It’s a new year, and with it, we’ve brought together some of our resident product experts and technical evangelists here at Scale Computing to share their insights and offer their forecasts on what trends might shape the edge computing, hyperconverged infrastructure (HCI), and virtualization market in the year ahead.
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
In 2025, edge computing will become 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. Likewise, 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, as companies need to ensure predictable costs while enhancing AI performance.
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. Similarly, in manufacturing, where IoT devices and sensors monitor equipment health, edge-based processing can enable predictive maintenance while reducing latency and dependence on the cloud.
Looking ahead, 2025 will likely 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 Solutions at the Edge: ROBOs Will Embrace Containerized Deployments in 2025
In 2025, containerized solutions will become indispensable for remote office/branch office (ROBO) deployments as edge computing takes on a more prominent role in the distributed enterprise. As organizations seek scalable, agile application deployment across numerous locations, containers will provide the ideal lightweight, portable execution environment needed to meet these goals. By encapsulating applications and their dependencies into self-contained units, containerization allows developers and IT teams to create consistent, reliable deployment packages that work seamlessly at the edge. This approach is particularly valuable for distributed enterprises that require rapid deployment, simplified maintenance, and minimal resource overhead across multiple sites.
The success of containerization at the edge parallels that of the public cloud model, which popularized streamlined, application-first approaches to software deployment. Concepts like continuous integration and continuous deployment (CI/CD) and DevOps practices shifted focus from infrastructure management to application-centric development. By enabling developers to package applications in universal formats that operate the same way across development, testing, and production environments, containerization simplifies the otherwise complex deployment process.
Industries are already seeing these benefits play out in their edge deployments. In retail, containers allow stores to run localized applications for inventory tracking, customer insights, and real-time analytics, helping reduce reliance on cloud connectivity. Similarly, in healthcare, containerized applications are being deployed in clinical environments to support diagnostics and data processing directly at the point of care. Manufacturing plants, meanwhile, are using containers to standardize and automate processes across facilities, enabling quick updates and reducing the need for specialized infrastructure in each location.
Additionally, containerized solutions provide a universal framework that mitigates the complexities of managing edge sites with varying architectures. Edge operators can build multi-architecture packages that support a variety of CPU architectures, reducing compatibility issues and allowing organizations to optimize deployments for cost and performance. Furthermore, the layered nature of container images minimizes data transfer needs, as only updated layers are downloaded during deployment, addressing the challenges of limited or costly connectivity often faced at the edge.
With standardized observability and monitoring capabilities, containers offer ROBO environments a robust mechanism for maintaining application health and functionality across all sites and 2025 will see organizations continue to embrace containerized solutions as they look to improve the resilience and agility of their edge environments.
4. IaC and Kubernetes Bring Cloud-Like Simplicity to the Edge in 2025
In 2025, edge computing will mature to offer the same simplicity, flexibility, and agility traditionally associated with today’s popular public cloud platforms, transforming how organizations deploy low-latency, localized applications. With advancements in edge infrastructure and management practices, developers will increasingly be able to build and manage edge applications as easily as they do in the public cloud, paving the way for innovative services in industries that demand real-time data processing and minimal latency.
A critical enabler of this transformation is the adoption of Infrastructure as Code (IaC) principles at the edge. By extending IaC practices to edge environments, DevOps teams can leverage automated, version-controlled deployments for remote infrastructure, streamlining configuration and reducing deployment times. This approach enhances consistency, simplifies management, and allows for rapid scaling, even in highly distributed environments. As a result, edge computing is evolving to mirror the programmability and efficiency that made cloud platforms so successful, empowering organizations to maintain consistency across diverse environments without introducing further complexity.
Kubernetes is also gaining traction as a key technology that enables edge orchestration. As more enterprises seek to deploy applications at the edge, Kubernetes provides the scalability, reliability, and orchestration capabilities needed to manage distributed workloads. While challenging to implement, Kubernetes at the edge offers a unified platform for containerized applications, allowing organizations to standardize deployments and streamline management across both cloud and edge infrastructures.
Together, the principles of IaC and Kubernetes orchestration are set to transform the edge from a fragmented set of isolated nodes into a cohesive, cloud-like environment. By combining automation with robust orchestration, edge computing will support a growing range of applications that demand low latency, including IoT, augmented reality, and autonomous systems.
5. Edge Computing Fuels the Expansion of Computer Vision Beyond Retail in 2025
In 2025, edge computing will 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 2025 will see this technology flourish 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.