5 Predictions for Edge Computing and Virtualization in 2025 and Beyond
The past year has been a transformative one for our company and for the edge computing market. With the combination of Scale Computing and Acumera, we've brought together complementary strengths that unlock new synergies at the edge. Scale Compting’s leadership in hyperconverged infrastructure and virtualization, combined with Acumera's deep expertise in distributed application orchestration and observability, has created a platform that is stronger than the sum of its parts.
A few months into the acquisition, it's clear that these synergies are resonating with customers: the ability to deploy applications at scale with enterprise-grade resilience, monitor them with intelligent observability, and orchestrate them seamlessly across thousands of locations is reshaping what’s possible at the edge. Looking ahead, these capabilities align closely with the broader trends shaping the industry. Here’s what I see driving edge computing, virtualization, and infrastructure innovation.
1. Frustrations with VMware Mount, Pushing IT Leaders to Seek Modern Alternatives
2024 proved to be a turbulent time for IT as VMware customers faced mounting pressures from licensing complexities, escalating costs, and restrictive product bundles. Broadcom’s acquisition-driven focus on short-term profitability—raising prices, cutting R&D, and changing products to focus on profit and not customer needs—continues to alienate long-time customers and partners.
These factors forced some organizations to make hasty decisions to mitigate immediate financial and operational burdens, while others, constrained by resource or timing limitations, began planning longer-term strategies to transition away from VMware.
This dissatisfaction represents more than a momentary shift; it marks the early stages of a broader exodus. Broadcom’s attempts to repair relationships by recalibrating its approach may come too late, as the IT community has grown skeptical of its intentions and more confident in pursuing alternative solutions that emphasize flexibility, innovation, and customer-first priorities.
The growing discontent has not only opened the door for VMware competitors but also stimulated the search for disruptive technologies and operational models. These shifts are likely to drive long-term changes in virtualization, including greater adoption of open-source hypervisors, edge-optimized platforms, and hybrid-cloud solutions.
2. Edge Computing Will Fuel the Next Generation of AI Innovation in the Enterprise
Edge computing is quickly becoming a foundational element of AI, shifting how companies collect, process, and analyze data. As AI applications grow more sophisticated and data-intensive, relying solely on cloud-based architectures will prove cost-prohibitive for many organizations.
The rapid scaling of data from AI applications requires immense computational resources, leading companies to turn to multi-layered edge infrastructures where data processing and storage can occur closer to the point of collection. For industries such as retail, healthcare, manufacturing, and transportation, this edge-centric approach addresses both financial and operational challenges associated with cloud usage.
The unpredictability of cloud costs will continue to be a significant driver in the shift toward edge-based AI processing. Cloud providers often charge based on data transfer, storage, and compute time, all of which scale dramatically with AI’s high CPU and GPU demands. By processing data locally at the edge, organizations can mitigate the volatility of fluctuating costs and reduce their dependency on long-term cloud storage and computation.
Furthermore, edge processing enables real-time data analysis, which is becoming increasingly critical in industries like healthcare, manufacturing, and retail. In retail, for instance, edge computing allows stores to leverage AI for real-time inventory management, customer behavior analysis, and even security monitoring without incurring excessive cloud costs. In manufacturing, where IoT devices and sensors monitor equipment health, edge-based processing enables predictive maintenance while reducing latency and dependence on the cloud.
Looking ahead, we believe we will see increased investment in multi-layered edge networks that can dynamically support AI workloads. With edge computing addressing both the operational and financial demands of AI applications, a broad cross-section of industries is poised to adopt more robust edge solutions, transforming edge-based infrastructure into a critical enabler of AI-driven innovation.
3. Scalable Edge Deployments Will Rely on Containerized Applications With Observability and Orchestration
In 2025, containerized applications will become a cornerstone of scalable edge deployments. Containers provide a lightweight, portable execution framework, allowing enterprises to deliver applications consistently across sites with diverse hardware, connectivity, and operational constraints.
The real differentiator, however, lies in combining container portability with embedded observability and orchestration. Organizations are increasingly adopting frameworks that not only package and deliver applications, but also provide real-time monitoring, predictive insights, and automated compliance checks to keep workloads healthy and secure. At the same time, orchestration frameworks simplify multi-site rollouts, reducing operational overhead and ensuring consistency across thousands of locations.
