Fog Computing Market Size - By Component, By Deployment Model, By Application, Growth Forecast, 2025 - 2034

Fog Computing Market Size

The global fog computing market size was valued at USD 346.8 million in 2024 and is expected to grow at a compound annual growth rate (CAGR) of 16.2% between 2025 and 2034. Fog computing is becoming more and more popular as the need for low-latency communication, real-time processing, and decentralized data management grows, especially in sectors like manufacturing, automotive, healthcare, and smart cities.
 

In contrast to conventional cloud computing, fog computing helps businesses save bandwidth and speed up response times by bringing data storage, processing, and analytics closer to the point of data generation. Faster decision-making, improved security, and support for mission-critical applications where latency is an issue are all made possible by this proximity. With more IoT devices and edge systems being used, fog computing is becoming a key technology for smooth, scalable, and smart operations. As businesses focus on digital transformation and updating their infrastructure, fog computing is set to play a major role in providing local, responsive computing power while connecting edge and cloud systems.
 

The momentum behind fog computing is intensifying as industries ranging from manufacturing and smart cities to telecom and healthcare seek ultra-low latency, secure, and decentralized data processing. Fog computing, in contrast to conventional cloud models, places analytics and computation near the location where data is generated, lowering bandwidth requirements, improving responsiveness, and protecting data through localized processing. For mission-critical applications, where even a few milliseconds of delay can affect user experience, performance, or safety, this decentralized approach has become essential.
 

For instance, In April 2025, Cisco introduced an enhanced version of its “Edge Fog Fabric” platform, incorporating AIdriven analytics specifically designed for edge environments. This update solves major latency and bandwidth issues that industries implementing fog architectures face by enabling real-time processing and automated decision-making right at the network edge.
 

Fog computing is becoming more and more popular in industries with crucial latency-sensitive operations, like utilities, industrial automation, and autonomous systems, particularly when AI is integrated into edge applications. In situations where quick decisions are crucial, the incorporation of fog nodes with local AI capabilities enables instantaneous inference and control.
 

For instance, In March 2024, IBM unveiled a new fog computing platform tailored for industrial settings, integrating on-site AI model training and inference for realtime equipment monitoring. This implementation facilitated predictive maintenance, allowing equipment to foresee problems and initiate repairs on its own, reducing downtime and operating costs and highlighting fog's revolutionary potential in intelligent operations.
 

Fog Computing Market Trends

• In industrial automation, fog computing is rapidly gaining traction as businesses look to process data closer to the source for in-the-moment decision-making. Manufacturers can improve operational efficiency and decrease latency by positioning computing resources at the edge of the network.
   
• For instance, in April 2025, Cisco Systems collaborated with Siemens Digital Industries to deploy fog-based edge nodes within manufacturing environments. By combining Siemens' SIMATIC automation systems with Cisco's IOx fog application environment, the collaborative solution allows for local decision-making and real-time analytics without heavily depending on cloud processing.
   
• Fog computing is being used as a basis for localized AI applications like energy optimization, traffic control, and surveillance as a result of the growth of smart city initiatives. Real-time responses are made possible by fog nodes, which also lessen the bandwidth demand on central servers.
   
• For instance, in March 2025, Dell Technologies announced its Edge for Smart Cities platform, which leverages fog computing to process AI workloads at city-level edge devices.  To improve energy grid efficiency and urban mobility, the platform was introduced in partnership with the city of Barcelona.
   
• Energy management systems must manage distributed and intermittent sources like solar panels, wind turbines, and microgrids as the world moves toward renewable energy. The use of fog computing to carry out demand-response, fault-detection, and real-time load balancing nearer the energy source is growing. This lessens dependency on centralized data centers, increases energy efficiency, and guarantees more robust grid operations.
   
• For instance, in February 2025, ABB introduced a fog-enabled upgrade to its ABB Ability Energy Management System. The system, which was implemented in cooperation with Korea Electric Power Corporation (KEPCO), made it possible to optimize energy flows from solar and battery storage units across a hybrid grid in real time. Fault isolation and load distribution were managed by local edge nodes, which improved grid uptime and energy throughput by 15% during periods of peak demand.
   
