Enterprise Edge Compute Market Landscape

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Post Updated: Aug. 6, 2025

Hyperscalers focus investments on AI workloads, which will likely land in the cloud anyway and thus in some ways foster the edge ecosystem

With the edge AI opportunity stemming from the central cloud, the hyperscalers trim edge portfolios and focus investments elsewhere, creating new openings for edge pure plays

Over the past several months, many of the hyperscalers have reevaluated their edge portfolios to focus more on their central cloud services, which is where most of the AI opportunity will land. For example, AWS discontinued edge hardware in the Snow family and, later this year, will sunset features in its IoT Device Management service. In our view, these developments speak to customers’ preference for consuming edge computing as an extension of the central cloud; this includes customers migrating workloads to the cloud and building AI models that can be brought to the edge for a particular use case. This proposition will continue to challenge the “edge-native” players, including many hardware vendors and software pure plays that feed on demand from customers crafting their edge strategies from the ground up. But at the same time, it unlocks more opportunities within the ecosystem. For example, the hyperscalers’ pivot away from first-party IoT services welcomes more openings for IoT specialists that can attach themselves to an AI use case, while allowing the hyperscalers to strategically focus on AI and build the capabilities and infrastructure to support customers’ AI workloads regardless of where they are deployed. In some cases, we could see the hyperscalers investing in AI workloads to actually create an edge ecosystem.

AI use cases at the edge already exist

AI has been a foundational technology in enterprise edge computing for years and continues to support growth of the enterprise edge market, which TBR expects will expand from $58 billion in 2024 to $144 billion in 2029. TBR’s enterprise edge spending forecast has not increased significantly from our previous guidance in 2024, which already incorporated our long-standing assumption that AI will propel market growth. TBR expects that the industrywide focus on generative AI (GenAI) will likely lead to increased adoption of edge computing but that the bulk of enterprises embarking on these projects in 2025 will focus on piloting and adoption of cloud and centralized AI resources.

Compared to other deployment methods, edge expansion still lags

According to TBR’s 2Q24 Infrastructure Strategy Customer Research, 34% of respondents expect to expand IT resources at edge sites and branch locations over the next two years. But this is noticeably lower than the 55% who plan to expand IT resources within centralized data centers, while the central cloud and managed hosting are also gaining more traction. The possibility of large capital outlays and an unclear path to ROI remain the biggest adoption hurdles to edge technology, with some customers exploring other alternatives that have a clearer ROI road map.

GenAI will not have a significant impact on enterprise edge market growth, at least in the near term, as customers prioritize their investments in the IT core and cloud

Forecast assumptions

TBR continues to revise its enterprise edge forecast to account for changes in the traditional IT and cloud markets, including the advent of generative AI (GenAI). Although the enterprise edge market benefited from the hype surrounding AI in 2024, many pilot projects may not enter production and more concrete use cases around edge AI need to be developed.

The enterprise edge market is estimated to grow at a 19.9% CAGR from 2024 to 2029, surpassing $144 billion by 2029. Professional and managed services will remain the fastest-growing segment, followed by software, at estimated CAGRs of 22.4% and 19.3%, respectively.

Although there is general interest in edge across industries, demand varies by vertical, with surveillance and quality assurance use cases particularly strong in healthcare and consumer goods

Overall, the edge use cases that garnered the most interest from respondents were security & surveillance, quality assurance, and network (e.g., vRAN). Despite the AI hype, real-time analytics was the fourth most popular use case, although other use cases may embed these technologies.

Interest in deploying a certain use case is often industry-specific, such as above-average interest in security & surveillance among respondents in the consumer goods vertical.

Enterprise respondents had an above-average interest in remote asset management.

Cloud vendors look to partners to bridge the gap between IT and OT buyers and drive traction for edge solutions

TBR’s newly launched Voice of the Partner Ecosystem Report includes survey results from alliance partnership decision makers across three groups of vendors: OEMs, cloud providers and services providers. For cloud respondents, edge computing ranked No. 4 out of 26 technologies as the area that will drive the most partner-led growth in the next two years. This stems from the big gap that still exists between IT and OT buyers, and an overall optimism about cloud vendors’ ability to use partners to drive adoption.

OT stakeholders understand the edge but are not necessarily thinking about IT solutions through the lens of their own processes. Because of this, edge hardware vendors and cloud providers benefit from partnering with edge-native software vendors that have permission from OT buyers and can help edge incumbents sell solutions, including attached software and services. The dynamics between IT and OT departments reinforce the importance of the vendor ecosystem in the enterprise edge market.

Dell infuses GenAI into its NativeEdge edge operations platform, enabling customers’ edge environments to operate like their centralized data centers

TBR Assessment: Dell is working to build an ecosystem surrounding its hardware, primarily through expanding its NativeEdge operations software, enrolling ISV partners to create validated solution designs for specific industry use cases, and designing new services to facilitate edge infrastructure rollouts. Dell’s edge approach is increasingly intertwined with its AI strategy and its close partnership with NVIDIA, as Dell seeks to capitalize on growth opportunities through AI use cases that require video analytics, speech analytics and inferencing at edge locations. This approach expands Dell’s addressable market as its previous edge play was primarily focused on computervision solutions, and NVIDIA’s AI Enterprise software portfolio will open the door to a greater variety of use cases. Dell’s edge hardware portfolio includes not only ruggedized servers and gateways but also storage, backup and hybrid cloud solutions.

Key Strategies

• Build validated designs for verticals with high growth potential, including telecom, retail and manufacturing.
• Leverage Dell NativeEdge, an edge operations software platform, to add value on top of the company’s diverse infrastructure portfolio.
• Simplify edge management using AI, and add edge management features that support the needs of AI-based workloads.
• Partner with leading ISVs to provide an enhanced edge orchestration experience.

Recent Developments
In November SVP of Edge Computing Gil Shneorson outlined how Dell NativeEdge 2.0, Dell’s edge orchestration software, better enables AI workloads at the edge. Shneorson emphasized that although AI workloads have existed at the edge for years, Dell Technologies’ (Dell) orchestration platform utilizes AI to make these workloads easier to deploy and manage. One example is a new software feature that offers high-availability clustering, which provides automatic application failover and live virtual machine migration.

The scope of Dell-branded devices and infrastructure that can be managed under NativeEdge is broad — including servers, high-end storage and backup systems, edge gateways, and even workstation PCs. These various types of endpoints can be clustered through the software to act as a single system.


Dell has also updated NativeEdge to address other customer needs surrounding AI, including data mobility and support for NVIDIA Inferencing Microservices (NIMs).