Why Are Businesses Turning To Edge Computing Solutions?

From factory floors to retail stores to remote sites, businesses are generating more data at the edge — and traditional cloud models can’t always keep up with the speed and responsiveness required. That’s why more companies are turning to edge computing solutions to process data closer to where it’s created. 

By reducing latency, improving performance and enabling real-time decision-making, edge computing empowers organizations to act faster, operate smarter and stay competitive in a connected world. In this blog, we’ll explore what edge computing solutions are, how they work and why they’re becoming essential across industries.

Understanding the edge computing basics

To fully grasp the benefits of edge computing, it’s important to understand the basics of how edge computing works. Here’s a brief overview of what edge computing is, what it does and why it’s so necessary to stay competitive.

What is edge computing?

At its core, edge computing is a distributed IT framework that brings computation and data storage closer to where data is generated, like sensors, IoT devices or local machines. Rather than relying entirely on centralized cloud infrastructure, it processes data at or near the source to minimize delays and reduce the burden on network bandwidth.

This approach is especially useful for scenarios where real-time processing is critical. Applications such as autonomous vehicles, smart manufacturing or healthcare monitoring benefit from being able to analyze and act on data instantly. While it doesn’t replace the cloud, edge computing complements it by providing faster response times, improving resilience and enabling more efficient use of resources.

How does edge computing work?

Instead of routing all data to a distant cloud server, edge systems use local nodes — like smart gateways or mini data centers — to process and filter information close to where it’s produced. These local resources handle time-sensitive tasks on-site, sending only essential data back to the cloud for further storage or broader analysis.

The architecture typically includes edge devices, nearby computing infrastructure and a connection to centralized cloud services. This tiered setup allows systems to prioritize local decision-making for speed while still leveraging the cloud’s scalability for more intensive workloads. It’s an effective balance that combines the benefits of distributed and centralized computing.

Why is edge computing needed?

Traditional cloud models often struggle to meet the speed and responsiveness required by today’s data-heavy applications. With the rise of connected devices, transmitting all raw data to the cloud can lead to latency, bandwidth congestion and slower decision-making — none of which are acceptable in environments like automated factories or critical healthcare systems.

In addition to performance, reliability is a key driver. Operations in remote or disconnected areas — such as offshore oil rigs or rural farms — still need to function even if internet connectivity is lost. Edge computing enhances reliability in such scenarios by allowing systems to operate independently until reconnection occurs. It also helps meet data privacy and regulatory requirements by enabling local data processing and minimizing unnecessary data transfers.

Edge computing devices

Edge computing relies on a variety of devices that bring computation closer to where data is created, reducing the need for constant communication with central cloud systems. These components form the backbone of modern distributed systems by enabling faster decision-making, improving efficiency and supporting environments with limited connectivity. Below are key types of edge computing devices and how they make edge processing possible.

Sensors

Sensors are the data generators at the edge, capturing temperature, motion, pressure, location or other environmental inputs. While traditional sensors send raw data to the cloud for analysis, edge-enabled sensors can now perform basic filtering or preprocessing locally. This reduces bandwidth consumption and allows time-sensitive decisions to happen closer to the source.

Routers

Routers at the edge do more than route data — they often act as the first layer of control in edge computing environments. Many modern edge routers come with built-in processing capabilities that allow them to prioritize traffic, apply security rules or even run lightweight applications. This ensures that only critical or summarized data is passed to the cloud while maintaining local performance.

Edge gateways

Edge gateways serve as intermediaries between edge devices and the cloud, aggregating and processing data from sensors, machines or local networks. These devices translate different protocols, filter noisy data and may run local applications that trigger automated actions. Gateways help ensure that systems remain responsive even when internet connectivity is slow or disrupted.

Edge chips (AI/ML accelerators)

Specialized edge chips, such as AI accelerators or system-on-chips (SoCs), enable devices to run machine learning models or perform complex computations locally. Found in everything from smart cameras to industrial equipment, these chips reduce reliance on external servers for analysis. This supports real-time processing and makes intelligent decision-making possible directly at the device level.

Edge platforms

Edge platforms are software and hardware ecosystems that manage the deployment, orchestration and monitoring of applications across edge devices. These platforms may include container runtimes, real-time operating systems or lightweight virtualization environments. They ensure consistency and security across a fleet of edge nodes, enabling centralized control of distributed compute resources.

How does edge computing work with other technologies?