• In the healthcare industry, latency and data privacy are critical. Hospitals and clinics can process patient data closer to the point of care with fog computing, including vital signs, diagnostic images, and ECGs. This facilitates quicker clinical decision-making, improves patient safety, and supports telehealth applications in situations where there may be sporadic or delayed connectivity to central data centers. Additionally, local data processing aids hospitals in adhering to privacy laws like GDPR and HIPAA.
   
• For instance, in December 2024, Philips upgraded its IntelliVue Guardian real-time patient monitoring system with fog computing capabilities. The system used localized edge servers placed in hospital wards to process patient vitals from bedside monitors. Because of this, alerts for patient deterioration were produced in milliseconds, allowing for prompt interventions and bettering results in critical care units throughout the Netherlands' pilot hospitals.
   

Fog Computing Market Analysis

Based on deployment model, the fog computing market is segmented into private fog node, community fog node, public fog node, and hybrid fog node. In 2024, the private fog node segment dominated the market, accounting for around 42% share and is expected to grow at a CAGR of over 17.2% during the forecast period.
 

• Private fog nodes continue to lead the fog computing deployment landscape due to enhanced control, security, and customizability. These nodes are specialized infrastructures that are set up inside the boundaries of proprietary networks or on the property of individual businesses. Because private fog models allow for localized processing with complete control over infrastructure and data flow, they are preferred by businesses with crucial data sovereignty requirements, such as those in the manufacturing, healthcare, defense, and energy sectors. In addition to ensuring data integrity, this autonomy guarantees regulatory compliance in extremely sensitive environments.
   
• Private fog architectures are appropriate for complex use cases that require reliable performance, even under heavy operating loads, due to their scalability and configurability. Private nodes, as opposed to public or shared fog networks, provide guaranteed bandwidth, prioritized data access, and predictable latency—features that are essential for automated control systems, mission-critical edge AI applications, and real-time analytics. Their market adoption is greatly increased by the capacity to customize processing, storage, and security protocols to organizational needs, especially in digitally mature enterprises.
   
• In sectors like industrial automation and smart energy, where edge-to-core integration is fundamental, private fog nodes facilitate seamless coordination between operational technology (OT) and IT systems. While keeping a close relationship with centralized cloud analytics, they enable industrial players to process control signals, machine telemetry, and predictive insights close to the edge. This architecture enhances system resilience and lowers decision-making latency, particularly in settings with sporadic cloud access or unreliable networks.
   
• Another important factor contributing to the preference for private fog deployments is cybersecurity. Businesses are using private fog nodes to create firewall-protected, hardened environments where data never leaves the organizational boundary unless specifically programmed to do so in response to the growing threat of cyberattacks at the edge. For industries handling patient data, classified operations, or intellectual property, this localized security posture reduces susceptibility to external attacks and supports zero-trust architectures. Additionally, in compliance with data protection regulations like GDPR and HIPAA, private deployments provide greater transparency and auditability.
   
• For instance, in October 2024, Cisco Systems deployed a series of private fog nodes for a leading pharmaceutical company as part of their secure industrial IoT infrastructure. The implementation allowed for real-time monitoring and process automation throughout the company's North American manufacturing facilities by integrating Cisco's IOx edge compute platform with its internal data fabric.
   
• Through this project, the company was able to maximize efficiency and adhere to stringent regulatory compliance while keeping all sensitive production data within its network. Showcasing the useful advantages of safe, localized fog infrastructure, the private fog node configuration greatly decreased latency for AI inference and enabled remote diagnostics and system updates.
   

Based on components, the fog computing market is segmented into Hardware, and Software. In 2024, the Software segment dominated the market with a market share of 69%.
 

• Advanced orchestration, virtualization, and data analytics software are becoming more and more necessary as fog computing transforms from a specialized framework to a crucial part of edge-driven digital transformation. Businesses looking for operational intelligence at the network edge depend on fog software platforms because they make it possible to distribute, deploy, and manage applications across heterogeneous edge devices with ease.
   
