February 25, 2026
CloudRAN.AI and Firecell Partner to Cut the Cost and Complexity of Private 5G
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Edge computing and cloud computing play distinct roles in private 5G networks, especially when it comes to latency. Here’s the key takeaway: edge computing is faster for time-sensitive tasks, while cloud computing suits less urgent, large-scale operations.
| Feature | Edge Computing | Cloud Computing |
|---|---|---|
| Latency | 1–10 ms | 50–200+ ms |
| Data Processing Location | Near the source | Centralised data centres |
| Best For | Time-sensitive tasks | Long-term analysis/storage |
| Resilience | Local operations continue | Reliant on connectivity |
Edge computing is crucial for industries needing immediate responses, like manufacturing and logistics. Cloud computing, however, excels in handling large-scale, non-critical tasks. Many businesses now rely on hybrid models to balance speed, cost, and scalability.
Edge Computing vs Cloud Computing Latency Comparison for Private 5G Networks
Edge computing focuses on processing data right where it is generated – whether that’s on factory floors, in warehouses, or at industrial sites – bypassing the need to send it to far-off data centres. This decentralised setup avoids the delays and limitations tied to long-distance data transmission.
Here’s how it works: computation and storage are brought closer to IoT devices and sensors. In private 5G networks, the User Plane Function (UPF) directs traffic to a local Edge Data Network. This setup ensures data travels just one or two network hops, dramatically reducing transmission time. By keeping data processing local, edge computing achieves much lower latency, as explained further below.
This shift is gaining momentum. By 2025, 75% of enterprise data is expected to be processed at the edge, a huge leap from just 10% today. Rob van der Meulen from Gartner Research emphasises this:
By 2025, 75% of enterprise data will be processed at the edge, compared to only 10% today.
Why the change? Sending raw sensor data to the cloud not only strains bandwidth but also increases costs. Edge computing solves this by processing data locally and sending only critical summaries or alerts to centralised systems when necessary.
Edge computing significantly reduces latency – by two to ten times compared to cloud-based 5G. While cloud computing often introduces delays of 30–60 milliseconds, edge processing can achieve response times as low as 5–10 milliseconds. According to the 3GPP standards body, this reduced latency is one of edge computing’s standout benefits.
For applications that demand split-second reactions – like autonomous vehicles using V2X communication or robotic arms on production lines – this speed is crucial. To put it in perspective, human perception for tasks like facial recognition takes 370–620 milliseconds. Edge computing enables machines to respond much faster than human reflexes.
But it’s not just about speed. Edge computing also boosts operational reliability. Local edge nodes can keep systems running even during connectivity disruptions, ensuring uninterrupted operations if a central server goes offline. These latency and resilience improvements are vital for many industries.
Edge computing is transforming industries by enabling faster, smarter operations. Take manufacturing, for example: autonomous factory robots can coordinate in real time, avoiding collisions and adjusting paths instantly – no need to wait for instructions from a remote data centre.
Predictive maintenance is another game-changer. Sensors gather data like vibrations, temperatures, and sound patterns. Edge nodes analyse this data on-site, spotting anomalies that hint at potential equipment failures. Maintenance teams get alerts within milliseconds, helping them prevent costly downtime.
In logistics, edge computing powers real-time monitoring systems across warehouses and distribution centres. Machine vision cameras process images locally to verify package contents, detect damage, and guide automated sorting systems. This reduces network congestion while ensuring the fast processing speeds modern supply chains demand.
The rise of Edge AI takes things a step further. By deploying machine learning models directly on edge devices, businesses can make complex decisions without relying on the cloud. For example, smart grids use Edge AI for real-time energy management, while remote robotic surgery systems process patient data locally, ensuring critical operations remain uninterrupted. These examples highlight how edge computing’s low latency and localised processing enhance efficiency across industrial 5G applications.
Cloud computing relies on centralising data processing in data centres, often located over 1,000 kilometres away. This setup introduces predictable delays as data must pass through multiple network layers – radio interfaces, switches, routers, firewalls, and the public internet – before reaching its destination. As Dongwook Kim from 3GPP MCC puts it:
"Telecommunications is not an exception and, despite the continued efforts to enhance performance, there always is limit to where latency can be reduced (i.e. the theoretical minimum is the total length of distance divided by the speed of light)".
This unavoidable delay caused by physical distance highlights the performance constraints inherent to cloud computing.
Cloud computing latency often exceeds 20 milliseconds, with delays potentially reaching over 1,000 milliseconds in unfavourable conditions. Achieving a round-trip time below 10 milliseconds would require data centres to be located within 200 kilometres – something most centralised cloud providers cannot guarantee. While 50% of users might experience median latencies below 54 milliseconds, around 5% face delays exceeding 100 milliseconds due to network jitter and inconsistencies. Additionally, centralised systems come with a significant drawback: a single point of failure. If the central server experiences downtime, the entire system becomes inoperable. These latency figures reveal why cloud computing struggles to meet the high responsiveness required in industrial applications.
