Edge AI vs. Cloud AI: What Is the Difference?

A key distinction between edge artificial intelligence (AI) and cloud AI lies in where data processing occurs. In simple terms, edge AI processes data directly on the device where it's generated, while cloud AI sends that data to a remote server for processing. Edge AI systems analyze data closer to its source, referred to as “on the edge.” In contrast, cloud AI systems operate applications in private data centers or cloud facilities. Each approach has unique benefits, and the best choice for your organization depends on your computing power, response time, bandwidth, and security needs.

Additionally, while each approach has distinct uses, it is helpful to note that they are deeply interconnected. Edge AI relies on cloud computing for training and deploying machine learning models and continues to depend on the cloud for ongoing improvements, with problematic data sent to the cloud for additional training.

Demand for both edge AI and cloud AI technologies is rapidly increasing. As businesses aim to optimize operations and enhance decision-making through digital transformation initiatives, the demand for technical solutions to support these changes continues to grow. Learn about the specifics of these approaches and explore use cases and pros and cons for each.

What is the difference between edge AI and cloud AI?

Edge AI and cloud AI differ in several aspects, including speed, latency, privacy, computational power, network reliability, and scalability. They are distinct technologies that can complement each other, allowing businesses to leverage both strengths.

Before exploring the specifics of each approach more deeply, consider a brief overview of some key differences.

What is edge AI?

Edge AI, also called “AI on the edge” or “on-device AI,” combines edge computing with artificial intelligence to enable real-time data processing and analysis at the point of user interaction. This can occur on a computer, edge server, or any Internet of Things (IoT) device, such as smartwatches, smartphones, or smart home devices like thermostats. 

With edge computing, data processing, storage, and analysis occur closer to their source. With AI systems, such as predictive analytics, speech and image recognition, or anomaly detection, integrated data processing and response generation happen directly on the network edge, providing real-time feedback based on specific programming parameters.

Edge AI eliminates the need to send data to a remote server for processing, resulting in faster response times, reduced latency, and enhanced privacy and security. 

What is edge AI used for?

Edge AI is found in smart devices like phones, watches, and appliances; virtual assistants like Google Assistant, Siri, and Amazon Alexa; real-time traffic updates; and autonomous vehicles. It has applications across various industries, including health care, manufacturing, retail, smart homes and cities, and security and surveillance. 

• Health care: Edge AI enhances patient experiences by reducing diagnosis and treatment times and enabling real-time patient monitoring with IoT devices. Its rapid data processing facilitates the swift exchange of information among health care professionals, which is crucial for emergency patient stabilization.

• Manufacturing: Edge AI improves operations in areas such as quality control, worker safety, and supply chain analytics. Edge-AI-powered sensors perform predictive maintenance, identifying potential issues and alerting staff proactively.

• Retail: Edge AI can help brick-and-mortar stores compete with e-commerce by enabling innovations like smart shopping carts and checkouts. It allows businesses to analyze customer data in real time, creating a more dynamic customer experience, reducing inventory loss, and maintaining appropriate inventory levels.

• Security and surveillance: On smart security devices, edge AI’s computer vision and object detection capabilities promptly notify users of potential issues and allow for real-time image and video analysis. It can also detect abnormal banking activity, informing you of suspicious transactions.

• Smart homes and cities: Smart devices like doorbells, thermostats, and refrigerators use edge AI to process data on-site, monitoring heating, cooling, and lighting, identifying faces, detecting motion, and providing alerts. Cities also use edge AI for traffic pattern analysis and parking management. 

Pros and cons of edge AI

The benefits of edge AI include reduced latency, lower bandwidth usage, real-time processing, enhanced data privacy, and lower costs, likely contributing to its $20.78 billion market size, which should increase by around 22 percent annually between 2025 and 2030 [1]. Offline capabilities and the removal of cost barriers make Edge AI an accessible option. The technology can help businesses automate processes, improve decision-making, and optimize operations, while offering a more sustainable AI option due to local, low-power data processing. 

However, this sustainability comes at the expense of computing power, which may be a downside depending on your needs. Additionally, the storage capacity of edge AI is another factor to consider—the amount of storage you have is limited to the edge device.

What is cloud AI?

Cloud AI, or AI as a service (AIaaS), refers to AI models running on remote servers, typically hosted by cloud providers like Google Cloud, AWS, or Microsoft Azure. It integrates AI with cloud computing to give organizations access to large data sets and significant computing power without relying on complex and costly on-premises infrastructure such as databases, software, facilities, and hardware. 