• Software solutions are essential for enabling key fog computing functionalities such as resource provisioning, service orchestration, security management, and real-time analytics. These platforms enable developers to dynamically modify workloads according to latency, bandwidth, or processing constraints and run containerized applications across decentralized fog nodes. Additionally, real-time inference nearer data sources is made possible by integration with AI/ML libraries, which is essential for mission-critical applications in industries like manufacturing, smart cities, healthcare, and transportation.
   
• One of the defining strengths of fog software is its ability to support cross-platform interoperability and centralized control across a distributed ecosystem. Fog platforms offer a centralized interface for monitoring, deploying, and updating edge applications, whether they are used to manage distant assets in energy grids or to enable autonomous operations in industrial plants. This is especially important in hybrid environments, where cloud systems and on-premise fog nodes must cooperate to provide seamless service delivery and decision-making.
   
• The role of software has grown even more critical with the rise of software-defined infrastructure and AI at the edge. To simulate and optimize physical processes in real time, fog software frameworks now include digital twin modeling, machine learning algorithms, and container orchestration engines (like Kubernetes). Furthermore, continuous improvement, security patching, and system recalibration are supported by the capability of fog nodes to deliver over-the-air (OTA) updates, guaranteeing long-term flexibility, performance, and resilience for businesses in a variety of industries.
   
• For instance, in September 2024, Dell Technologies announced the expansion of its Project Frontier software platform to support fog-enabled edge orchestration across industrial and retail sectors.  Real-time data analytics, automated failover, and edge application lifecycle management were made possible by this platform's centralized visibility and control over dispersed edge nodes.
   
• A multinational industrial automation company used the software to improve uptime and operational responsiveness while lowering system latency by more than 30% through smooth coordination between IoT devices and local processing units. Dell underlined that scalable and agile fog deployments catered to business requirements were made possible by the software's modular, secure, and vendor-agnostic architecture.
   

Based on applications, the fog computing market is segmented into security, intelligent energy, smart manufacturing, traffic & logistics, connected health, building & home automation, and others. In 2024, the smart manufacturing segment is expected to dominate due to the increasing demand for intelligent, sensor-driven, and compliant safety technologies.
 

• Smart Manufacturing emerged as the leading application segment in the fog computing market, driven by the global shift toward Industry 4.0 and the growing need for real-time, sensor-driven automation. Because fog computing bridges the gap between information technology (IT) and operational technology (OT), it is essential in contemporary manufacturing environments. By enabling on-site data processing near machinery, it lowers latency and permits real-time control of robotics, safety systems, and production lines. The capacity to compute at the edge becomes essential as manufacturers aim for increased throughput, quality control, and predictive maintenance.
   
• IoT sensors abound in contemporary manufacturing facilities, monitoring everything from temperature and vibration of machines to safety and product quality indicators. Every second, these sensors produce enormous volumes of data. To analyze all of this data, relying only on clouds may cause network bottlenecks and slow down operations. By enabling real-time data analysis and action by local systems, fog computing provides a workable solution. Having processing power at the edge streamlines and improves operations, whether it is identifying a malfunctioning component, modifying a robotic arm, or optimizing energy consumption.
   
• Additionally, fog computing is assisting manufacturers in adopting intelligent, self-optimizing systems rather than just automating processes. Fog nodes can identify trends, anticipate malfunctions, and automatically modify machine behavior thanks to their integrated analytics and artificial intelligence capabilities. Nowadays, digital twins- virtual representations of actual machines that operate in real time- are powered by fog computing in many factories. Without interfering with the real production line, these digital replicas assist operators in testing scenarios, preventing malfunctions, and enhancing performance. The outcome is- More flexible manufacturing procedures, reduced maintenance expenses, and fewer mistakes.
   
• Security and compliance are just as important as speed and intelligence. Manufacturers are facing increasing cybersecurity risks as more devices are connected than ever before. By processing sensitive data locally and reducing its susceptibility to attacks during transmission, fog computing improves security. Additionally, it facilitates adherence to stringent industry and data privacy regulations. Maintaining control over the storage and processing of data lowers risks and improves accountability, regardless of whether a factory is making pharmaceuticals or auto parts.
   
