GigaOm Radar for Full-Stack Edge Deployments v3

Full-stack edge deployment solutions are cloud-managed and cloud-connected hyperconverged tools that provide all the capabilities necessary to run applications at customers' preferred locations for local data collection and processing.

These solutions bring a cloud‑like experience to edge locations, which are otherwise difficult to manage at scale with traditional IT practices, especially in DevOps‑oriented, agile organizations. They are considered full‑stack because they provide all the technologies required to manage edge deployments. For example, customers do not need to supply or maintain the underlying operating systems.

These solutions support multiple execution environments, including bare metal hardware, operating systems with type 1 or type 2 hypervisors, and containerization engines. Once deployed, they can provision and run applications through self‑service mechanisms delivered via a service catalog. Advanced solutions can enable customers to build their applications natively on the platform, which can take advantage of edge-native runtimes. Further, administrators can interact programmatically with the solution and provide a suite of visibility and troubleshooting tools.

These solutions are also labeled as “edge deployments” to differentiate them from as-a-service edge solutions, such as those provided by content delivery networks (CDNs) or edge development platforms. In other words, full-stack edge deployments are “near edge” solutions.

Particularly important use cases are those in which applications must operate under constraints related to connectivity, latency, data locality, architectural control, or cost:

• Run in air‑gapped environments with limited or unstable network connectivity

• Support latency‑sensitive workloads that require near‑real‑time processing

• Keep data local to meet legislative or regulatory requirements

• Avoid costly or slow data transfers when moving data off the edge is impractical

• Simplify cloud architectures in geographically distributed environments

• Reduce dependencies on cloud platforms where customers lack control over the underlying infrastructure or virtualization layers

• Optimize costs, including storage savings through tiering and minimizing data‑transfer charges

All of the solutions evaluated in this report provide full‑stack edge deployments, but they originate from different architectural lineages and target distinct use cases. A key differentiator is their approach to scale, which must be assessed across scaling up, scaling out, and scaling down.

Those that scale up can support large workloads and massive amounts of data to be stored and processed on their nodes. Those that scale out can manage thousands of geographically distributed nodes. Those that scale down offer lightweight virtualization and runtimes that consume very small amounts of compute and memory resources, suitable for IoT deployments and small form factor devices. It’s important to note that a vendor can deliver on more than one of these scalability types.

This is our third year evaluating the full-stack edge deployments space. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Radar report examines 16 of the top full-stack edge deployments solutions and compares offerings against capabilities (table stakes, key features, and emerging features) and nonfunctional requirements. This report provides an overview of the market, identifies leading full-stack edge deployment offerings, and helps decision-makers evaluate these solutions so they can make a more informed investment decision.

To help prospective customers find the best fit for their use case and business requirements, we assess how well full-stack edge deployment solutions are designed for specific deployment models (Table 1).

For this report, we recognize the following deployment models:

• Type 1 hypervisor: This type of hypervisor runs directly on bare metal to provision compute instances such as virtual machines (VMs)

• Type 2 hypervisor: This type of hypervisor runs on top of a host OS to provision VM compute instances

• Host OS: Vendors that do not offer a type 1 hypervisor (or another way of running applications on bare metal) can provide a proprietary or open source host OS to run type 2 hypervisors or container runtimes

• Container runtime: This runs on top of a host OS to provision container compute instances

• Integrated hardware and software: These are prepackaged hardware and software solutions with ready-made images

Table 1. Vendor Positioning: Deployment Model

Table 1 components are evaluated in a binary yes/no manner and do not factor into a vendor’s designation as a Leader, Challenger, or Entrant on the Radar chart (Figure 1). 

All solutions included in this Radar report meet the following table stakes—capabilities widely adopted and well implemented in the sector:

• Converged infrastructure compatibility

• Centralized management

• Bare metal virtualization and containerization

• Software-defined infrastructure

• Logging and telemetry

Tables 2, 3, and 4 summarize how each vendor in this research performs in the areas we consider differentiating and critical in this sector. The objective is to give the reader a snapshot of the technical capabilities of available solutions, define the perimeter of the relevant market space, and gauge the potential impact on the business.

• Key features differentiate solutions, highlighting the primary criteria to be considered when evaluating a full-stack edge deployment solution

• Emerging features show how well each vendor implements capabilities that are not yet mainstream but are expected to become more widespread and compelling within the next 12 to 18 months

• Business criteria provide insight into the nonfunctional requirements that factor into a purchase decision and determine a solution’s impact on an organization

These decision criteria are summarized below.