Cloud computing provides AI with the infrastructure and computational power to efficiently process complex tasks such as image recognition, natural language processing, and data analytics. This combination enables organizations to enhance operations through strategic, data-driven decisions and automation, streamlining processes and increasing efficiency. 

What is cloud AI used for?

Chatbots, business intelligence, IoT, and generative AI, among other applications, rely on cloud AI. It is especially beneficial for industries relying heavily on large data sets, such as e-commerce, banking, and health care.

• Health care: Cloud AI supports modern health care initiatives, from drug discovery and patient data management to diagnosis and prognosis. It streamlines treatment development by quickly analyzing biomedical data and optimizing clinical trials using patient history data. 

• Retail: With cloud AI’s computing power, businesses can predict trends, optimize operations, and make data-driven decisions. Its predictive analytics and machine learning capabilities enable cloud AI to provide tailored advice to customer questions, personalized product recommendations, and streamline inventory management, enhancing customer satisfaction and reducing costs.

• Finance: Cloud AI excels in tasks like fraud detection and risk management to detect unusual patterns or abnormalities in large data sets. 

Pros and cons of cloud AI

Cloud AI offers scalability, accessibility, processing power, and potential cost savings, making it appealing for businesses. It is generally easier to upgrade than edge AI, ensuring timely updates. Since cloud AI isn’t dependent on individual hardware, processing capacity can be adjusted as needed, benefiting organizations with fluctuating workloads.

Cloud AI increases accessibility to powerful AI models, which were previously too technical and expensive for widespread use, for organizations of all sizes. It offers application programming interfaces (APIs) for easy integration into existing operating systems.

However, challenges remain, including concerns around latency, connectivity, data privacy, and integration. Cloud AI’s reliance on remote data centers can lead to increased latency and bandwidth issues. Sensitive data transfer raises privacy concerns, and because cloud AI requires an internet connection, its functionality can suffer in the face of unreliable connections. 

Edge AI vs. cloud AI

Edge AI is likely a suitable choice if you need real-time decisions, offline functionality, or enhanced privacy. On the other hand, cloud AI may be preferable if you require computational power, increased data storage, or continuous model updates.

It’s also helpful to note that many industries now employ both edge AI and cloud AI. This hybrid approach, where edge AI manages real-time tasks and cloud AI handles complex processing, can optimize performance more effectively than either alone. One cannot fully replace the other, making hybrid options attractive to forward-thinking organizations.

Edge AI vs. cloud AI cost

Determining the cost benefits of these approaches isn’t as straightforward as simply comparing the two and declaring one as less expensive than the other. Because edge AI and cloud AI offer unique benefits, comparing the two in terms of cost doesn’t provide a clear picture. Both have the potential to provide organizations with cost savings depending on their needs. 

The integration of AI into cloud computing resulted in significant cost savings compared to using cloud computing alone. Before this integration, managing fluctuating workloads and complex cloud environments was increasingly costly and challenging for organizations. The introduction of AI techniques into cloud computing provided businesses with pay-as-you-go pricing without having to invest in expensive hardware. AI-powered algorithms enabled automation, leading to cost savings due to streamlined operations, increased efficiency, and reduced dependency on manual labor. Cloud AI provided more organizations with the chance to access AI-driven insights and capabilities, reducing operational costs and enhancing performance. 

As edge AI technology has evolved, it has enhanced many of the applications of cloud AI and provided more options for organizations. Although consumers may face an upfront cost in acquiring an edge AI-capable device, businesses deploying edge AI typically experience cost savings. Processing data on the edge reduces costs associated with data transfer, cloud resource use, and the need for data supervision by data scientists. Edge AI has provided even greater access with less expensive infrastructure needs and preloaded algorithms.

Learn more about artificial intelligence on Coursera

Edge AI and cloud AI offer distinct benefits in terms of speed, scalability, privacy, and computing power, and you can use both independently or together to meet different organizational needs. Learn more about implementing machine learning models and building deep learning models with the AI Engineering Professional Certificate from IBM on Coursera. To prepare for a career in business intelligence, explore topics such as data collection, analysis, visualization, and reporting in the Business Intelligence (BI) Analyst Professional Certificate, also offered by IBM on Coursera.