What is edge computing?

The emergence of AI at the edge

Artificial intelligence (AI) at the edge is transforming how organizations operate. While model training predominantly occurs in the data center, test-time inference is increasingly shifting to the edge, making it the new frontier for enterprise AI. This enables predictive analytics and automation across a broad range of verticals and use cases—ranging from personalized customer recommendations to fully automated systems. AI at the edge allows organizations to act proactively on evolving business needs.

Edge computing use cases

Edge infrastructure powers a wide range of real-time, data-intensive applications across industries:

• Manufacturing and industrial IoT: Enables real-time monitoring and control of machinery, predictive maintenance, and quality assurance—leading to higher operational efficiency and reduced downtime.
• Retail: Powers in-store analytics, personalized customer experiences, shrink prevention, and inventory management—allowing retailers to process data quickly and respond to customer needs in real time.
• Healthcare: Supports remote patient monitoring, telemedicine, and real-time analytics of medical data—enhancing patient care and operational efficiency.
• Financial: Facilitates bank analytics, fraud detection, branch office experience delivery, and security—enabling organizations to operate more efficiently and respond rapidly to dynamic conditions.

Challenges in edge infrastructure and operations  

Edge environments introduce unique operational challenges:

• Environmental constraints: Limited power, cooling, acoustics, and space require specialized solutions.
• Bandwidth and latency: Efficient local data processing and transmission is essential due to bandwidth and latency limitations.
  
• Data sovereignty: Compliance with regional regulations adds complexity. 
• Operational complexity: Managing infrastructure distributed across multiple edge sites raises risks of inconsistent deployments and configuration drift. 
• Scalability: Edge systems must be able to support evolving workloads, especially as AI accelerates. 
• Limited technical expertise: Many edge locations lack on-site technical staff, complicating onboarding and troubleshooting.
• Security: Data processed across multiple edge locations can increase the risk of cyberthreats, which makes robust, multilayered security measures essential.
• Legacy integration: Integrating edge computing solutions with existing systems requires careful planning to ensure seamless interoperability and a smooth transition.

Key considerations for AI-ready edge infrastructure

Modern, AI-ready edge infrastructure should include:

• Flexible compute, storage, and GPU customization: Meets diverse and evolving workload and automation requirements.
• Centralized, SaaS-based management: Enables zero-touch provisioning, automated updates, and real-time visibility across distributed sites.
• Operational simplicity: Streamlines deployment and maintenance, essential for remote or resource-constrained locations.
• Comprehensive security: Provides end to-end physical and digital protection, including native firewalls, intrusion prevention, and multilayered safeguards for workloads and AI models
• Pre-validated, industry-specific solutions: Streamlines deployment, reduces operational risks, and accelerates time to value.
• Repeatable deployment at scale: Deploys patches and upgrades across multiple sites without needing to send a technician to each site.

Benefits of edge computing

Processing real-time data closer to where it is generated reduces latency and enables faster insights and decision making, which are critical for time-sensitive applications. The architecture also enhances operational efficiency by minimizing reliance on centralized data centers and reducing the need for constant, high-bandwidth connectivity. Edge computing supports new AI and IoT initiatives; improves customer experiences through personalized, local services; and reduces costs through real-time operational analysis.  

Overall, it drives innovation, agility, and competitiveness by delivering faster insights, better user experiences, and more efficient, scalable digital services. Additionally, edge computing improves cost efficiency by reducing the volume of data sent to and from central servers, resulting in significant bandwidth and storage savings.

Building blocks of edge technology

Edge computing technology consists of a distributed network of components such as sensors, IoT devices, and local servers that work together to process data at the source. Unlike the traditional cloud model—which sends data to distant cloud servers for processing—edge computing reduces data travel time and enhances operational efficiency. This is critical for applications that require instant data processing, such as traffic management in smart cities or industrial monitoring. 

Importantly, edge computing enhances data security and privacy by processing information close to the source, minimizing the risk of data breaches during transmission to remote servers. This makes edge computing ideal for industries that handle sensitive information and must adhere to strict data privacy regulations. 

Types of edge computing technology

Edge cloud

Edge cloud is a hybrid model that leverages the strengths of both edge computing and cloud technology by processing critical data at the edge while using cloud resources for more extensive data storage and complex analyses. This synergy allows businesses to ensure quick response times and robust data analysis capabilities. Edge cloud solutions are particularly valuable for organizations that require scalable, reliable infrastructure that maintains high performance even as their data processing needs grow.

Fog computing

Decentralizes a computing infrastructure by extending the cloud through the strategic placement of nodes between the cloud and edge devices. This architecture brings data, compute, storage, and applications closer to users or IoT devices, allowing processing to occur near where data is generated. By creating a “fog” outside the centralized cloud, fog computing reduces data transfer times and latency for data processing.

Multi-access edge computing (MEC)

As defined by the European Telecommunications Standards Institute (ETSI), MEC provides application developers and content providers with cloud-computing capabilities and an IT service environment at the edge of the network. This environment offers ultra-low latency, high bandwidth, and real-time access to radio network information that can be leveraged by applications.

Micro data centers

Highly mobile, ruggedized data centers that deliver the same components as traditional data centers but are deployed locally near the data source.  Their flexibility allows for custom-built configurations tailored to the specific implementation requirements of unique situations, such as for hosting 5G virtual network functions in the field or managing predictive maintenance on the factory floor. 

Cloudlets

Small-scale, mobility-enhanced data centers that are situated close to edge devices, enabling these devices to offload processes to the nearby cloudlet. Modeled after clouds, cloudlets are designed to improve resource-intensive and interactive mobile applications by providing low-latency computing resources. 

Emergency response units

Mobile, self-contained systems that establish interoperable communications for first responders in emergency situations. They can be rapidly deployed to any crisis sites, along with highly skilled tactical operations teams to quickly reestablish critical communications in affected areas.