Edge computing doesn’t exist in a vacuum. It plays a critical role in enabling other transformative technologies to reach their full potential. As organizations embrace real-time data, distributed systems and intelligent automation, they need computing power that’s fast, local and efficient. Edge computing meets that need by reducing latency, improving responsiveness and allowing key technologies like IoT, 5G and AI to function effectively at scale.

Internet of Things (IoT)

IoT devices generate massive amounts of data from sensors, machines and everyday objects. Without edge computing, all of that data would need to travel to a central cloud for processing, causing delays and consuming unnecessary bandwidth. Edge computing allows this data to be filtered, analyzed and acted on closer to where it’s generated, which is especially important for applications like industrial automation, smart cities or connected vehicles.

By processing data locally, edge computing helps IoT systems respond faster and become more efficient. For example, a smart building can adjust lighting, HVAC and security systems in real time without relying on an external network. In industrial settings, edge-enabled IoT devices can detect equipment failures and trigger preventative maintenance before downtime occurs. This level of autonomy and responsiveness simply isn’t possible with cloud-only infrastructure.

5G connectivity

The promise of 5G goes beyond faster mobile internet — it’s about enabling ultra-low latency, high bandwidth and massive device connectivity. But without edge computing, the speed advantage of 5G is limited, since data still needs to travel long distances to centralized data centers. Edge computing places processing power closer to users and devices, unlocking the full value of 5G by minimizing lag and making real-time applications truly feasible.

Together, 5G and edge computing create a foundation for next-generation experiences like remote surgery, autonomous driving and immersive augmented reality. These use cases depend on real-time decision-making and cannot tolerate delays. By combining ultra-fast data transmission with localized compute resources, 5G and edge computing work in tandem to enable a new class of applications that are both responsive and resilient.

AI

AI models require significant compute power to process data and make decisions, which historically meant relying on cloud infrastructure. But edge computing now allows for lightweight versions of those models to run on local devices like cameras, sensors or gateways. This means AI can analyze images, recognize speech or detect anomalies instantly, without needing to send data to the cloud and wait for a response.

Running AI at the edge opens the door to smarter, faster systems across industries. In retail, cameras equipped with edge AI can monitor shelf inventory and customer behavior in real time. In healthcare, AI algorithms on edge devices can detect irregular heartbeats or oxygen levels and alert caregivers instantly. This shift from centralized AI to distributed intelligence makes critical systems more agile, private and responsive.

How are businesses using edge computing solutions?

Edge computing is transforming how businesses operate by enabling faster decision-making, improving performance and supporting real-time applications. By processing data closer to where it’s generated, companies can reduce latency, minimize bandwidth use and improve reliability without relying entirely on centralized cloud systems. Below are four practical use cases that show how businesses are putting edge computing solutions to work.

Real-time analytics in manufacturing

Manufacturers use edge computing to monitor equipment performance, production lines and environmental conditions in real time. Sensors and controllers on the factory floor process data locally to detect anomalies, predict maintenance needs or automatically adjust machinery. This reduces downtime, improves product quality and helps avoid costly production delays.

Smart retail operations

Retailers leverage edge solutions to personalize in-store experiences, manage inventory and speed up point-of-sale transactions. For example, smart cameras can analyze foot traffic and shopper behavior to optimize store layouts or trigger dynamic pricing. By handling these tasks locally, retailers reduce response time and maintain performance even during network disruptions.

Connected healthcare devices

Hospitals and clinics use edge-enabled medical devices to monitor patients continuously and respond to changes in real time. Devices like heart monitors or infusion pumps can analyze vital signs locally and alert staff to potential issues immediately. This helps improve patient outcomes, especially in intensive care settings where every second counts.

Logistics and fleet management

Edge computing plays a key role in logistics by enabling delivery vehicles and shipping centers to process data on location. Vehicles equipped with GPS, cameras and sensors can make real-time decisions about routing, fuel efficiency or driver safety. Edge devices in warehouses support faster barcode scanning, inventory tracking and equipment automation.

Edge computing success stories

Edge computing is driving tangible results for businesses around the world. From optimizing data collection at sea to transforming retail operations, organizations are using edge solutions to unlock speed, efficiency and real-time intelligence. Below are four success stories that highlight how companies in different industries are putting edge computing to work with the help of SUSE technologies.

Orange: Scaling 5G edge infrastructure across Europe

Headquartered in Paris, France, Orange is one of the world’s leading telecommunications operators. The company offers a wide range of services, including mobile, fixed broadband and telecommunications solutions for enterprise customers.

