Why edge adoption is surging, and how to get it right

Once limited to wind farms, hospitals, or tactical deployments, edge is increasingly common across industries. Driven by the growth and demands of AI, sustainability goals, and rising cloud costs, edge adoption is exploding, with full-scale Kubernetes deployments at the edge having grown 400%.

Organizations moving to the edge can:

• Reduce latency and faster response times by processing data closer to where it’s generated.
• Optimize bandwidth usage by minimizing the need to transfer large volumes of data to centralized cloud systems.
• Shrink carbon footprint by reducing network traffic and reliance on energy-intensive data centers.
• Enhance privacy and data protection by keeping sensitive information on local systems.
• Improve reliability and fault tolerance with decentralized, distributed architectures.
• Enable real-time responsiveness for AI, machine learning, and time-sensitive applications.

We worked with The New Stack to compile a guide on Kubernetes at the Edge: Container Orchestration at Scale. In the process, we compiled the foundation knowledge organizations need to get started and understand the landscape.. Here’s what you’ll want to know.

How to start edge right

Teams often underestimate the complexity of integrating new systems with legacy processes, causing edge projects to fail early. This is especially true in manufacturing, energy, and logistics, where existing environments are often proprietary, stateful, and tightly coupled to physical infrastructure.

Cloud-native tools like Kubernetes aren’t plug-and-play in these environments. Legacy apps assume persistent local storage, static IPs, and constant connectivity, all of which conflict with modern, distributed orchestration. Breaking these assumptions often requires refactoring core logic, adding brittle workarounds, or introducing unnecessary complexity.

The other major oversight? Culture. Technical modernization without operational alignment leads to edge deployments that are brittle, hard to scale, and over-reliant on cloud APIs. Building for the edge necessitates taking on a new mindset. You must assume low bandwidth, offline conditions, and non-technical users.

Designing for edge means embracing:

• Data locality
• Latency sensitivity
• Disconnected operation
• Hardened, secure devices
• Distributed, resilient services

Containerization is now standard. Secure provisioning, reliable upgrades, and built-in recovery enable organizations to succeed from day one.

Use cases vary, but solutions can span industry lines

Edge computing is no longer a one-size-fits-all technology, especially across industries. “Edge” is not just a concept but rather a shifting reality defined by context: a dusty farm, a remote EV charger, a lab, or a retail checkout. Each industry is evolving its own understanding of what edge means, how to deploy it, and what success looks like. Here are some real-world examples.

Agriculture relies on edge computing to run analysis locally, which can reduce pesticide and water use through precision weeding and irrigation. Drones capture real-time imagery, which is processed on-site to guide targeted actions. This is especially important for farms with unreliable connectivity that need to make fast decisions based on local data.

Energy operations use edge-based analytics to monitor and reduce emissions. Real-time video and sensor data help detect and prevent excessive gas flaring. In the renewables space, edge systems manage solar arrays, EV charging, and battery storage, automatically adjusting to shifting energy demands and minimizing grid strain.

Healthcare demands secure, always-available systems. Hospitals and labs deploy containerized workloads on-site to maintain control of sensitive data while enabling fast, local diagnostics. AI models assist in analyzing medical imaging, and edge clusters ensure uptime even in the face of hardware failures.

Retail needs efficient, low-touch infrastructure for thousands of stores. Applications run locally for reliability, with fully automated provisioning and updates. Edge systems are designed to be replaceable, boot quickly, and recover without manual intervention, making them ideal for environments with limited IT support.

While use cases vary across industries, certain needs are universal. Organizations should prioritize solutions that address security, reliability, and automation. Talos Linux and Omni are designed from the ground up to be location agnostic, inherently secure, and ideal for hands-off upgrades.

For example, France’s national railway had to comply with a 200-page security manifesto while exiting public cloud. They needed zero-drift infrastructure, simplified patching, and reliable rollback. Meanwhile, global retailer JYSK needed hands-off, scalable Kubernetes across 3,400 sites, and industrial refrigeration automation technology provider CrossnoKaye needed a solution to laborious provisioning, so they could prepare and ship units faster–without jumping on TeamViewer just to get a device online.

Talos Linux solved each of these unique situations, providing the teams with an immutable, minimal OS that enabled hands-off upgrades, easy rollbacks, and more.

Want to Dive Deeper?

Download our ebook with The New Stack here. Kubernetes at the Edge: Container Orchestration at Scale covers real-world use cases, design strategies, and a competitive analysis of several industry players to help you get started, whether you’re at the beginning of the edge journey or making a pivot.