Written & Interviewed by : LITEON Editorial Team (Corporate Brand Value Development Center)
Technical review :LITEON Center of Core Competence
We need to see LITEON as an integration partner, not a point-product provider, before we compare Edge AI vs Cloud AI. Its key portfolio includes AI surveillance cameras, access control and intercom systems, sensors, AI Box computing, networking infrastructure, and 5G AI-RAN capability. This wider scope is important because real deployments do not typically involve a single device. They need cameras, entry systems, local compute, data transmission, and cloud connection to work as a single coordinated environment. LITEON’s product scope also includes solutions related to networking, surveillance, AIoT, access control, and video intercom which also support this broader infrastructure position.
The AI video analytics market is estimated to increase from USD 6.19 billion in 2026 to USD 17.23 billion by 2031, at a compound annual growth rate of 22.72%. Note that the growth is attributed to increasing demand for intelligent surveillance and monitoring systems.
Global spending on edge computing solutions will reach USD 261 billion in 2025 and will grow to USD 380 billion by 2028, signaling that edge infrastructure is a major enterprise investment. LITEON’s value in Edge AI computing also resides in the way its hardware and software capabilities are linked across the deployment chain.
LITEON is not leaving enterprises, distributors, and system integrators to work with different device, software, network and cloud vendors. But it is integrating internally developed capabilities across a large part of its Edge AI product portfolio, as well as still using selected standardized modules where appropriate. This reduces cross-vendor integration burden, troubleshooting cycles, and responsibility gaps during installation and long term operation. The architecture is also enhanced by its AI-RAN direction, which combines edge inference and network-side intelligence for future AI infrastructure.
Edge AI is the execution of AI algorithms and computation closer to the source of the data – be it cameras, sensors, IoT devices, or local edge systems. This means the first layer of analysis can happen close to the video stream in an AI monitoring system. So, simple detections, event flags, and routine filtering do not need to make a round trip to a remote data center.
The main difference between Edge AI vs Cloud AI is not whether one is better, but where the intelligence is running. Cloud AI sends endpoint data to centralized cloud infrastructure for processing which is useful for complex analytics, retrospective review, centralized model management, and multi-site coordination. But cloud-heavy workflows can tax bandwidth demand, storage growth, and transmission-latency pressure when large video volumes need to be uploaded continuously. The research on edge-based video analytics also points out that high bandwidth consumption and latency can be problems for cloud-based video processing.
In a well-designed Edge AI computing architecture, the local layer carries out a significant part of initial event detection and filtering, and only relevant events, abnormal footage, or selected data move upward for deeper cloud analysis. For example, a camera or edge node might first evaluate if there is a person, intrusion, or anomalous event, and the cloud may later support cross-camera comparison, long-term pattern analysis, or broader site-level decisions. This keeps time-sensitive action closer to the scene, while saving cloud resources for large-context tasks.
The most expensive data in cloud-heavy surveillance is often the footage that never becomes an incident, audit record, or investigation lead. Continuous upload can consume network bandwidth and inflate cloud storage requirements, even if most frames have no operational value. Edge-side filtering adds a new dimension to the cost model by filtering normal scenes locally, and only sending selected alerts, clips, or metadata upstream. This is why edge-based video analytics is spoken of as a way to solve high bandwidth consumption in traditional cloud video workflows.
When it comes to intrusion detection, safety monitoring, and building security, an alert is useless if the system waits too long for remote processing. However, Edge AI can facilitate faster local detection by running selected AI video analytics tasks closer to the camera, sensor, or local node. Remember, that does not mean zero delay or a promise of being immune from every network hiccup, but it does mean that site-level functions can proceed with less reliance on cloud round trips when the local architecture is designed for it.
Initial steps can be onerous for centralized AI processing:
The IEA projects that global data center electricity use will double to some 945 TWh by 2030. That is why organizations are seeking more distributed means of handling AI workloads.
However, with a distributed edge model, select workloads are distributed to the cameras, AI Box nodes, gateways, or local systems. This enables organizations to begin with a controlled site, floor, store, or building zone before a broader roll-out. For LITEON customers this supports a phased path where entry level and higher end edge device options can be matched to real site requirements rather than pushing every deployment into the same server centered design. LITEON’s portfolio, which covers surveillance, access control, smart hub, networking, as well as 5G-related solution areas, also supports this broader deployment flexibility.
Gartner predicts that 50% of critical enterprise applications will be outside of centralized public cloud locations by 2027, which proves the need for hybrid infrastructure strategies.
We start with an edge layer in a hybrid model and we put time sensitive surveillance functions there. Tasks such as intrusion detection, presence detection, initial object classification, event triggers, and local alerts are best placed close to the cameras or sensors as they depend on fast scene level interpretation. This direction is supported by research on edge-based video analytics, where edge architectures perform video processing closer to the source to meet real-time requirements that can be problematic for traditional cloud-only solutions.
