What is AI as a Service & 7 Best Platforms to Accelerate Innovation

Over the past few years, artificial intelligence has emerged as one of the most critical technologies that impact businesses in a generation. But for all of the excitement around AI, few companies have the resources to develop cutting-edge AI systems in-house. Instead, they are turning to AI as a Service solutions to reduce costs, streamline operations, innovate, and gain a competitive edge. AI as a Service provides businesses with access to advanced AI capabilities through APIs and other tools without the complexity of building the systems themselves.

In this blog, we'll explore AI as a Service, also known as AI in the Cloud, and discuss its benefits and applications. We'll also introduce Inference's AI inference APIs as a valuable tool to help you achieve your objectives, including leveraging AI to drive innovation and reduce costs without the complexity of in-house AI development.

What is AI as a Service (AIaaS)?

AI as a Service (AIaaS) is a cloud-based model that allows businesses to incorporate AI capabilities into their operations on demand. The service is offered by third-party vendors that maintain the infrastructure, so organizations don’t have to invest resources to build and implement AI tools independently. AIaaS provides a low-risk and cost-effective model for businesses looking to automate processes and improve business functions.

How Does AI as a Service Work?

AI as a service works like other cloud-based models, providing users with AI functionality over the Internet. Instead of downloading AI software or installing a program locally, businesses can access AI tools hosted on the cloud through a web browser.

This eliminates the need for in-house infrastructure and allows organizations to scale AI capabilities to fit their unique operational needs.

Examples of Common AI as a Service Tools

Depending on their operational needs, businesses can leverage various types of AI services. Like software as a service (SaaS) business models, companies can subscribe to AIaaS plans that provide them with AI in the workplace. If you're here, chances are you're probably looking for a specific tool already, so let’s shed some light on the most common types on the market.

Bots

Regardless of whether you search the web for anything from government websites to clothing stores, you likely come across bots – particularly the most common type, i.e., chatbots. They use Natural Language Processing (NLP) algorithms to emulate natural conversations between humans. These bots are mainly used for customer service and provide relevant answers to customers’ most recurring questions.

As they respond on a 24/7 basis, they save time and resources, allowing employees to focus their time on more complex tasks. One of Europe’s fastest-growing parcel companies, InPost, has recently reported that they automate as many as 92% of the millions of customer conversations they handle each year through leveraging a chatbot.

APIs

An API (Application Programming Interface) is the software “middle-man” that allows two applications to communicate. An example of this would be a third-party airline booking website such as:

• Expedia
• CheapOair
• Kayak

Which pulls information from a collection of airline databases to present all their deals in one place, in a readable manner. Common uses for APIs also include:

• Natural Language Processing (e.g., sentiment or urgency analysis)
• Computer Vision
• Conversational AI

Machine Learning

Companies use Machine Learning (ML) to analyze and find patterns within their data. As a result, it makes predictions it wasn’t explicitly taught to make, learning as the process evolves. This method of data analysis is meant to be run with little to no human intervention.

In AIaaS, companies can manage Machine Learning without particular technical expertise. There are many solutions, from pre-trained models to building one to perform a custom task (just don't forget the rule of thumb!).

Data Labeling

Data labeling is annotating large quantities of data to be organized efficiently. It has a wide range of use cases – i.e., assuring data quality, categorizing it by size, and further training your AI, to name a few. In the case of the latter, human-in-the-loop (which we mentioned earlier in this post) is used to label data so that AI can quickly evaluate it in the future.

Data Classification

Data classification is when data is tagged under one or more categories. These classifications usually include:

• Content-based
• Context-based
• User-based

Artificial Intelligence can classify data on a larger scale, provided that a data classification outline and criteria are clearly defined.

Agent Copilot

Agent copilots are AI-powered second-in-commands that can support agents. When human agents engage with a customer, the agent copilot analyzes the conversation and suggests contextually relevant responses or actions based on the customer’s sentiment and replies. From there, the human agent can approve, modify, or execute these suggestions, resulting in a streamlined ticket resolution process.

As the technology learns from agent interactions, it can work autonomously to resolve specific types of high-volume, routine tickets like order cancellations. Agent Copilot blends AI efficiency and human expertise, improving the speed and quality of customer service interactions.

