Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
By Jeff Ready, CEO and co-founder, Scale Computing
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.
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ABOUT THE AUTHOR
Jeff is the CEO and co-founder of Scale Computing. Prior to founding Scale Computing, Jeff was co-founder and CEO of Corvigo, a Linux-based anti-spam appliance, where he oversaw the company from startup through funding to acquisition. After the acquisition, Jeff served as VP of Marketing at Tumbleweed Communications. Prior to Corvigo, Jeff was co-founder, COO and VP of Marketing at Radiate. Jeff holds a degree in Computer Science from Rose-Hulman Institute of Technology.
