What is edge computing?
Edge computing brings processing capabilities closer to the end-user or the source of data. In effect, this means having less computation and storage in the cloud, and instead moving to local places, such as an edge server. Read our overview of edge computing here.
Why focus on edge computing now?
New technologies and demand for new applications means the time is ripe for edge. Consumers want low latency for content-driven experiences and enterprises require local processing for security and resilient operations. If you want to learn more about where edge computing is headed, we cover the future of edge computing here.
What are the major edge computing use cases?
There are many use cases which involve edge computing, from virtualised RAN to cloud gaming. We explain 10 examples of edge computing in more detail.
What companies are working in edge computing?
Many companies, big and small, are looking to play within this space. We compiled a list of edge computing companies to look out for in the next couple of years.
How does 5G and edge computing?
5G needs edge computing for two reasons: 1) 5G will rely on edge computing to meet latency requirements of 5G applications 2) edge computing will help cultivate an ecosystem of applications that also need 5G. Find out more about edge computing and 5G here.
How does IoT relate to edge computing?
IoT applications will need edge computing for the benefits it gives around latency, bandwidth and security. Learn more about IoT and edge computing.
How STL Partners can help you
Those who want to add value in this space need to understand the business models, the use cases and define a clear go-to-market plan, including operators who can use edge computing to grow revenues outside connectivity.
Are you looking for advisory services in edge computing?
Read more about edge computing
Edge computing market overview
This 33-page document will provide you with a summary of our insights from our edge computing research and consulting work:
From cloud AI to hybrid AI: The rise of model cascading
The next phase of enterprise AI is likely to be more distributed than the first. Rather than routing every workload to a large cloud-hosted model, organisations are increasingly combining on-device, edge and cloud-based AI models in cascaded architectures. This article examines the drivers behind model cascading, its role in enabling hybrid AI and the ecosystem challenges that must be overcome for it to scale.
Inside the National Edge AI Hub
This article is based on a February 2026 discussion with Professor Rajiv Ranjan, Director of the National Edge AI Hub. It explores the Hub’s origins and ambitions, looking at how its network of university and industry partners ia supporting organisations deploy real-time AI in the physical world.
Edge computing at MWC 2026
In contrast to previous years, when edge computing had drifted somewhat to the periphery of the agenda, it was more prominent at MWC 2026, with discussions and demonstrations across the Fira reinfo…