Key Features

• Plug-and-play provisioning: To support a seamless deployment experience, solutions strive to be as close as possible to plug-and-play provisioning. This means that customers, upon receiving their solution hardware, can plug the device in and provide network connectivity. The solution will be automatically provisioned and connected to the management platform.

• Cloud-like management: This metric evaluates a solution’s ability to provide an administrator and developer experience similar to that available in the cloud. Using a web-based interface, organizations can provision compute resources and services, define access controls, and run applications.

• Cloud integrations: While full-stack edge deployments can operate completely independently of cloud environments, we expect that organizations will integrate these solutions with their existing cloud infrastructure to support use cases such as backup and disaster recovery, batch and large data processing, cloud bursting, and supporting distributed applications across the cloud and edge deployments.

• DevOps suitability: To support DevOps use cases, full-stack edge deployment solutions must provide capabilities to enable developers and infrastructure teams to interact programmatically with the solution. These teams require the tools, flexibility, and automation necessary to build, deploy, and manage applications across diverse environments, ensuring optimal resource use, operational efficiency, and accelerated delivery.

• Marketplace and services catalog: Solutions provide a marketplace of applications and services from which customers can self-serve the procurement and deployment of various applications. This helps organizations leverage a curated set of validated and prepackaged applications.

• Edge security: This criterion refers to the protection of devices deployed at the edge and the applications running on them. It should encompass safeguards at the hardware and network levels, support for third-party tool integrations, data protection, access controls, and secure software practices.

• Visibility and monitoring: Full-stack edge deployments must provide health monitoring, observability, and troubleshooting capabilities for deployed devices, services, and applications. This enables organizations to proactively identify and address issues, optimize resource use, and maintain the security and stability of their distributed infrastructure.

• Cluster management: This feature is evaluated based on the solution’s ability to define and manage clusters, which are collections of multiple deployments or nodes. This capability enables the consistent application of configurations, application deployments, and updates across all members of a cluster.

Table 2. Key Features Comparison

Emerging Features

• Development environments and software development kits (SDKs): Instead of enabling DevOps teams to programmatically interact with the solution, this metric assesses how well the tools offered by the solution enable developers to natively build and run edge applications.

• Non-x86 compute: By supporting non-x86 compute architectures, a full-stack edge deployment solution can cater to a wider variety of edge computing use cases, allowing customers to choose the most suitable hardware for their specific application requirements, whether they're power efficiency, performance, or specialized acceleration needs.

• Edge AI inference: This refers to the solution’s ability to support inference use cases at the edge. Inference is the post-training phase of an AI or ML product, when it processes a novel input for generation, analysis, or categorization. These models should be optimized to work on the edge devices, where resources are limited compared to those in data centers.

• Edge-native runtime: VMs can support large applications and are generally persistent or durable. Containers are more flexible and can also be ephemeral. However, both are fairly resource-intensive and subject to cold-start delays. Edge-native runtimes are specialized environments designed to run applications and services at the edge, optimizing performance and resource use.

Table 3. Emerging Features Comparison

Business Criteria

• Scale-up support: The solution can support large-scale use cases by enabling high-performance, resource-intensive deployments at a single location. This is achieved through the solution's ability to handle large hardware configurations, such as multinode clusters and full racks of compute, storage, and networking resources.

• Scale-out support: The solution can manage a large number of geographically distributed edge nodes. To do this, the full-stack edge deployment solution provides clear interfaces and centralized orchestration capabilities. This allows administrators to deploy, configure, and manage the virtualized components, services, and applications across multiple edge sites from a single pane of glass.

• Scale-down support: To accommodate resource-constrained IoT devices and other small form factor edge hardware, the full-stack edge deployment solution must be able to scale down its virtualization technologies and software components. This involves optimizing the solution's runtime, compilers, and system services to require minimal memory and CPU usage.

• Partner ecosystem: A full-stack edge deployment vendor should have a broad ecosystem of third-party hardware providers, software providers, channel partners, and distributors.

• Resiliency: The solution should provide robust high-availability and disaster recovery capabilities to ensure business continuity. For example, the solution should support data replication across multiple nodes to eliminate single points of failure.

• Support services: The vendor should make support services consisting of professional services, managed services, and technical support available to customers.

Table 4. Business Criteria Comparison

The GigaOm Radar plots vendor solutions across a series of concentric rings, with those positioned closer to the center being judged as having the most complete solution. The chart characterizes each vendor on two axes—balancing Maturity versus Innovation and Feature Play versus Platform Play—while providing an arrowhead that projects each solution’s expected evolution over the coming 12 to 18 months.