You still want cloud AI when the job needs more compute, shared datasets, or centralized coordination. Cloud infrastructure suits cross-camera search, multi-site analysis, large-scale video comparison, historical pattern review, and centralized model control, as they need a wider perspective than what can be obtained from a single camera, floor, or building zone. Recent work on video analytics at the cloud edge describes hybrid systems that combine edge-centric and cloud-centric techniques through adaptive task offloading and resource-aware scheduling.
A video object-detection study showed that combined edge-and-cloud predictions performed up to 50% better than edge-only and cloud-only approaches. This data lends credence to the idea that the most robust architecture is often collaborative, not just local or just cloud.
The next step is deciding which AI task deserves computing resources first.
LITEON’s AI Box architecture can back this modular task-offloading model, prioritizing time-critical AI events and scheduling lower-urgency workloads according to device capacity. This helps to balance responsiveness, system load, and operating cost. Such an edge-to-cloud logic is also reflected in LITEON’s broader AI-RAN direction, which distributes AI workloads between user equipment and edge or cloud environments based on network conditions.
For many SMEs, the first barrier to AI adoption is the idea of starting with a big centralized AI server, new cameras, and a complete overhaul of the surveillance stack. A lower-barrier path is to introduce an AI Box as a local Edge AI computing and management node that can add analytics around existing camera and storage environments. This kind of approach allows teams to test use cases in a controlled environment before a broader migration, which is particularly useful for businesses that require tangible operational benefits before increasing investment.
Almost all real sites are created on multiple vendor ecosystems and multiple generations of hardware. LITEON’s AI Box supports flexible multi-brand compatibility and multi-protocol integration needs in existing surveillance environments so that customers can add AI capability without an immediate full replacement cycle. This is ideal for stores, offices, or individual floors with several camera channels, when organisations need practical AI video analytics without the cost and complexity of a server-based roll-out. LITEON’s portfolio also extends to video surveillance, building surveillance cameras, access control, smart hubs, alarm kits, wireless LAN, wired switches, and 5G total solution areas to support this broader deployment context.
A phased Edge AI deployment also depends on matching compute capability with the actual site, not over-provisioning every location from day one. LITEON can support device options from entry level to higher end configurations, allowing customers to match performance, budget, and analytics scope to each environment. This site-by-site approach can be linked to broader networking and edge-to-cloud infrastructure as adoption increases. LITEON’s AI-RAN also emphasizes scalable AI-native RAN architectures, lower integration complexity, faster deployment, and edge inference within a common RAN and AI workload model.
The next stage of edge-to-cloud architecture does not just incorporate the components of smarter cameras or local computing nodes. However, it will also include coordinating the placement of AI tasks among devices, network infrastructure, and cloud resources.
This trend is supported by the AI-RAN materials that LITEON has developed. These materials highlight scalable AI-native RAN, validated compatibility, reduced complexity of integration, and edge inference through the integration of AI and RAN workloads.
The actual value is that, as deployments grow, there will be fewer disconnected handoffs between device suppliers, network teams, and cloud platform owners. LITEON’s Device + Networking + Cloud capability enables enterprises and system integrators to build a more coordinated architecture between edge devices, 5G AI-RAN, cloud connectivity, as well as API integration. Furthermore, LITEON also describes end-to-end AI capability from cloud to edge, which supports this broader integration message.
AI surveillance systems can still produce false alarms in real-world scenarios with fluctuating lighting, shadows, weather, reflections, and complicated human movement. The realistic goal is not a perfect detection claim but to improve model behavior over time with historical data, AI optimization, and better event validation. False alarm reduction is also a continuing technical challenge in research on video-surveillance change detection, not a solved issue.
For distributed edge deployments OTA update capability is important as devices may be installed across many rooms, floors, branches, or sites. Over time, edge devices may be more capable of assessing local operating conditions and contributing to update decisions, thus reducing manual intervention, but still requiring controlled release, validation, and rollback processes. Remember, remote updates are helpful to add features and fix bugs or vulnerabilities, but they need to consider device constraints, scale, heterogeneity, reliability, and security.
By keeping selected video data, metadata, and decisions at the edge, unnecessary cloud exposure can be reduced and data privacy goals can be supported. That is particularly true when the footage contains sensitive people, places, or business activity. But edge processing alone does not remove cybersecurity risk. Access control, data governance, network protection, software update discipline, and security architecture still need to be in place.
The Edge AI vs Cloud AI right strategy is to place each task where it delivers the strongest operational value. Edge AI computing allows surveillance and smart-building teams to cut down on unnecessary video transport, improve local alert timing, support a more privacy-conscious approach to data handling, and scale adoption through a flexible hybrid architecture. Meanwhile, cloud platforms are still important for broader analytics and centralized coordination.
LITEON describes its AI capability from cloud to edge, covering edge computing, 5G, AI-RAN, and smart-city infrastructure. It offers value for enterprises, distributors, and system integrators with AI Box upgrades for existing environments, flexible integration needs for multi-brand equipment and open protocol, coordination of Device + Networking + Cloud, hardware-software capability, less troubleshooting, and a future-ready path for scalable Edge AI deployment.