focused on Machine Learning and Deep Learning.

Machine Learning Workflow

Here are some of its key characteristics:

  • It’s suitable for AI developers and data scientists looking for a comprehensive and off-the-shelf solution for all their development and data science needs. It provides a Jupyter Notebook-type interface to help operate on a unified platform.
  • Developers can create their own AI solutions using built-in community templates and algorithms and don't have to switch from one system to another during development.
  • Businesses can easily scale their processes with SageMaker as it auto-trains AI models faster.

IBM

IBM Watson is a popular AIPaaS platform that provides hands-on tools and services to drive seamless AI adoption. The company as a whole focuses on cost-effective and accessible solutions to optimize business outcomes and promote the responsible use of AI.

IBM Watson Studio finds its place among the most widely used AIPaaS platforms.

Train AI models with IBM Watson Studio

Let's look at some of its main features:

  • Helps developers, business analysts, and data scientists with practical and AI-supported decision-making.
  • Opens access to different AI models in varied cloud setups and environments.
  • Offers unmatched AI services such as a visual user interface for modeling, a workflow designer for automated learning, and automates other mundane data management tasks like cleaning and filtering.

Google

Google isn’t just a household name for the general public but also for AI developers and scientists. Google Cloud Platform (GCP) offers a range of cloud computing services hosted on the same infrastructure as Google. Infrastructural quality makes GCP faster, more scalable, and better than many similar platforms.

Vertex AI is the epitome of what GCP has to offer. It's an integrated ML platform for building, running, and scaling constructive AI models.

Learning path on Vertex AI

Here are some AIPaaS services Vertex AI offers:

  • Vertex AI Feature Store: Access a central repository of pre-built ML functions for faster and easier development
  • Vertex AI Experiments and TensorBoard: Create, track, analyze, and visualize ML experiments for better decision making
  • Vertex AI Training: Train your AI models and automatically generate text for your training data
  • Vertex Explainable AI: Test features by making predictions and comparing them to detailed model evaluation metrics and feature maps

Google Cloud isn’t limited to Vertex AI. It offers many other AI and ML products like AutoML, Dialogflow, Deep Learning VM Image, and Cloud Natural Language.

Microsoft

Microsoft never shies from creating innovative solutions. Its AIPaaS platform, Azure AI, is a perfect example of this innovative spirit.

Like other AIPaaS solutions, Azure AI is a unified platform for deploying, running and managing AI products and services. However, it’s more powerful and customizable for developers and data scientists to build their own AI solutions.

Azure AI gives users access to high-quality vision, language, speech, and decision-making AI models via simple API calls. It’s an all-in platform for building custom Machine Learning models using popular third-party tools like Jupyter Notebooks, Visual Studio Code, PyTorch, and more.

Azure AI ML lifecycle

Some key characteristics of Azure AI:

  • Friendly to users of all knowledge levels, from beginners to advanced data engineers.
  • Offers mission-critical AI solutions powering Xbox, HoloLens, and Microsoft Teams.
  • Facilitates responsible and secure use of AI with helpful guidelines, tools, and services.

Is AIPaaS better than AIaaS?

AIPaaS and AIaaS are not in direct competition with each other. Both are used in different circumstances and offer substantial differences in terms of functionality.

AIPaaS is a much more technical solution, so it makes more sense to use this if your business is building a full infrastructure. This solution and its providers do in a lot of cases offer at least some no-code functionality, but it is misleading to place AIPaaS as a solely no or low-code solution. Developer resources of some capacity will inevitably be needed to bring this solution to its utmost capabilities.

AIaaS on the other hand can be completely no-code, like Levity. AIaaS solutions tend to be more suited to small-medium-sized businesses without a team of developers but who still want to take advantage of the wonders of AI. You can think of AIaaS as a ready-made solution for you and your teams.

How do I choose an AIPaaS or AIaaS Platform?

The first step is to assess if your business needs are better suited to AIaaS or AIPaaS, using the information provided in this article will help you with that. In both cases, the top players in their respective fields are popular for a reason. Figuring out the key features and benefits your business is looking to obtain, as well as the resources and know-how your organization can offer in this process is paramount to selecting the best option.

Keep in mind that AIaaS is a functional solution to a business problem whereas AIPaaS is a core infrastructural business decision.

To learn more about how AIaaS like Levity can help your business, sign up now.

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