Figure 1. GigaOm Radar for Full-Stack Edge Deployments

As you can see in Figure 1, vendors are equally distributed across the quadrants in the chart. Perhaps the clearest differentiation is the Feature Play/Platform Play split, where products in the Platform half are general-purpose solutions that can be used across verticals, while solutions in the Feature half have tailored their solution to verticals, such as IoT use cases, heavy industries, fleet management, or even Kubernetes management at the edge.

Vendors in this category seem to have different levels of investment in supporting edge use cases. Hyperscalers, despite a strong initial shared edge-cloud strategy, seem to have reduced investments in their portfolios. Other large providers whose portfolio extends beyond the scope of this report have built much stronger products compared to the hyperscalers. They are also actively developing partnerships and channels to market.

Most smaller vendors are positioned in the Feature Play half, distributed across both the Innovation and Maturity hemispheres and showing consistent year-on-year improvements across the board.

In reviewing solutions, it’s important to keep in mind that there are no universal “best” or “worst” offerings; every solution has aspects that might make it a better or worse fit for specific customer requirements. Prospective customers should consider their current and future needs when comparing solutions and vendor roadmaps.

INSIDE THE GIGAOM RADAR

To create the GigaOm Radar graphic, key features, emerging features, and business criteria are scored and weighted. Key features and business criteria receive the highest weighting and have the most impact on vendor positioning on the Radar graphic. Emerging features receive a lower weighting and have a lower impact on vendor positioning on the Radar graphic. The resulting chart is a forward-looking perspective on all the vendors in this report, based on their products’ technical capabilities and roadmaps.

Note that the Radar is technology-focused, and business considerations such as vendor market share, customer share, spend, recency or longevity in the market, and so on are not considered in our evaluations. As such, these factors do not impact scoring and positioning on the Radar graphic.

For more information, please visit our Methodology.

Arcfra: Arcfra Enterprise Cloud Platform (AECP)

Solution Overview

Arcfra Enterprise Cloud Platform (AECP) is a software stack that can deploy and run infrastructure at any location. It supports both traditional and cloud-native applications with full infrastructure support for compute, storage, networking, security, and disaster recovery.

AECP is a single software stack with multiple add-ons, sharing a single-pane-of-glass management console. The product suite consists of a cloud operating system, a virtualization and Kubernetes engine, network services, operation center, block and file storage, and data protection.

Arcfra Cloud Operating System (ACOS) functions as a host operating system on top of which Arcfra Virtualization Engine and Arcfra Block Storage can be installed. It also supports integration with VMware ESX. The Arcfra Virtualization Engine (AVE) is a Kernel‑based Virtual Machine (KVM)-based virtualization for production environments, equipped with complete compute virtualization and VM lifecycle management functionalities.

Arcfra Kubernetes Engine (AKE) enables businesses to automate the complete production-grade Kubernetes infrastructure, simplifying deployment, management, and usage with an out-of-the-box experience. Arcfra Operation Center (AOC) is Arcfra's centralized management platform for clusters in multiple data centers. It oversees and manages all IT resources in one place.

Arcfra is positioned as a Challenger and Fast Mover in the Innovation/Feature Play quadrant of the full-stack edge deployments Radar chart.

Strengths

Arcfra scored well on a number of decision criteria, including:

• Plug-and-play provisioning: ACOS nodes in remote sites will be assigned an IP address automatically via DHCP or a static IP. Arcfra provides a wizard-driven workflow to guide ACOS installation and configuration with minimal IT skill requirements. Arcfra’s solution is on-prem based and natively supports offline mode or an air-gapped environment. When the connectivity is restored, the remote devices can reconnect with AOC to enable management functions like upgrade and expansion.

• Cloud-like management: AECP can provision VMs or containers through a centralized management console (AOC) as well as other cloud services like storage and networking. AOC provides a unified view and control for the entire environment, including clusters, VMs, containers, applications, storage, security policies, and load balances. AOC provides labeling methods to enable admins to work with identities rather than IPs.

• Edge security: Arcfra’s platform provides end‑to‑end security across identity, network, data, platform, operations, and compliance, using a zero trust, encryption‑first, and automation‑driven model integrated with external SIEM/SOAR tools.​ Arcfra supports software‑defined microsegmentation to enforce zero trust at the network layer and block lateral movement, with distributed firewalls applying policies at the VM level.​ It can define auto‑quarantine policies that can isolate suspicious VMs in one click, and unified security policies cover both VMs and Kubernetes workloads.​ ACOS applies automated hardening against RHEL 7 STIG baselines and standardizes controller VMs to a strict security profile, reducing configuration drift and platform attack surface.​

Opportunities

Arcfra has room for improvement in a few decision criteria, including:

• Cloud integrations: Currently, AECP supports disaster recovery use cases through integrations with cloud environments, but it does not establish bidirectional data flows with the cloud. It also does not support batch or large‑scale data processing, cloud bursting, or defining distributed applications across cloud and edge deployments.