• For instance, In July 2024, Siemens Digital Industries launched their Industrial Edge fog platform at a state-of-the-art Italian auto component facility. The objective was to enable the plant to react more quickly to real-time production data, such as whether a part is defective or how a conveyor belt is operating. They reduced downtime by 20% by using fog-based edge analytics to identify problems immediately and make necessary process adjustments. To maximize energy use, particularly during times of high demand, they also employed AI at the edge.
   

In 2024, the U.S. region dominated the fog computing market with around 67% market share and generated around USD 114.2 million in revenue.
 

• The U.S. emerged as the leading region in the global fog computing market, driven by its advanced digital infrastructure, early technology adoption, and strong demand for low-latency, localized data processing. Fog computing is now a crucial component of the U.S. digital transformation strategy as businesses in key industries move toward edge computing models. Fog computing has established itself as a workable way to address real-time operational demands because it can process data closer to where it is generated, whether that be at a traffic intersection, a hospital, or a factory floor.
   
• The U.S. is home to many of the world’s most influential technology companies, which are actively shaping and scaling fog computing solutions. Leading companies in the sector, including Cisco, HPE, Dell Technologies, and Intel, are creating platforms that connect cloud infrastructure and Internet of Things devices. Their inventions enable companies to improve system responsiveness, automate procedures, and conduct edge analytics without fully relying on centralized data centers. Fog computing is still being pushed into the mainstream of business operations by this innovative ecosystem, which is backed by robust venture capital and public-private partnerships.
   
• The smart manufacturing industry in the U.S. is one of the biggest adopters, as fog computing facilitates supply chain optimization, predictive maintenance, and real-time machine control. Fog-enabled systems are being implemented by industrial facilities in Texas, Ohio, and Michigan to increase the resilience and agility of production. In an increasingly automated and competitive industrial environment, these systems give manufacturers the ability to detect flaws earlier, minimize downtime, and quickly adjust to changing inputs.
   
• Fog computing is also gaining traction in sectors where security, compliance, and uptime are mission-critical. Fog in healthcare enables local processing of sensitive patient data, guaranteeing HIPAA compliance while preserving responsiveness in real time. Localized computing improves system resilience and lowers vulnerability to cyberattacks in defense and critical infrastructure. To facilitate real-time decision-making and lessen reliance on cloud computing, smart city projects in several U.S. states are incorporating fog nodes into traffic, environmental, and safety systems.
   
• For instance, in October 2024, Hewlett Packard Enterprise (HPE), for instance, installed its Edgeline EL8000 Converged Edge System at a significant aerospace manufacturing facility in the United States. To process data from high-precision sensors in real time without transferring it to the cloud, the fog-enabled platform was deployed throughout the production lines. This facilitated quicker corrective action, decreased part inspection times, and enhanced quality control. This deployment demonstrated how American manufacturers are using fog computing to increase visibility, speed, and control over critical edge operations, according to HPE.
   

The fog computing market in Germany region is expected to experience significant and promising growth from 2025 to 2034.
 

• Germany is poised to experience significant and promising growth in the fog computing market from 2025 through 2034, fueled by its strong industrial base, commitment to digital innovation, and leadership in Industry 4.0 initiatives. Germany, one of the world's most developed manufacturing economies, keeps giving top priority to technologies that improve factory automation, machine connectivity, and real-time operations.
   
• Fog computing makes sense because it allows for localized processing at the network edge, where operational efficiency, data privacy, and real-time responsiveness are crucial. In the upcoming ten years, fog deployments are anticipated to flourish due to the nation's proactive approach to smart factories and cyber-physical systems.
   
• The growth in Germany is also supported by the deep integration of industrial IoT (IIoT) technologies across sectors such as automotive, machinery, logistics, and energy. Sensors, robotics, and AI-powered systems that depend on quick, decentralized computing are being actively implemented by German manufacturers. By reducing latency and facilitating more accurate control over time-sensitive processes, fog architecture enables data to be processed directly at the machinery or on the production line. For the nation's highly engineered manufacturing environments, these edge-to-fog configurations are critical for energy optimization, predictive maintenance, and quality assurance.
   