This dual capability mirrors the best of both worlds: the intelligence to detect and resolve network issues proactively (as seen in observability-first approaches like Scale Computing AcuVigil™) and the deployment resilience proven in complex, multi-site environments (as demonstrated in platforms like Scale Computing Reliant™). Together, these advances mean containerized applications can be deployed at scale with confidence, operating more like self-managing services than fragile workloads.
Industries such as retail, healthcare, and manufacturing are already benefiting. Retailers are running analytics and customer experience applications at the edge with proactive health monitoring and automated updates. Healthcare providers are deploying containerized diagnostic systems with continuous compliance checks. Manufacturers are using orchestrated, containerized apps to standardize processes across plants, with predictive monitoring to keep production lines efficient.
We believe containerization at the edge will evolve beyond portability—it will become intelligent, orchestrated, and enterprise-ready, enabling organizations to scale with agility and resilience.
4. Automation and Orchestration Will Bring Cloud-Like Simplicity and Intelligence to the Edge
In 2025, edge computing will take another leap forward by combining the automation of Infrastructure as Code (IaC), the scalability of container orchestration, and the intelligence of continuous observability and compliance. This convergence will allow enterprises to manage distributed edge applications with the same speed and flexibility they’ve come to expect from the cloud — but with lower latency, stronger resilience, and more predictable costs.
IaC ensures infrastructure updates can be rolled out across hundreds or thousands of sites in minutes, with version-controlled consistency and minimal manual intervention. Kubernetes provides the orchestration backbone to scale containerized workloads, making it possible to deploy, manage, and update distributed applications seamlessly.
The differentiator comes from embedding intelligent monitoring and automated policy enforcement directly into this stack. With AI-driven observability, edge environments gain self-healing capabilities, anomaly detection, and security-first compliance at scale. And with orchestration approaches hardened in complex multi-site environments (similar to Reliant’s proven model), organizations can manage thousands of sites as though they were one cohesive platform.
Together, these advancements transform the edge from a fragmented collection of isolated nodes into a cohesive, cloud-like fabric — one that is automated, observable, and orchestrated end-to-end. This shift enables enterprises to confidently run advanced workloads such as AI inference, IoT analytics, and real-time customer experiences closer to where data is generated, without adding complexity for IT teams.
The edge will no longer just extend the cloud—it will match its agility while adding intelligence and reliability purpose-built for distributed operations.
5. Edge Computing Fuels the Expansion of Computer Vision Beyond Retail
Edge computing continues to usher in a variety of innovative use cases for computer vision, driving adoption across industries beyond its retail origins. The ability to process visual data locally at the edge enables real-time decision-making and enhances operational efficiency in scenarios where latency and bandwidth constraints would otherwise hinder AI applications.
While 2024 marked a turning point for computer vision in retail, powering innovations like automated checkout, personalized shopping experiences, and loss prevention, we believe we will see this technology grow even more in sectors such as healthcare, logistics, and manufacturing, where the demand for real-time insights is accelerating.
Healthcare providers are poised to leverage computer vision at the edge for applications like diagnostic imaging, patient monitoring, and surgical assistance. By processing visual data locally, hospitals and clinics can make faster, data-driven decisions, enhancing patient outcomes while maintaining compliance with stringent data privacy regulations.
Similarly, logistics companies will deploy edge-enabled computer vision to optimize warehouse operations, track inventory in real time, and improve last-mile delivery efficiency. These capabilities reduce delays and errors, ensuring smoother supply chain operations in a world increasingly reliant on rapid, reliable deliveries.
Manufacturing will also benefit from the marriage of edge computing and computer vision, enabling advancements in quality control, predictive maintenance, and worker safety. Smart cameras integrated with edge infrastructure can improve the identification of defects on production lines, monitor equipment health, and detect potential hazards — all in real time.
Across all these sectors, the shift to edge-powered computer vision reduces reliance on cloud infrastructure, making the technology more cost-effective and accessible for enterprises.