• DevOps suitability: While Arcfra supports integrations with tools such as GitLab and offers a Terraform provider, it can further improve by integrating with other industry standard tools such as Argo, Jenkins, and Flux, supporting scripting via languages such as Python and JavaScript, and allowing customers to write custom integrations.

• Marketplace and services catalog: While Arcfra allows provisioning of services such as load balancers and firewalls, it does not currently offer a catalog of third-party products that customers can provision directly from the platform.

Purchase Considerations

Arcfra offers subscription-based licenses. It offers four packages: AECP Essential, which offers virtualization and storage; AECP Standard, which also includes Kubernetes; AECP Advanced for Full Stack HCI; and AECP Virtual Desktop Infrastructure (VDI) Essential for VDI Storage.

Use Cases

The solution enables bimodal IT within a single platform and is suited for edge and ROBO deployments, private AI workloads, active‑active data centers, and scalable, cost‑efficient VDI environments. It also supports business‑critical applications, databases, VDI workloads, and development and test environments.

AWS: AWS Outposts*

Solution Overview

AWS Outposts is a family of fully managed solutions that deliver AWS infrastructure and services to on‑prem or edge locations for a consistent hybrid experience. Outposts extends native AWS services to on‑prem environments and is available in two form factors: Outposts servers and Outposts racks.

The AWS Outposts rack is an industry-standard 42U form factor. It provides the same AWS infrastructure, services, APIs, and tools to data centers or colocation spaces. Outposts racks provide AWS compute, storage, database, and other services locally while still allowing users to access the full range of AWS services available in the region for a consistent hybrid experience.

The AWS Outposts servers come in a 1U or 2U form factor. They provide AWS infrastructure, services, APIs, and tools to on-prem and edge locations with limited space or smaller capacity requirements, such as retail stores, branch offices, healthcare provider locations, or factory floors. Outposts servers provide local compute and networking services.

Outposts is a fully managed service, which means AWS specialists will install and manage the appliances, including troubleshooting, carrying out updates, patching, backup, provisioning, incident management, business continuity, and cost optimization.

Once an AWS Outposts solution is activated in the Amazon Managed Services (AMS) Multi-Account Landing Zone or Single-Account Landing Zone account, organizations must follow the existing AMS change management processes to provision and manage AWS resources. AMS-hosted infrastructure can be managed by specifying an AWS Outposts-specific subnet. AWS Outposts lifecycles can be managed directly in the AWS Outposts console using the AWS Outposts self-provision services role.

AWS is positioned as a Challenger and Forward Mover in the Maturity/Platform Play quadrant of the full-stack edge deployments Radar chart.

Strengths

AWS scored well on a number of decision criteria, including:

• Cloud-like management: Outposts’ most notable strength is the shared AWS cloud management mechanism, which creates a single-vendor cloud-edge deployment with the same management interface and the same services running both in the cloud and locally on edge deployments.

• Visibility and monitoring: Outposts benefits from Amazon CloudWatch capacity metrics and alarms to monitor application health. Users can create CloudWatch actions to configure automatic recovery options and track the capacity utilization of their Outposts over time. Metrics include statistics for Outposts data points, as well as logs that capture detailed information about calls made to AWS APIs. These logs can be stored in Amazon S3. The solution also supports monitoring through Traffic Mirroring.

• Edge security: AWS Outposts provides extensive security, including at-rest and in-transit encryption via Transport Layer Security (TLS) and request signing using an access key ID and a secret access key associated with an AWS Identity and Access Management (IAM) principal.

Opportunities

AWS has room for improvement in a few decision criteria, including:

• Plug-and-play provisioning: Since AWS is a managed service, the customer does not have to handle the physical deployment and booting of the hardware but must work with AWS to arrange the deployment. Once deployed, the solution must set up an AWS virtual private cloud (VPC), subnet, and custom route table and configure the local gateway connectivity and the on-prem network.

• Cloud integrations: While Outposts is deeply integrated with the AWS portfolio and ecosystem, there is no out-of-the-box way to integrate with non-AWS environments.