• Germany’s focus on data sovereignty and compliance also makes fog computing an attractive solution. Businesses must have localized processing environments that reduce their dependency on external cloud services and comply with stringent European data privacy regulations like the GDPR. When placed in business buildings, fog nodes provide real-time control while guaranteeing that private customer and operational information stays within the bounds of the law. This is especially important in industries where data ownership, security, and traceability are essential to operations and customer trust, such as healthcare, automotive, and aerospace.
   
• Germany’s ecosystem of global industrial technology leaders and specialized SMEs is another major driver. Companies like Festo, Siemens, and Bosch, as well as cutting-edge software developers, are already providing fog-capable platforms that combine cybersecurity, artificial intelligence, and system interoperability. These businesses, which are well-known for their modular, scalable industrial systems, are now integrating fog-based intelligence into machine controllers, control panels, and industrial gateways. Universities and applied research centers, like Fraunhofer and RWTH Aachen, are also actively collaborating with industry to speed up fog innovation and pilot projects in the fields of smart manufacturing and mobility.
   
• For instance, in March 2025, Bosch Rexroth declared that a fog-enabled version of its ctrlX AUTOMATION platform would be implemented at a few southern German production facilities. With the addition of edge controllers and fog nodes, shop-floor devices could now process and exchange data instantly without relying on the cloud. This made it possible for predictive maintenance algorithms to operate directly on local devices, improved system responsiveness, and decreased latency in robotic operations.
   
• Bosch emphasized that the fog-based architecture marked a major advancement in Germany's digital manufacturing capabilities by supporting both GDPR compliance and integration with AI analytics for more intelligent production decisions.
   

The fog computing market in the China region in Europe is expected to experience significant and promising growth from 2025 to 2034.
 

• China is expected to experience substantial growth in the global fog computing market, backed by its strong industrial base, expansive IoT ecosystem, and national focus on digital infrastructure. Fog computing is becoming a key enabler as the country advances its "New Infrastructure" strategy, which encourages the adoption of AI, smart cities, 5G, and industrial automation. China needs low-latency, real-time applications, especially in high-density urban areas, intelligent manufacturing hubs, and next-generation transportation networks. Fog computing meets these needs by facilitating faster, localized data processing.
   
• The quick adoption of edge-powered automation and smart factories in China's manufacturing-heavy economy is a major growth driver. Sensors and machine vision systems are being implemented in Chinese factories that produce consumer goods, electronics, and automobiles. These systems need adaptive control and real-time analysis. Fog computing is becoming more popular because cloud-only solutions have trouble with latency and bandwidth in these dynamic environments. Without depending on distant data centers, fog nodes placed at production lines enable manufacturers to instantly optimize energy consumption, control robotic processes, and identify flaws.
   
• China’s ambition in AI and 5G integration also reinforces the need for fog computing. Fog computing acts as a link between edge devices and centralized AI models as 5G networks are rapidly being deployed and edge AI chips are being widely used in urban services, transportation, and surveillance. Fog nodes make it possible for data to be processed safely, quickly, and near the point of interaction- essential for public safety and effective urban management- whether it is used to power connected car systems, manage traffic flows in smart cities, or enable real-time face recognition at transit gates.
   
• China's fog computing momentum is further accelerated by government support. Industrial digitalization and edge-intelligent technologies are given priority in national initiatives like Made in China 2025 and Digital China. Additionally, pilot projects surrounding fog-enabled industrial parks, logistics hubs, and medical campuses are being funded by local governments in provinces like Guangdong, Jiangsu, and Zhejiang. Major technology providers like Huawei, Alibaba Cloud, ZTE, and Inspur are present to guarantee that the software, infrastructure, and R&D required for fog computing are being developed at a large scale and domestically.
   
• For instance, in August 2024, Huawei declared in August 2024 that its EdgeStation fog computing platform would be installed at a smart port facility in Ningbo, China. Real-time video analytics, autonomous vehicle coordination, and crane operation monitoring were made possible by the solution, which processed large amounts of data locally without relying on the central cloud. Huawei emphasized that EdgeStation enhanced on-site decision-making speed, decreased latency by more than 40%, and seamlessly integrated with the port's AI-based logistics system. China's increasing reliance on fog computing to update infrastructure and enable large-scale, real-time, AI-driven operations is reflected in this deployment.
   