To make these services possible, Orange had been relying on old virtual network functions (VNFs). However, the company needed to modernize to the cloud to stay at the forefront of the telecommunications industry. They joined Project Silva, which aims to leverage open source technology, reduce operating costs through interoperable network infrastructure and comply with stringent regulatory requirements around information security and data governance.

Their first step was implementing SUSE solutions. With Rancher Prime, SUSE empowered Orange to manage 100% of their cloud deployments with Kubernetes. SUSE also helped them streamline the deployment processes for containerized applications that cut time-to-market for their new services. 

By bringing compute power closer to customers and devices, Orange delivered the low-latency performance required for 5G applications — while maintaining full control and efficiency across its network infrastructure. Read more about how they modernized with SUSE edge computing power. 

Danelec: Maritime data processing at the edge

Danelec, a Danish maritime technology leader, is charting course to a goal of net-zero emissions by 2050. One of ways to do that was to invest in digital solutions, which could optimize travel efficiencies and document the environmental impact of sea level transportation.

When considering different solutions, Danelec was impressed with SUSE’s open-source Kubernetes support, single-pane-of-glass management and enterprise support. They implemented SUSE Edge, which was perfect for highly distributed locations like their ships. 

SUSE technology now supports everything related to voyage optimization, route optimization, weather routing and anything else in relation to optimizing the operations of its customer’s deep sea vessels. Dive into how SUSE’s edge computing solutions charted a new course for Danelec.

Kratos: Delivering software-defined satellite communications

Around 80% of commercial satellite and space operations rely on Kratos solutions. But as consumer demand for terrestrial communications networks started to skyrocket, driven largely by increases in mobile voice services for consumers who are out of range of terrestrial cell towers, Kratos knew they had to step up. 

But with ground stations in extremely remote areas, the company knew that delivering satellite services at the edge would be a challenge. They trusted SUSE solutions to manage the full lifecycle of edge devices at scale.

As a result, Kratos reduced setup time to initiate client services from weeks to minutes. All client data is protected by robust, externally validated security capabilities, and it’s all done with zero-touch provisioning. Learn more about how SUSE edge solutions empowered Kratos to reach for the stars. 

Stylez: AI-driven quality control in automotive manufacturing

Manufacturing is an industry that is experiencing major leaps as it invests in edge computing. Stylez, a Tokyo-based company that manufactures electronics for cars, is a prime example. 

As Japan’s birth rate started to decline and its aging population grew, competition for skilled employees was becoming more difficult than ever. To help make up for staff shortages, Stylez started investing in intelligent automation and orchestration solutions. They decided that a containerized approach worked best for their factories. 

Stylez wanted a Kubernetes distribution backed by long-term support from a recognized industry leader for two key reasons. First, it aimed to achieve a rapid rollout without needing teams to gain deep expertise in Kubernetes. Second, it wanted to ensure that its customers could achieve the highest possible levels of availability for edge computing solutions in their factories.

SUSE Rancher Prime was the perfect fit for their distributed environment. With SUSE Rancher Prime, Stylez was able to enhance efficiency of containerization setup by up to 40%. Its ease of scalability enables deployment of a large number of edge devices. The modular, self-healing software ensures the highest quality of their electronics. Learn more about how SUSE sparked efficiency and productivity for Stylez.

Edge computing solutions: Final thoughts

Edge computing solutions are the key for modern organizations to process data faster, operate more efficiently and unlock new possibilities at the network’s edge. Whether you’re modernizing manufacturing, enabling remote operations or building intelligent services, having the right edge platform makes all the difference. 

SUSE provides secure, scalable and open edge computing solutions designed to meet the demands of today’s distributed environments. Learn how SUSE can support your edge strategy.

Edge computing solutions FAQs

What is the difference between edge computing and cloud computing?

Edge computing processes data locally, near the source of data generation, to reduce latency and enable real-time decisions. Cloud computing, on the other hand, relies on centralized data centers that handle processing and storage remotely over the internet.

How can edge computing be kept secure?

Edge computing can be secured through strong device authentication, encryption, zero trust architecture and continuous monitoring. Because edge environments are often distributed and remote, consistent security policies and automated patching are essential.

What are the challenges of edge computing?

Key challenges include managing a large number of distributed devices, ensuring consistent security and maintaining reliable connectivity in remote environments. Organizations must also address limited on-site resources and the complexity of scaling infrastructure at the edge.