• Cluster management: The solution lacks several advanced cluster management features, including the ability to use the management platform as the central interface for multiple deployments and to support cross‑cluster application execution with secure data exchange between isolated instances.

AWS was classified as a Forward Mover given its slow release cadence and few year-on-year developments.

Purchase Considerations

AWS Outposts is a managed service, which means AWS assumes the responsibility for procuring, delivering, installing, and operating the solution’s hardware. The shared responsibility model that AWS adheres to in its public cloud solution also extends to the AWS Outposts deployment. Third-party auditors regularly test and verify the effectiveness of AWS’s security. AWS Outposts’ biggest limitation is its small set of supported AWS services. The 42RU deployment can deliver most of the AWS services commonly used in enterprise environments, including EC2, S3, EKS, RDS, and VMware Cloud. In contrast, the 2RU deployment is limited to Amazon EC2, Amazon ECS, AWS IoT Greengrass, and Amazon SageMaker Edge Manager.

The solution has additional technical limitations, such as restricted air‑gap functionality, the absence of RDS metrics and logs, Linux workload ingest working only when the pre‑Workload Ingest EC2 instance is placed on a non‑Outposts subnet, and the inability to migrate Elastic Block Store volumes created on Outposts in non‑AWS Managed Services accounts into AWS Managed Services.

Use Cases

Outposts supports workloads and devices requiring low-latency access to on-prem systems, local data processing, data residency, and application migration with local system interdependencies.

The AWS solution is suitable for delivering high-quality experiences for interactive applications like real-time multiplayer games, running manufacturing execution systems (MES), high-frequency trading, and medical diagnostics that require low network latency and large amounts of compute power at the edge.

Azion: Azion Web Platform

Solution Overview

Azion is a comprehensive, full-stack distributed computing platform that integrates multiple products to build, secure, deploy, and observe modern applications seamlessly across various environments. This includes Azion’s operated distributed network, remote devices, on-prem infrastructure, and multicloud environments.

Azion is the only vendor in this report that delivers its platform in both software‑only form, deployable at customer‑selected locations, and as an as‑a‑service offering backed by Azion’s global network of geographically distributed points of presence (PoPs).

The Azion Marketplace is a curated digital catalog that offers ready-to-use edge-running software. Customers can easily purchase and deploy solutions from Azion or independent software vendors (ISVs). Customers can also act as ISVs by publishing and distributing their software through the Azion Marketplace, which features solutions spanning security, performance, and databases from vendors, including Radware, Fauna, Upstash, and hCaptcha. Available offerings include edge‑native functions, cloud‑native network functions (CNFs), third‑party WAFs, bot managers, databases, and professional services.

Azion Cells employs a layered isolation strategy. The hypervisor implements proprietary kernel-level isolation to partition processes and resources. The runtime system harnesses the power of the V8 engine, a high-performance open source JavaScript and WebAssembly interpreter. Using V8's Isolate, each edge function runs in its own execution context, ensuring data and process integrity. Azion Cells creates a secure sandbox for code execution to ensure each function operates within its defined boundaries, safeguarding against potential vulnerabilities.

Azion is positioned as a Leader and Fast Mover in the Innovation/Feature Play quadrant of the full-stack edge deployments Radar chart.

Strengths

Azion scored well on a number of decision criteria, including:

• Development environments and SDKs: Azion’s solution emphasizes the developer experience, allowing developers to choose from curated templates, import existing applications from GitHub, and interact with the platform through a CLI, graphical UI, APIs, SDKs, or natural language prompts via Azion’s ChatGPT plug‑in. The Azion Console is available as open source, enabling extensive customization, including white labeling. Developer‑focused features include a built-in integrated development environment (IDE) based on Visual Studio Code (VS Code) with live preview. The platform integrates with ChatGPT for generating or interpreting code and provides version control and automation for testing and deployment, along with integrations with tools such as GitHub.

• Edge-native runtime: The Azion platform operates on Azion's proprietary hypervisor and edge runtime environment, Azion Cells, which is purpose-built for distributed applications. The solution supports software-defined networking (SDN) and edge-native functions analogous to virtual network functions (VNFs) and cloud-native network functions (CNFs). The Azion Orchestrator enables real-time management and control of edge resources (including load balancers, firewalls, and other services), while the Traffic Router provides SDN capabilities, which dynamically route packets across the network for optimal performance based on real-time analytics.