Fog Computing Market Share

• Top 7 companies of the fog computing industry are GE Digital, Cisco Systems Inc., IBM Corporation, Intel Corporation, Dell Technologies Inc., Fujitsu Limited, and Schneider Electric SE. around 48% of the market in 2024.
   
• GE Digital's Predix Edge technologies enable its industrial clients to conduct analytics and asset monitoring directly on-site, even in low-connectivity settings. Predix Edge reduces latency and bandwidth consumption by processing data close to the originating assets, like grid infrastructure or turbines, and connects field operations with cloud services for comprehensive asset management. Fog computing is essential to industrial companies' digital transformation strategies because of GE's approach, which enables them to make decisions in near real-time, whether they are modifying turbine output or carrying out predictive maintenance.
   
• Cisco's IOx platform, which runs apps on network routers and gateways, was one of the first to introduce fog computing into the common IT language.  For crucial use cases like utility substation control or smart city infrastructure, IOx enables embedded Linux-based automation at the network edge. Cisco reaffirms its fundamental role in fog architectures by integrating compute capabilities into current network hardware, allowing industries to deploy distributed intelligence without sacrificing security or connectivity.
   
• IBM’s Edge Application Manager, built on Red Hat OpenShift, provides autonomous orchestration of AI, analytics, and application workloads across distributed fog nodes. Clients can consistently deploy complex workloads at scale thanks to the platform's support for secure, multi-tenant management of remote edge devices. It is extensively used in telecommunication, retail, and manufacturing use cases, allowing businesses to preserve operational flexibility and compliance in hybrid cloud-fog systems.
   
• Intel’s OpenVINO toolkit is a pillar of AI efficiency at the fog layer. By optimizing deep learning models for Intel CPUs, GPUs, and NPUs, OpenVINO helps developers significantly increase inference speed and lower power consumption. For fog deployments in smart manufacturing, healthcare, and surveillance, where models must operate consistently on dispersed endpoints with low latency and optimal performance, these optimizations are crucial.
   
• Dell’s Project Frontier delivers a unified edge operations platform designed to simplify deployment, management, and orchestration of workloads across fog and edge environments. Frontier is a good option for businesses handling hundreds or thousands of fog nodes in industrial and retail settings because it offers zero-touch provisioning, security through zero-trust architectures, and hybrid-cloud integration. It is also available alongside hardware options like the PowerEdge XR series.
   
• Fujitsu’s COLMINA suite brings fog computing into the manufacturing floor, combining real-time sensor-driven analysis with cloud-informed dashboards. COLMINA Edge, creates the fog layer by processing telemetry from workers and equipment, while its cloud and service components allow for system-wide insights. This ecosystem, which is based on localized fog architecture, assists manufacturers in identifying inefficiencies, streamlining procedures, and providing connected services.
   
• Schneider’s EcoStruxure architecture integrates micro data centers and edge infrastructure into an intelligent energy and control framework. Their new 6U wall-mount EcoStruxure micro-data center allows for secure, robust deployments that are perfect for commercial edge and industrial settings. Schneider makes sure fog computing is in line with operational resilience and sustainability goals by combining it with AI support for predictive energy and UPS health. This makes fog computing essential for smart grid, factory, and building deployments.
   

Fog Computing Market Companies

Major players operating in the fog computing industry are:

• ARM Holdings
• Cisco
• Dell
• FogHorn
• Fujitsu
• GE
• IBM
• Intel
• Microsoft
• Schneider
   

The fog computing market is experiencing rapid expansion as industries increasingly require localized, low-latency data processing to support real-time decision-making. Fog computing serves as the vital link between edge devices and cloud platforms in a variety of applications, including intelligent transportation, connected healthcare, smart manufacturing, and energy management.
 

In addition to improving speed and operational efficiency, its capacity to process, store, and analyze data closer to the source also allays security and bandwidth issues associated with centralized computing models. Driven by the need for increased autonomy, responsiveness, and resilience in mission-critical environments, innovations like containerized fog nodes, context-aware workload distribution, and AI-powered edge orchestration are becoming commonplace. Fog computing is rapidly evolving from an emerging niche to a fundamental component of scalable, software-defined infrastructure as the world's digital transformation picks up speed.
 

