Blog | nearby computing

Digital transformation is accelerating at a pace that surprises even those deeply familiar with it. New applications built on IoT, real-time AI, advanced automation and autonomous operations are no longer predictions — they are becoming reality and, crucially, they are moving to the edge of the network.

Yet, as highlighted in the report Why Edge-Native Platforms Are the Future of Computing (source STL partners, 2025) , many organisations are still trying to support these new workloads with tools designed for centralised cloud environments — an approach that simply doesn’t hold when brought into real-world edge conditions.

At the edge, environments are distributed, resources are constrained and connectivity cannot always be guaranteed. This makes traditional cloud-native systems — built for abundant compute and stable, high-capacity connectivity — less efficient, more complex and significantly more expensive to operate. As the report notes, “lift & shift” attempts often fail for three key reasons: their complexity overwhelms small edge devices, their generic design doesn’t suit diverse hardware, and operational costs quickly rise.

For this reason, a new architectural category is emerging: edge-native platforms. Lightweight, modular, and designed from the ground up for real-world edge deployments, these platforms enable organisations to run AI models locally, process data on-site, operate autonomously without cloud dependency, and manage hundreds or thousands of edge nodes with minimal overhead. In short, they make innovation — both the innovation happening today and what is coming — truly scalable and maintainable.

What must an edge-native platform include?

According to the report, a next-generation platform must be:

R Lightweight and efficient, capable of running in tight resource environments.

R Modular, avoiding unnecessary components and allowing incremental growth.

R Able to operate offline, providing resilience and autonomy.

R Designed for distributed management, enabling central control of many sites.

R Optimised for AI at the edge, enabling low-latency inference and continuous iteration.

How Nearby Computing makes this accessible — step by step

At Nearby Computing, we have spent years building exactly this kind of foundation. NearbyOne — our modular edge-to-cloud orchestration and automation platform — provides what the market now demands: the ability to start small, activate only the modules needed (infrastructure, applications, networks, APIs…), control costs, and expand as requirements evolve.

Because NearbyOne is modular, lightweight and fully vendor-agnostic, organisations can:

R Begin with a single use case and avoid upfront over-investment.

R Add new capabilities as digital operations mature.

R Scale confidently to thousands of nodes, multiple clouds, private 5G, or real-time AI workloads — all without redesigning their architecture.

Ultimately, it’s about enabling progress with clarity and confidence: no oversized budgets, no new silos, and full assurance that whatever is built today will remain relevant tomorrow.