• DevOps suitability: Azion’s distributed platform facilitates rapid, iterative application delivery and seamless infrastructure management by offering CI/CD integration; native support for Jenkins, CircleCI, and GitLab; and supporting infrastructure-as-code via Terraform and Ansible. The solution supports configurations in YAML and JSON, enabling infrastructure and application manifests as well as application definitions.

Opportunities

Azion has room for improvement in a few decision criteria, including:

• Plug-and-play provisioning: Though not inherently a challenge, the self-managed deployment via Orchestrator is available as an optional capability for customers requiring custom infrastructure control. In this instance, customers manage the underlying hardware and operating systems, while other solutions offer a fully packaged hardware-software solution or a type 1 hypervisor or operating system.

• Marketplace and services catalog: While Azion has a good marketplace and service catalog populated with proprietary and third-party apps, the solution has room to further expand its partner ecosystem.

• Edge security: As a software-only solution, Azion does not offer hardware security features such as attestation or supply chain security on hardware operated by the customer. The Azion-operated hardware implements secure boot, firmware integrity verification, tamper-resistant deployments, and leverages hardware security modules (HSMs).

Purchase Considerations

Customers can leverage Azion’s globally distributed network for as‑a‑service delivery of the platform and for edge deployments. This is particularly valuable when applications are sensitive to network performance across geographically distributed environments. However, customers seeking an integrated hardware‑software solution from a single vendor will find that Azion does not meet that requirement. Prospective users should evaluate Azion’s distinctive architecture to determine whether it aligns with their needs. In addition, while other products in this report rely on familiar virtualization technologies, Azion’s edge‑native architecture may require service refactoring or process changes.

Use Cases

Azion’s full-stack edge deployment solution can be used to build and run serverless applications on the Azion-operated network edge, as well as on remote devices, on-prem facilities, and multicloud environments.

Cisco-Nutanix: Cisco Compute Hyperconverged with Nutanix

Solution Overview

Cisco Compute Hyperconverged with Nutanix is a co-engineered solution that combines Cisco's hardware expertise and SaaS-based infrastructure management with Nutanix's hyperconverged software platform.

This combination provides a unified hardware solution and full-stack virtualization built using Cisco Unified Computing System (UCS), Nutanix Cloud Platform software, Cisco Intersight cloud operations software, and Cisco Unified Edge.

This integrated solution addresses the challenges of edge deployments by providing preconfigured and prevalidated hyperconverged infrastructure (HCI) nodes based on Cisco UCS servers and the Nutanix software platform for simplified deployment and configuration. It can scale compute and storage resources on demand to support dynamic edge workloads, incorporates built-in security features from Cisco and Nutanix to protect data at the edge, offers centralized management, and supports both containerized applications and virtual machines.

Nutanix Cloud Management (NCM) and NCM‑Edge Self‑Service automation can run applications across multiple hypervisors and clouds without platform lock‑in and can adjust workloads based on business priorities. Self‑Service also provides policy‑based governance to optimize VM usage and sizing, which can reduce OpEx and CapEx and shorten time to value.

Cisco-Nutanix is positioned as a Leader and Fast Mover in the Maturity/Platform Play quadrant of the full-stack edge deployments Radar report.

Strengths

Cisco-Nutanix scored well on a number of decision criteria, including:

• Cloud integrations: Nutanix full-stack appliances can connect and integrate with AWS, GCP, and Microsoft Azure using Nutanix Cloud Clusters (NC2). NC2 can be deployed on public cloud infrastructure, which can interoperate with on-prem Nutanix clusters on UCS. Nutanix products and services can run on bare metal instances in public clouds, allowing customers to easily migrate or extend applications from on-prem to public cloud providers. It can configure a cloud-tiering policy through which aged objects can be removed from local on-prem cluster storage and stored in the public cloud using AWS S3 or Azure Blob Storage. Spending can be monitored in the public cloud with dashboards for AWS, Azure, and GCP.

• Cluster management: The underlying Nutanix Cloud Infrastructure software combines the cluster’s storage devices into a single distributed, multitier, object-based data store. Its self-healing architecture replicates data for high availability, remediates hardware failures, and alerts IT administrators so that problems can be resolved quickly and the business can operate normally.

• DevOps suitability: With Self-Service and the Self-Service plug-in for Jenkins, administrators can create a fully automated CI/CD pipeline, resulting in faster application delivery and a more satisfied customer. In addition, the Self-Service domain-specific language used in NCM Self-Service is a specialized open source, Python-based programming language that allows developers to define and automate tasks and application workflows within their IaC environment. Certified solutions, such as Red Hat OpenShift (which includes CI/CD integrations, tools, and utilities) and Google Anthos, running on the Nutanix Cloud Platform, provide development environments and workspaces.

