Verdant TCS

Cloud Computing vs. Edge Computing: What Your Business Needs to Know

By Mike Shollack,
CEO & President

If you’re a decision-maker for your business, you’ve almost certainly been hearing about cloud computing for years. But as the number of connected devices that collect and transmit data (including Internet of Things devices) continues to explode, and the need for secure and lightning-fast connections increases, more and more companies are beginning to look at edge computing solutions.

Cloud computing and edge computing are often conflated, and there’s a lot of misunderstanding about what these two terms actually mean. In this blog, we’ll take a closer look at these two technologies and assess their pros, cons, and what your business needs to know about them.

If you still have questions when you’re done, please reach out to our team of cloud technology experts at Verdant TCS. We’ll be happy to walk through the options that make sense for your business.


That’s a huge question, but let’s consider how cloud computing works in fairly simple terms.

In a traditional cloud setup, all your data storage, applications, and processing power lives on a centralized cloud server, probably in a data center very far from where you live. If you want to access the network from your laptop, smartphone, or other device, it has to upload a request to the central server. Then, the cloud server has to send the data back to your device.

Cloud computing offers some massive benefits over in-house systems, including security, reliability, scalability, huge amounts of data storage and processing power, and cost. But as the number of internet-capable devices and the amount of data sent over the internet continues to expand rapidly, there’s one downside that may become increasingly prevalent: latency.

Traffic Jams on the Information Superhighway

When you send a request to the cloud server, that “packet” of data may have to travel hundreds or even thousands of miles. While data can travel at the speed of light, it also often needs to pass through different intermediary routers (or “nodes”) along the way to its destination, each of which adds some measurable delay.

Furthermore, if there is heavy congestion along the network, with many users all trying to access large quantities of data from the same big data centers at the same time, requests can be delayed even further if the available bandwidth isn’t sufficient to handle all the traffic.

At best, this can be an inconvenience to the user and reduce the speed and efficiency with which they can accomplish tasks. At worst, it can lead to significant business or even safety issues when near-instantaneous data processing is critically important.

RELATED ARTICLE: The Cloud Is Confusing Enough. Don’t Fall For These Common Myths


With edge computing, rather than having a far-away cloud solution like Amazon Web Services or Microsoft Azure handling all the processing, some of the data storage and processing capabilities are offloaded to devices or servers that are closer to the end user’s physical location—including, potentially, the end user’s device itself. This is a decentralized computing infrastructure that puts processing power at the “edge of the network.”

Even if the edge device doesn’t have the same raw computing power as the cloud, its proximity to the user (and the ability process data on its own, without needing to send massive amounts of data to the cloud) can result in significant speed increases and potential cost savings.

Common Examples and Use Cases of Edge Computing vs. Cloud Computing

One example of edge computing is how video streaming services might choose to “cache” their most popular programs in local servers closer to end users. When a person wants to watch a popular show, they can get it from whichever local server happens to be closest to their location, rather than the “core” data center. That means faster streaming (and less traffic congestion for streamers everywhere).

Another example concerns the increasing scope of the Internet of Things (IoT). How many devices do you own—both personally and in terms of company equipment—that have software, data, and wireless connections with other objects or networks? Today, consumer IoT devices can include everything from autonomous cars to smart speakers, light bulbs, TVs, security systems, refrigerators, smoke detectors, and more.

A business application might involve machines and robots on a smart manufacturing plant that need to communicate with one another.

For some of these applications, like a smart lighting system, full cloud computing is unnecessary. (Your smart home shouldn’t have to connect to a data center thousands of miles away to turn off your lights.) For others, the extremely low latency of edge computing could help you avoid costly or even deadly errors. A decision-making delay of even a few hundred milliseconds could be catastrophic for a malfunctioning machine on an assembly line, or an autonomous car that’s just detected an obstacle in the road.


As time goes on, and especially as IoT devices become more prevalent throughout the business world, the demand for edge computing will grow. However, that does not mean that cloud computing is going away anytime soon. Cloud and edge computing each have unique strengths, and they can and will continue to co-exist.

You might prefer edge computing for applications where getting the lowest possible latency is the main objective, or in remote locations where establishing a mini data center is a better option than using cloud servers thousands of miles away.

But for many organizations—especially those that aren’t heavily relying on IoT devices just yet—there may be little reason to consider an “edge-first” strategy at this time. And cloud computing is still going to be the IT environment of choice whenever you need to leverage the cloud’s superior computer power to aggregate and analyze a ton of data, or if a vast amount of cloud storage is required. Advanced artificial intelligence and machine learning capabilities, for example, rely on the cloud’s advanced, large-scale data analysis capabilities that would not be easily replicable in an edge environment.

The trick, then, isn’t going to be choosing one or the other, but figuring out how to effectively leverage the two technologies in a way that makes the best sense for your business needs.

RELATED: Custom Cloud Computing Solutions That Are Scalable And Secure


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