No-Code or Low-Code ML Services

Fully managed machine learning services provide the same features as machine learning frameworks without requiring developers to build their AI models.

These AIaaS solutions include pre-built models, custom templates, and no-code interfaces. This is ideal for companies that do not want to invest in development tools and do not have in-house data science expertise.

Artificial Intelligence of Things (IoT)

Internet of Things (IoT) is a network of devices connected to the Internet that share data. The devices contain sensors that exchange information in real time. Artificial Intelligence of Things (AIoT) embeds AI technology and machine learning capabilities into IoT, analyzing data to identify patterns, gather operational insights, and detect and fix problems.

AIoT devices can send relevant information to the cloud (with user permission) to assist with a product’s performance. AIaaS providers may offer forecasting services that enable IoT devices to predict when a machine and equipment may need maintenance, helping businesses avoid expensive interruptions.

Benefits of AIaaS

• Boost Team Productivity and Efficiency: AIaaS allows you to leverage AI-powered features, such as intelligent routing and triage, generative AI, and sentiment analysis. These tools help streamline workflows and improve your team’s skills, increasing productivity without additional headcount.
• Enhance the Customer Experience: With AIaaS, businesses can implement AI faster to deliver personalized, conversational support and level up their CX.
• Reduce Costs: AIaaS is a cost-effective way for businesses to use plug-and-play AI functionality to keep up with evolving business trends and customer expectations without heavy IT spending.
• Scale Faster: AIaaS allows businesses of all sizes, even small companies and startups, to deploy AI. The right AIaaS provider grows with your company, allowing you to adapt the AI capabilities to your needs and increase scalability.

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5 Benefits of Using AIaas Platforms

1. No Need for Sophisticated Tech Skills

AI as a service is accessible even to organizations that do not have an AI-skilled programmer on board. Companies that genuinely provide AIaaS often do not require coding or tech skills at any point in the setup process. In a piece for Forbes, Daniel Newman of Broadsuite Media Group aptly notices that “in a time when there’s a shortage of AI experts and ever-increasing competition in the marketplace, that’s a huge workaround”.

It’s important to underline that while some AIaaS solutions do not require coding skills, the level of implementation complexity varies greatly when we enter the world of legacy software.

2. Advanced Infrastructure - And Fast

Before AI as a Service, strong and fast GPUs were required to run successful AI and Machine Learning models. Most SMEs don't have the resources and time to develop software in-house. In the world of AI, there are a few rules of thumb - one is that your model will only perform well in a task if the data it's been fed is of good quality.

AIaaS, being customizable, will offer the opportunity to build a specific task-oriented model on top of the abundance of data most organizations sit on top of already. “The worldwide AIaaS market will be valued at just a shade under $11 billion by the end of 2023, with the 2017-2023 CAGR hovering around the 49% mark.” - Hussain Fakhruddin, the CEO of Teksmobile

3. Transparency

Not only does AI as a Service give you access to AI while cutting back on non-value-added labor, but there is also a significant amount of transparency. AI as a Service allows you to pay per usage. Machine Learning requires much computing power, but most pricing models focus on usage.

Some platforms allow the user to have more control over AI automation. One way to do this is to add humans in the loop as an option. HITL is a continuous feedback loop where the process owners give AI feedback in edge cases. The feature aims to achieve what neither a human being nor a machine can achieve.

4. Usability

Most as-a-service platforms are not as user-friendly as they'd like to make it sound. Although many AI options are open-sourced and can be downloaded, modified, and used freely, they can be challenging to install and develop. AI as a Service, on the other hand, is, in most cases, entirely ready for use.

Process owners can utilize AI software without any formal training. End-to-end ML services include both pre-built models and custom-created models, as well as drag-and-drop interfaces for reduced complexity. The cool thing about this? Getting your ML project started within hours without engineers.

5. Scalability

AI as a Service is built for scaling. If you've trained your model to classify your info based on email urgency or sentiment and funnel the right emails to the right person, you're already ahead of the game. AI as a Service is perfect for performing tasks that require some level of cognitive judgment but where the task itself is not value-adding.