Opportunities

Cisco has room for improvement in a few decision criteria, including:

• Development environments and SDKs: The solution does not provide tools such as IDEs, SDKs, application templates and blueprints, or native version control to help developers build and run edge applications natively.

• Marketplace and services catalog: While customers can provision third-party services through the solution’s catalog, the vendor can further improve on this metric by providing an easy mechanism for third parties to develop and publish their applications to the catalog.

• Visibility and monitoring: While offering good visibility and monitoring capabilities, the complex co-engineered solution can make the product difficult to monitor across the hardware, firmware, and software layers, especially when considering integrations and cross-deployments with cloud environments. This limitation is addressed with Cisco’s device connector in Nutanix’s marketplace.

Purchase Considerations

The joint Cisco-Nutanix solution requires customers to adopt a technology stack built by two vendors, which can add management complexity. Technically, this is not substantially different from a broader solution delivered by a single vendor following an acquisition. From a delivery and support perspective, engagements are typically handled through channel partners, which should mitigate most challenges associated with a new deployment.

While the solution has high scores across most key features and business criteria, it is not intended to scale down for running on lightweight devices, such as IoT devices, gateways, or other small factor appliances. Neither the software nor the hardware component is designed to target these use cases.

Use Cases

The Cisco-Nutanix solution targets deployments in regional or remote data centers and remote office or other edge locations that require real-time data processing compute at the edge. Cisco caters to all use cases with the same hardware platform set, allowing customers to size their environments based on the application’s requirements.

ClearBlade: Edge AI, IoT Core+, Intelligent Assets

Solution Overview

ClearBlade provides a full-stack software solution for edge processing, which includes all necessary functions for protocol integration, logic processing, data persistence, optimized backhaul, and cloud management. ClearBlade Edge AI is bare metal and works on multiple CPU types, including x86, ARM, MIPSLE, Power, and containerized functions. The management platform allows users to dynamically synchronize logic, data structures, integrations, sidecar polyglot processes, and files in real time over the customer-selected backhaul.

ClearBlade Edge comes in two offerings: Basic Edge for lightweight protocol conversion, and Edge AI for full enterprise and AI capabilities.

ClearBlade Edge AI provides full‑scale edge computing capabilities, enabling advanced analytics, AI/ML models, and edge intelligence. It is designed for large‑scale industrial IoT (IIoT) environments and can support thousands of devices across distributed deployments. The platform integrates seamlessly with cloud and on‑prem infrastructure and includes failover mechanisms to ensure continuous operation in mission‑critical scenarios.

Some of ClearBlade’s recent developments include WebAssembly support through the V8 execution engine, enabling WASM‑based services to interact with containers and native edge capabilities. ClearBlade has also expanded its partnership with Google Cloud, making its products compatible with Google Distributed Cloud, alongside Google Kubernetes Engine (GKE) and Vertex AI.

ClearBlade is the only vendor in this report that delivers capabilities across all the emerging technologies discussed, including non‑x86 compute, development environments, edge AI, and edge‑native runtime environments. For developer environments, ClearBlade provides a web‑based IDE and a CLI that integrates with common editors such as VS Code, Cursor, Claude Code, Atmosphere, and others. Its Intelligent Assets Store offers both hardware and software add-ons for AI models and integrations. ClearBlade also supports the Open Neural Network Exchange (ONNX) AI runtime.

ClearBlade is positioned as a Leader and Fast Mover in the Innovation/Feature Play quadrant of the full-stack edge deployments Radar report.

Strengths

ClearBlade scored well on a number of decision criteria, including:

• Cloud integrations: ClearBlade provides cloud integrations via queuing and publishing using services like Kafka, SQS, and PubSub. It can also architect data flows to get machine and device data streaming directly into business applications and data lake strategies.

• Edge-native runtime: The complete ClearBlade stack is a proprietary runtime system developed to run on lightweight edge devices (the compiled runtime has a 35 MB footprint) and provides a full message broker, code execution engine, UI, authentication, and synchronization services. ClearBlade statically compiles its edge binary to be indifferent to virtualization technologies. ClearBlade can replace container and virtualization scenarios by running WebAssembly as a local service under a secure permissions model.

• DevOps suitability: ClearBlade offers capabilities such as integrations with CI/CD tooling like Jenkins, CircleCI, and GitLab, and IaC tools such as Terraform and Ansible. It also exposes features via APIs, supports declarative configurations via languages such as YAML, scripting via languages such as Python and JavaScript, CLI tools for managing edge clusters, and configuration management programs such as PowerShell.

