Edge Computing Vs. Cloud Computing: What Is the Difference?

Edge Computing Vs. Cloud Computing

Living in a technologically advanced era, the debate over edge computing vs. cloud computing continues to exist. It is now possible for enterprises to use public cloud computing services to add global servers to their own data centers, extending their infrastructure to any location and allowing them to scale computational resources up and down just as much as necessary. These hybrid public-private clouds provide enterprises with computing applications that give the company a high level of flexibility, value, and security. This was never possible before for the enterprise.

However, real-time AI applications can demand a lot of local processing power, frequently in remote areas too far from centralized cloud servers. And due to low latency or data-residency constraints, some workloads must stay on-site or in a specific area.

This is why many businesses use edge computing, the term for processing where data is produced, to deploy their AI applications. Edge computing manages and saves data locally on an edge device rather than doing the task in a remote, centralized data reserve. And the device can function as a stand-alone network node rather than relying on an internet connection.

Moreover, before we talk further about the many differences between edge computing vs. cloud computing, you need to understand the systems and what they mean properly. This will help you differentiate and understand the rest of the article better.

What Is Edge Computing?

Edge computing is technology making it easy and fast to process data and storage by placing computer systems as close as feasible to the device, application, or component that gathers or generates data.

All data processing takes place at the edge, which reduces the need for communication with a central processing system and speeds up the processing time. This increases the effectiveness of data processing, reduces the need for internet bandwidth, lowers operational expenses, and makes it possible to operate apps in remote areas with spotty connectivity.

Edge computing delivers greater data control, lower costs, quicker insights and actions, and more continuous and organized operations by moving data processing and storage closer to its source.

What Is Cloud Computing?

Cloud computing can be explained very easily. When using hosted services like servers, data storage, networking, and software through the internet, the information is kept on real servers that the cloud service provider manages.

Businesses may now access cutting-edge software, servers, storage, networking, development tools, and other resources on demand via the internet for very little cost due to cloud computing. Cloud computing services are housed in distant data centers run privately by an enterprise or a third-party vendor.

Differences Between Edge Computing Vs. Cloud Computing

After deeply understanding the computing systems, it is time to figure out the main differences between them and how each works.

Data processing

Cloud computing indeed has non-time sensitive data processing, and edge computing has real-time data processing. This means that cloud computing has physical servers that do the work immediately compared to edge computing.


Another vast difference between edge computing vs. cloud computing is that cloud computing always needs a reliable internet connection. In contrast, in the case of edge computing, it can also work in remote sites where there is little or no internet connectivity.

Data storage

If you have any sensitive data and wondering if you should go for edge vs. cloud, then an edge computing system is the one for you. In cloud computing, data is stored in the cloud, whereas in edge computing, the data is highly sensitive and comes with strict data laws making it too costly to send to the cloud.

Edge Vs. Cloud – Functionality

Edge computing vs. cloud computing focuses on resolving different kinds of problems. On one hand, edge computing aims to facilitate devices that require data transformation with latency, whereas cloud computing helps organizations manage. Cloud computing processes data that is not time driven as it mainly functions with the workload within the cloud. On the other hand, edge computing facilitates users with workloads on edge devices, even in places with limited or no connectivity. This is why edge computing is preferred in remote locations with little or no connectivity. It helps maintain local storages and specialized devices that are required to respond to specific instructions.

There are many workplaces and devices that are supposed to work in low or connectivity areas with local storage. These organizations find edge computing fairly simple and easy to analyze and transform their data.  However, cloud computing helps organizations that no longer require their technical infrastructure to run from physical surfaces. They rather opt for cloud solutions that help their scalability to a larger extent. Although Cloud computing requires connectivity and storage expenses, it enables larger organizations to achieve scalability with cloud-based technology infrastructure. These are typical tech bases organizations with a specific need to share their database from a centralized cloud base. Therefore, for such organizations, cloud computing helps different workplaces to stay connected through a centralized shared workplace system.

In comparison, edge computing helps more common organizations run their routine tasks and maintain their technical infrastructure offline and with easy processes. Moreover, many businesses and enterprises have now adopted the edge computing system due to its ability to resolve minor cloud computing concerns. Thus, edge computing is much better in the eyes of businesses—a vast amount of data such as websites, applications, etc.


After understanding what edge computing vs. cloud computing is and learning the difference between these computing systems, it is concluded that both edge vs. cloud are different in many ways as both systems aim at different problems. Therefore, they cannot be replaced with one another; instead, both computing systems provide problem-solving features to cater to different users.

Also Read: Cloud Computing Trends 2022: What to Look Out For