Inference: Cost-Effective Serverless AI Inference for Developers

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications. Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

Top 7 AIaaS vendors

1. Inference: A Cutting-Edge AIaaS Provider for Inference Operations

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

2. Levity: The Next Generation of AI-Assisted Document Processing

A solution like Levity is, in essence, what AIaaS should be about – to get started, you define your workflow, upload sample data, train the custom AI, and let it learn and refine itself. Initially, the tool will ask you for input when it’s uncertain about tackling an issue, but it’s designed to relieve you of mundane work over time.

While some basic AI knowledge will be helpful to, Levity doesn’t require you to be an expert. The tool aims “to deliver an intuitive and guided experience in all aspects – including what you need to know about AI”. Highlights Fully customizable document, image, and text classification Integrates with hundreds of tools

3. Odus: The AI Chatbot That Learns to Communicate Like a Human

Odus is a chatbot that provides automated messages and calls with a human-like AI. The chats are fully automated and can work entirely on their own. Odus has a user-friendly, no-code approach, making managing it simple even for those without tech skills.

Highlights:

• Provides call and written communication automation
• Fast integration with a variety of other tools
• Offers complex scenarios with entities

4. Viz.ai: AI for Healthcare

Viz.ai is a medical imaging company based in San Francisco, California, specializing in applying artificial intelligence in healthcare. The company uses advanced deep learning to quickly relay information about stroke patients to specialists who can treat them. They have also introduced a module that helps control the influx of patients during COVID-19, allowing medical professionals to rank the severity level for each patient. As a result, their solution enables a better flow of patients and provides a safer workplace for staff.

Highlights:

• Improving patient management
• Safer environment for medical staff and patients during COVID-19
• Reducing time to treatment and time to diagnosis

5. Microsoft Azure: The Extensive AI Services of Azure

Here are popular Azure AI services:

• Cognitive Services: Provides APIs for content moderation, anomaly detection, and more.
• Cognitive Search: Lets you add AI-powered cloud search into mobile and web applications.
• Azure Machine Learning (AML): Enables you to build, train, and deploy ML models from cloud to edge, supporting the service’s models and custom AI development.‍
• Bot Services: Offers a serverless chatbot service that can scale on demand.

6. AWS: Amazon's AI and ML Services

Amazon Web Services (AWS) offers various AI and ML services, including:

• Sagemaker: A fully managed service for machine learning in the cloud. It enables building and training machine learning models and deploying them to a production-ready, hosted environment.
• Lex: Provides features for building chatbots and virtual agents and integrating with new and existing applications. Lex includes natural language capabilities, such as speech recognition, natural language processing (NLP), and speech-to-text conversion.
• Polly: Includes features for creating speech-enabled applications and products. Polly helps convert text into spoken audio.‍
• Rekognition: Offers computer vision capabilities services, including algorithms pre-trained on datasets Amazon or its partners curated. You can also use algorithms that you have trained on a custom dataset.

7. Google Cloud: A Wide Array of Cloud AI Services

Google Cloud offers various cloud AI services, including:

• AI Platform: Provides capabilities to help you build, deploy, and manage ML models at scale.
• AI Hub: This hosted repository offers plug-and-play AI components, including out-of-the-box algorithms and end-to-end AI pipelines.
• Conversational AI services: These include various services, such as Text-to-Speech, Speech-to-Text, virtual agents, and the Dialogflow platform, to help create conversational actions across applications and devices.

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Is an AI as Solution Right for You?

Before considering AI as a Service, assess your process to determine whether it can be easily defined with rules. If yes, traditional automation might be a better fit for your goals.

There’s no need to complicate things by introducing AI into a process that is straightforward enough to be automated with robotic process automation or other legacy technologies.

Testing the AIaaS Product: What’s the Vendor’s Trial and Security Process?

Like any other software solution, you should test AIaaS products to ensure they fit your business needs. Look for a vendor that allows you to test the product with your data. Any AIaaS platform should also be able to answer your data security questions transparently.

API Security: Does the Product Have a Secure API?

Before deciding on an AIaaS vendor, check out any data compliance rules you may have internally and the vendor's SOC 2 credentials. This will help ensure that your organization’s data will be secure when using the product.

Start Building with $10 in Free API Credits Today!

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

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