In industrial settings, where time-sensitive operations are critical, such delays are simply not viable. For instance, autonomous mobile robots and automated guided vehicles need response times under 20 milliseconds to operate safely, while industrial process control loops often demand latencies of 10 milliseconds or less. Take high-speed manufacturing as an example: production lines can process 60 parts per second. A one-second delay could result in over 30 defective items, and a five-second delay in safety mechanisms could lead to catastrophic equipment damage.
Other scenarios, such as automated port operations and drone monitoring, rely on split-second decisions to avoid collisions and maintain safety. In these cases, cloud latency introduces an unacceptable level of risk. Even applications with higher latency tolerances have their limits. Voice over IP can manage delays up to 150 milliseconds, but technologies like augmented reality and 3D collaboration require bidirectional response times under 50 milliseconds. These challenges highlight the pressing need for architectures designed to handle real-time industrial requirements effectively.
In private 5G networks, latency isn’t just a theoretical concept – it has a tangible impact on industrial operations. Edge computing offers latency between 1–10 milliseconds, while cloud computing typically ranges from 50 milliseconds to over 200 milliseconds. To achieve a round-trip time of less than 10 milliseconds, the data centre must be within 200 kilometres of the device. Edge computing can cut latency by as much as 90% compared to 4G-based centralised systems. Additionally, the 5G Ultra-Reliable Low-Latency Communication (URLLC) standard is designed to deliver end-to-end latency under 1 millisecond with reliability levels reaching 99.999%. As STL Partners explains:
5G increases the speed the data travels at, and edge computing reduces the distance it travels before it is processed. In short, edge enhances the performance of 5G.
This highlights how proximity to the data source plays a critical role in achieving faster processing speeds.
| Application Type | Required Latency (RTT) | Edge Suitability | Cloud Suitability |
|---|---|---|---|
| Process Control Loops | ≤ 10 ms | Mandatory; designed for real-time | Unsuitable; cannot ensure sub-10 ms |
| Autonomous Mobile Robots (AMR/AGV) | < 20 ms | Highly recommended; low response times | Limited; high risk |
| Virtual/Augmented Reality (VR/AR) | < 20 ms to 50 ms | Preferred; avoids backhaul delays | May exceed 50 ms, reducing quality |
| Collaborative Tools | < 50 ms | Suitable; local processing | Acceptable if data centres are nearby |
| Big Data Analytics | > 100 ms | Possible but not essential | Ideal; supports scalability |
The disparity in latency between edge and cloud computing has a profound effect on industrial operations, influencing both safety and efficiency. Edge computing’s ability to process data within milliseconds is critical for applications where even the slightest delay – such as in autonomous vehicle navigation or industrial robotics – could pose serious risks. As Scale Computing notes:
Waiting on a round trip to the cloud is not feasible when the application must respond immediately (think computer vision alerts, automated quality checks, or on-site operational systems).
For environments that demand near-instantaneous stability, edge computing ensures operations can continue seamlessly, even if the connection to the central cloud is disrupted. On the other hand, cloud computing is better suited for tasks that aren’t time-sensitive, such as long-term data analysis, global coordination, and storage.
These performance differences are shaping the way organisations design their systems. A growing number of businesses are adopting hybrid models: edge computing handles time-critical tasks, while the cloud takes care of centralised analytics and long-term storage.
Selecting the best private 5G architecture revolves around factors like latency, data privacy, operational needs, and scalability. The 3GPP standard outlines three connectivity models to cater to different industrial requirements: distributed anchor points (all traffic routed to a local User Plane Function), session breakout (latency-sensitive traffic handled locally while other data is processed centrally), and multiple PDU sessions (separate sessions for local and centralised applications). Each model strikes a different balance between performance, complexity, and cost, offering flexibility to meet varied needs.
For latency-critical applications like industrial robotics, autonomous mobile robots, and virtual reality, edge computing is essential. User-interactive tasks often need round-trip times under 50 milliseconds, while more demanding applications, such as VR, might require responses in under 20 milliseconds. Edge computing can reduce latency by 2–10 times compared to centralised systems. Additionally, keeping sensitive industrial data on-site bolsters privacy and security, lowering the risk of data interception.
On the other hand, tasks that aren’t time-sensitive – such as IoT telemetry, long-term data analysis, or global coordination – are often better suited to centralised cloud processing. Although edge infrastructure can cost around 10% more than centralised setups, the key lies in aligning the architecture with the specific needs of the application. A hybrid model, using session breakout to route critical traffic locally while sending non-critical data to the cloud, can achieve an optimal balance of cost and performance.
Meeting low-latency demands often requires tailored solutions. Firecell offers turnkey private 5G networks designed for industrial environments. Their Pegasus Network supports deployments over 10,000 m² with up to 10 access points, providing a scalable system that integrates smoothly with existing enterprise LANs. For organisations exploring edge computing, Firecell’s Orion Labkit offers an open-source 5G lab network covering areas from 10 m² to 1,000 m². Priced at £10,200 upfront and £4,800 annually, it allows teams to test latency performance and application behaviour in a controlled setting before committing to a full-scale deployment.