Leading companies in the market, such as GE Digital, Cisco, IBM, Intel, Dell, Fujitsu, and Schneider Electric, are making significant investments in the creation of industry-specific solutions and modular fog platforms. To remain responsive to local infrastructure requirements and regulatory environments, these companies are proactively setting up industrial testbeds, regional edge data centers, and cooperative R&D hubs.
 

From energy optimization in North American smart grids to real-time machine control in European factories, their decentralized innovation model enables them to provide customized solutions for customers. These leaders are cultivating an ecosystem where fog computing can support developing applications in 5G, AI, cybersecurity, and sustainability by collaborating closely with telecom providers, OEMs, and government organizations. Their continued emphasis on interoperability, scalability, and AI integration ensures fog computing will play a defining role in shaping the future of digital infrastructure.
 

Fog Computing Industry News

• In May 2025, MediaTek demonstrated new hybrid computing capabilities in Taipei by presenting its integrated edge-to-cloud AI strategy. Their idea of combining on-device generative AI with 5G connectivity underscores a trend toward fog-enabled privacy, quick process cycles, and locally intelligent smart-home systems. MediaTek highlights how fog computing is emerging as a crucial enabler of low-latency AI in homes, businesses, and multimedia settings by showcasing an on-device generative AI gateway and AI Hub platform. 
   
• In February 2025, Veea and Vapor IO announced a strategic partnership offering turnkey edge AI solutions over private 5G networks. Their combined platform, which makes use of Vapor IO's Zero GapTM AI micro data centers and VeeaHub devices, delivers cloud-grade AI inferencing and federated learning straight to business premises. The system, which is intended for smart factories, hospitals, and campuses, guarantees private, fast analytics and tightly controlled data flows without necessitating additional infrastructure expenditures.
   
• In January 2025, NVIDIA and Telit Cinterion partnered to integrate AI inferencing at the edge of Internet of Things devices by fusing NVIDIA's potent GPUs and frameworks with Telit's secure connectivity. In industries like manufacturing, healthcare, and smart city infrastructure, this partnership opens the door for intelligent, fog-enabled IoT endpoints that can analyze data in real time. The partnership highlights fog's function as a conduit between devices and centralized AI by facilitating edge solutions that are smarter, faster, and more secure.
   
• In June 2024, MediaTek revealed at COMPUTEX 2024 that its NeuroPilot SDK now incorporates NVIDIA's TAO Toolkit, facilitating developers' deployment of AI inference models directly on edge devices. This partnership highlights the strategic significance of software-driven edge orchestration by providing smooth model training and deployment workflows, which speeds up the development of fog-powered applications in smart surveillance, industrial automation, and retail.
   
• In January 2024, IBM and American Tower collaborated to launch a hybrid edge-to-fog computing platform that makes use of the distributed tower infrastructure of American Tower. The solution was unveiled with the goal of bringing compute power closer to distant IoT endpoints. This will allow for real-time analytics and improved scalability for applications like smart utilities and industrial IoT. Without requiring central cloud resources, businesses can now directly deploy fog workloads on tower-based edge nodes, promoting quicker decision-making and increased network efficiency.
   

The fog computing market research report includes in-depth coverage of the industry with estimates & forecasts in terms of revenue ($ Mn) from 2021 to 2034, for the following segments:

Market, By Component

• Hardware
  • Gateways
  • Routers & switches
  • IP video cameras
  • Sensors
  • Micro data sensors 
• Software
  • Fog Computing Platform
  • Customized Application Software     

Market, By Deployment Model

• Private fog node
• Community fog node
• Public fog node
• Hybrid fog node                                                                     

Market, By Application

• Security                      
• Intelligent Energy                   
• Smart Manufacturing            
• Traffic & Logistics                   
• Connected Health                  
• Building & Home Automation            
• Others            

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • Russia
  • Nordics
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • ANZ
  • Southeast Asia
• Latin America
  • Brazil
  • Mexico
  • Argentina 
• MEA
  • UAE
  • Saudi Arabia
  • South Africa