Edge AI versus cloud AI: What’s the difference?

While there are similarities between them, there are also crucial differences worth considering when assessing each for business purposes.  

Edge AI refers to the process of using AI algorithms and AI models on edge or Internet of Things (IoT) devices like smartphones, thermostats and wearable health monitors. Edge AI gets its name from edge computing, a type of distributed computing that brings applications closer to data sources.

Cloud AI, alternatively, is a type of AI that depends on cloud computing—on-demand access to virtual compute resources over the internet—to function.

While both types support advanced data processing and analytics, they differ in how they run AI models and where they store and process data, giving them different applications and benefits.

Edge AI is a type of AI that deploys AI algorithms onto devices at the “edge” of a network, meaning close to its boundary with the real world, where it loses connectivity. These devices—commonly referred to as edge or Internet of Things (IoT) devices—include smart watches, smartphones, industrial sensors and wearable health monitors.

Edge AI uses certain kinds of algorithms to process data closer to its source rather than moving it into the cloud first. It thus enables real-time decision-making, an important capability in the devices it powers.

Edge AI is also becoming popular as a way to optimize workflows in complex industries like manufacturing and supply chain management. It’s a way for enterprises to reduce traffic and latency across their networks.

Unlike other types of AI, edge AI devices can function offline, making them ideal for applications that can’t depend on a constant internet connection for functionality.

Cloud AI refers to a type of AI that depends on cloud infrastructure for data processing and analytics. In cloud AI, data is collected at its source and moved into the cloud through an internet connection. There it can access virtual compute resources connected for data processing, data analysis and data storage.

While older and not considered as advanced as edge AI, cloud AI still has many applications for modern enterprises. It helps developers deploy AI applications that are too complex and compute-intensive to be deployed on the edge. Examples include the training of deep learning (DL) models and certain kinds of natural language processing (NLP) for trend analysis and predictive analytics. 

Both edge and cloud AI models are trained through machine learning (ML), a branch of AI that has become the backbone of most modern AI systems.

However, while the purpose of both edge and cloud AI is to process and analyze data for powerful AI applications, they accomplish these tasks in different ways: Edge AI processes data locally on small devices, whereas cloud-based AI leverages the compute power of the cloud. Here’s a closer look at each method.

Edge AI uses AI models that have been trained to identify objects by using neural networks and deep learning. While edge AI itself is deployed on devices, the training processes used to create its models depend on centralized cloud infrastructure. Data centers are required for the real-time processing of large volumes of data, essential for training purposes.

After edge AI models are deployed, they “learn” over time, gradually improving their abilities. They do so until they can spot data that they aren't able to process locally and can move it into the cloud instead. Through this method of constant feedback, the initial edge AI model that was deployed is eventually replaced by a new one that’s been trained in the cloud over time.

Unlike edge AI, cloud AI relies on the massive compute power and storage capabilities of cloud infrastructure for its functionality. Typically, these services are provided by large, global cloud service providers (CSPs) like Amazon (AWS), Google and Microsoft.

This approach makes Cloud AI a better choice than edge AI for compute-intensive tasks like big data analytics, high-performance computing (HPC) and the training of foundation models for advanced AI applications like computer vision and NLP.

By integrating AI systems into both public and private cloud platforms, cloud AI helps organizations deploy advanced AI applications at the enterprise level. These applications serve various purposes, such as optimizing business processes, generating insights and deploying customer service chatbots.  

There are important differences between edge and cloud AI that make each better suited for different use cases.

With enterprises rushing to build new AI and generative AI (gen AI) applications, interest in cloud and edge AI models is skyrocketing.

According to a recent report, the global edge AI market was valued at USD 20.45 billion in 2023 and was expected to reach nearly USD 270 billion by 2032.¹ During practically the same period, the global market for cloud AI was expected to jump from USD 78 billion to almost USD 590 billion.²

Here’s a closer look at the business benefits of both types of AI and how businesses are using them to reach their goals.

• Real-time decision-making: Leveraging the power of real-time data processing at the edge of networks, edge AI solutions allow IoT devices to improve response times. AI applications for this technology are soaring, from remotely piloted or autonomous vehicles like drones, to fully automated facilities that rely on IoT devices like sensors and cameras.
• Data privacy: Processing data locally rather than in the cloud keeps it safe from cyberattacks and reduces the risks of mishandling. For this reason, edge AI is receiving considerable attention from industries subject to data sovereignty restrictions like financial services and healthcare.
• Cost savings: Edge AI is considerably less expensive than cloud AI because it reduces the workload for cloud computers. By processing data locally, edge AI reduces the amount of data that depends on cloud resources, significantly reducing the operational costs of deploying the technology.

• Increased scalability: Cloud AI is inherently more scalable than edge AI because of its reliance on virtual compute resources that can more easily be scaled up and down to meet demands. Edge AI requires investment in on-premises hardware (for example, edge and IoT devices and sensors) that can be deployed close to data sources for local processing.
• Higher performance: Cloud AI typically reaches higher performance levels than edge AI because of its access to the resources and processing power the cloud can offer. Edge AI, alternatively, is restricted to the compute power and resources of individual edge and IoT devices.
• Access to large-scale datasets: Because of their access to cloud resources, cloud AI models are equipped to handle larger AI workloads than edge AI. The intensive training of deep learning models, for example, requires large datasets that can be accessed only through the cloud rather than through local processing.

Edge and cloud AI use cases at the enterprise level vary considerably, given the specific strengths of each of the models. Here are the most popular use cases for each.

• Autonomous vehicle operation: Edge devices help autonomous vehicles like satellites, drones and self-driving cars react to real-time changes in their environments, such as switching traffic signals or objects in their path.
• Wearable health devices: Edge AI devices are built into many wearable monitors to help patients and healthcare providers track vital information like heart rate, blood pressure and oxygen levels.
• Manufacturing processes: Enterprises are deploying edge AI on factory floors and inside complex manufacturing systems to automate processes, detect changes in machine performance and increase efficiencies.
• Retail stores: Business-to-consumer (B2C) businesses leverage edge AI to deploy smart checkout experiences, track inventory and create more personalized recommendations based on real-time data processed locally on edge devices.

• Big data analytics: Integrating AI with cloud data warehouses and data lakes enables businesses to uncover trends in data in near real-time, a process known as big data analytics. This type of analytics would take a team of human analysts years to parse.
• Large-scale NLP for sentiment analysis: Large-scale NLP relies on cloud AI to process text and other data used for sentiment analysis, a valuable AI application that determines whether text expresses a positive or negative sentiment.
• AI model optimization: Cloud AI ensures that sophisticated AI models have access to the massive compute power and resources needed to continuously train and develop, a critical aspect of AI technology.
• Chatbots: Popular chatbots like ChatGPT, Gemini and IBM watsonx all leverage the considerable power and compute resources of cloud AI for most of their functionality. This use includes automating workflows, conversing naturally with users and scouring big datasets for patterns and insights.