Firecell’s solutions come pre-configured for real-time monitoring, guaranteed Quality of Service (QoS), and military-grade security – features crucial for latency-sensitive use cases like autonomous robots or process control systems. Their subscription plan, starting at £85 per 1,000 m² per month (for indoor spaces of 10,000 m² or more), includes installation, maintenance, and management software. This reduces the operational workload on enterprises while ensuring that edge computing resources maintain smooth operation, even during disruptions in connectivity with central servers.
Choosing the right architecture – whether edge or cloud – requires aligning it with the latency demands of your applications. While latency-sensitive tasks typically need response times ranging from under 10 milliseconds to 50 milliseconds, large-scale data analysis and long-term storage are better suited to centralised infrastructure, which doesn’t rely on real-time processing.
Physical constraints, like limited space, power, or network capacity at industrial sites, can also influence whether edge resources are feasible. In such cases, a session breakout approach can offer a middle ground, routing critical traffic locally while sending less time-sensitive data to the cloud. This method ensures efficient use of resources without overloading the on-premises setup. Additionally, compliance and data sovereignty requirements often favour edge computing, as keeping data on-site reduces interception risks and ensures adherence to local regulations. Matching the architecture to the application’s specific needs ensures private 5G networks deliver both performance and efficiency across diverse industrial scenarios.
Edge architectures offer response times as low as 1 ms, compared to the 30–60 ms delays typical of cloud setups. This difference is critical for industrial applications where immediate responses are non-negotiable. For private 5G networks, edge computing can slash latency by a factor of 2 to 10 when compared to centralised models. These figures highlight just how crucial low-latency processing is for specific industrial tasks.
The decision between edge and cloud computing should be guided by the operational needs of private 5G networks. It’s not about choosing one as superior but about aligning the technology with the task at hand. For latency-sensitive operations, edge processing is indispensable. Meanwhile, cloud computing excels in areas like data analytics, long-term storage, and tasks that don’t require split-second responses. This trend mirrors the industry’s increasing preference for distributed architectures.
Dongwook Kim from 3GPP MCC emphasises this point, stating: "The most prominent benefit of edge computing is the reduced latency… it is possible to reduce the latency significantly (factor of 2 to 10) with edge computing". Physical transmission limits, dictated by distance, mean that latency can only be reduced so far. This makes edge computing a necessity for applications where every millisecond counts. That said, hybrid models – blending edge and cloud processing – often provide a balanced solution. These setups allow organisations to optimise both performance and cost across their private 5G deployments.
Understanding your latency requirements is essential. For example, sub-10 ms responses may be critical for robotics, while less time-sensitive tasks, like administrative functions, can tolerate higher latencies. By aligning infrastructure with these specific needs, businesses can ensure their operations remain efficient and safe, reinforcing the earlier point that architecture choices should be driven by precise latency demands.
Edge computing tackles latency issues by handling data locally, right where it’s created. This avoids the delays that can occur when data is sent over long distances to centralised cloud servers. By processing data on-site or nearby, edge computing allows for near real-time responses – a must-have for applications that need split-second decision-making, like autonomous robots or industrial automation.
This method shines in private 5G networks, where low latency is vital for smooth and efficient communication. Combining edge computing with private 5G can lead to faster data handling and better performance in high-demand settings such as manufacturing facilities, logistics operations, and transport hubs.
Edge computing plays a key role in industries that demand ultra-low latency, high bandwidth, and real-time data processing. It’s especially critical in areas like manufacturing automation, autonomous vehicles, robotics, and industrial control systems, where split-second decisions can improve both efficiency and safety.
Take manufacturing, for instance. With edge computing, machinery can be monitored and controlled in real time, cutting down on downtime and increasing productivity. Autonomous vehicles also rely heavily on edge processing to interpret sensor data instantly, enabling safe and responsive navigation. In robotics and industrial control systems, edge computing ensures that operational commands and safety protocols are executed with minimal delay.
Beyond these, edge computing has a strong presence in augmented and virtual reality (AR/VR), enhancing user experiences by processing data rapidly. It’s also essential in fields like healthcare, smart cities, and logistics, where real-time analytics and traffic management are game-changing. By processing data closer to its source, edge computing not only reduces response times but also eases bandwidth demands, making it a cornerstone of modern industry.
Businesses are increasingly adopting a hybrid strategy that combines edge and cloud computing to strike a balance between performance, reliability, and scalability. By processing critical workloads locally at the edge, they can reduce latency and maintain operations even if the connection to the cloud is interrupted. At the same time, the cloud is well-suited for managing less urgent tasks, storing data, and handling large-scale processing needs.
This approach is particularly effective for optimising private 5G networks. It ensures low-latency and high-reliability performance for essential operations while leveraging the cloud’s ability to scale and provide advanced analytics. Industries like manufacturing, logistics, and autonomous systems benefit significantly from this setup, where real-time responsiveness is a top priority.
