As of May 2026, there are 60 SaaS companies in IoT Edge Platforms. They have combined revenues of $360.1M and employ 2.3K people. They have raised $120.8M and serve 150K customers combined.
IoT Edge Platforms refer to a computing architecture designed to enable data processing and analysis closer to the source of data generation, particularly in Internet of Things (IoT) environments. These platforms facilitate real-time data processing, which is vital for applications requiring low latency, such as industrial automation, smart cities, and connected vehicles. By processing data at the edge, organizations can optimize bandwidth usage, reduce latency, and enhance responsiveness in critical applications.
Sorting: Highest -> Lowest
Showing 10 of 7 companies ranked by annual revenue.
San Francisco, California, United States
Armada specializes in solving complex challenges for industries operating in remote locations. By integrating connectivity, advanced compute, and real-world AI, we are pioneering edge computing and AI solutions that stand to change industries across the globe.
Irvine, California, United States
Syntiant is a leader in edge-AI deployments, bringing deep-learning to any device with industry-leading Neural Decision Processors and hardware-agnostic machine learning models.
Wolfsburg, Lower Saxony, Germany
Developer of optical AI infrastructure sensor system designed to analyze the work environment in real time and warn employees of impending collisions.
Ann Arbor, Michigan, United States
MemryX Inc. is a fabless semiconductor company pioneering AI processing solutions for edge devices. Powered by proprietary compute-at-memory technology, MemryX accelerators combine high performance and low power to enable efficient, scalable AI for industries including IoT, transportation, and industrial automation.
Richmond, Victoria, Australia
meldCX’s mission is to empower businesses and individuals to create premier customer experiences through AI and intelligent edge technologies. Market leaders need applications that make customer and employee experiences more interesting and fulfilling — meldCX technology delivers the building blocks for their success. Our PaaS solution, the meldCX Core Platform, is an enterprise platform that allows organizations to build or re-use, deploy, and manage commercial applications for commercial devices. Our SaaS solution, Viana™ (short for Vision Analytics) allows you to consume machine vision, artificial intelligence data without the need for developers. Anonymously measure how people work, shop, learn and play in physical and digital spaces. Tell meaningful data stories about your customers, and empower your teams to make insightful and data-driven business decisions. meldCX is headquartered in Melbourne, Australia, with teams in Sydney, Philippines, Bangladesh, USA, and UK (EMEA).
Campbell, California, United States
Developer of data processing and edge analytics software designed to deliver powerful real-time business and operational insights. The company's software uses machine learning and edge computing to process streaming data as it is generated in real-time at the edge without needing a message broker or central databases. Software combines and analyses data sources, builds a digital twin model or each source and finds critical events, anomalies and correlations in order to reduce data volumes, enabling businesses and governments to reduce, analyze, learn and predict from 'gray' edge data.
San Francisco, California, United States
Neeve is an edge cloud platform transforming smart buildings and spaces, making them more secure, smarter, and more sustainable. It provides cybersecurity, secure remote and cloud access, edge compute, applications, and cloud management for intelligent buildings.
- Must enable real-time data processing at or near the source of data generation. - Should support a range of IoT devices and protocols for seamless integration. - Must provide analytics capabilities to generate insights from collected data. - Should include features for remote device management and monitoring. - Not just basic connectivity; must also enable sophisticated data processing and management workflows. - Must ensure security and privacy of data both in transit and at rest. - Should have scalability to accommodate growing numbers of IoT devices and data traffic.
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