Opportunities

ClearBlade has room for improvement in a few decision criteria, including:

• Cloud-like management: While ClearBlade offers a comprehensive suite of services for IoT use cases, it does not deliver a full cloud experience, as it does not support provisioning virtual machines or running network appliances.

• Cluster management: The solution does not provide some of the more advanced cluster management features, such as using the management platform as the primary interface for managing multiple deployments or supporting cross-cluster application execution that allows secure data exchange and interaction between isolated instances.

• Marketplace and services catalog: Although ClearBlade’s Intelligent Assets store allows customers to buy both hardware and software add-ins like vertical-specific AI models and integrations, the solution can further improve by offering third parties a mechanism to publish their products in the ClearBlade catalog. The vendor can also further expand its portfolio of integrations with third-party products.

Purchase Considerations

ClearBlade’s solutions are available through Google Marketplace and are priced monthly based on data volume, vCPU usage, or edge‑gateway consumption. These charges are aggregated monthly and billed through Google. The solutions are also available directly from ClearBlade, allowing customers to integrate with their preferred cloud provider.

While ClearBlade has excellent scale-out and scale-down capabilities, its scale-up capabilities are limited, meaning it is not intended to host resource-intensive applications or process large volumes of data at the edge. ClearBlade’s software solution is not suitable for customers who require an integrated hardware-software solution provided by a single vendor.

Use Cases

The solution suits heavy industries, utilities, and industrial IoT use cases. Verticals supported by ClearBlade include rail transport, water, oil and gas, mining, smart cities, logistics, healthcare, and energy.

Dell Technologies: Dell NativeEdge

Solution Overview

The Dell Technologies full-stack edge deployment is an integrated hardware-software solution that includes Dell NativeEdge orchestration software. Dell NativeEdge is an edge operations software platform that helps businesses securely scale their edge platform and orchestrate applications across distributed locations. It streamlines edge operations at scale through centralized management, secure device onboarding, zero-touch deployment, and automated management of infrastructure and applications. The NativeEdge solution includes a centralized orchestrator (available as cloud-hosted SaaS or on-prem) and compute hardware enabled by the NativeEdge OS. Endpoints reside on the customer network for application delivery, while the orchestrator can be Dell-managed in the cloud or deployed on the customer’s infrastructure.

Dell Technologies has built a robust partner ecosystem across its entire portfolio and through its OEM business. The partner ecosystem is geared toward ISVs and system integrators (SIs) that serve multiple vertical industries. Partners can test their applications and solutions at scale and on the latest edge infrastructure. Certified partners have access to Dell Technologies’ certification labs and can access deeper integrations with marketing, engineering, CTOs, and sales teams for development and go-to-market efforts. Self-certification programs are available.

Dell Technologies’ recent developments include high availability (HA) clustering, live VM migration, automated VM restart, and intelligent load balancing for edge clusters. The company also added support to extend NativeEdge orchestration to vSphere and Kubernetes environments, enabling orchestration across edge, data center, and cloud deployments from a single platform. This capability also includes easy import of VMs into NativeEdge from VMware systems.

Some blueprint plug-ins serve use cases such as connecting to, discovering, and orchestrating workloads into other environments, including existing vSphere clusters, Kubernetes clusters, and cloud environments like Azure.

Dell Technologies is positioned as a Leader and Outperformer in the Maturity/Platform Play quadrant of the full-stack edge deployments Radar chart.

Strengths

Dell Technologies scored well on a number of decision criteria, including:

• Plug-and-play provisioning: Dell NativeEdge offers both secure device onboarding and zero touch provisioning (ZTP). Endpoint devices only need to be connected to Ethernet and power, after which they automatically connect to the NativeEdge Orchestrator to onboard themselves and provision the Dell NativeEdge OS. NativeEdge also allows customers to upload their own application images to the Orchestrator for deployment across multiple endpoints. Devices continue running the application even when disconnected from the Orchestrator.

• Edge Security: NativeEdge endpoints are secured with zero trust from the entire supply chain from order to deployment. NativeEdge devices are also locked down with secure boot, where only NativeEdge operating systems can boot on these devices. NativeEdge endpoints offer Secure Component Verification to ensure the components have not been tampered with.

• Cloud-like management: The Dell NativeEdge solution has a web-based interface through which administrators can deploy VMs via a VM image or solution blueprint. In addition, a solution blueprint can provide deeper integrations to deploy applications and container-based applications inside that VM. Dell NativeEdge offers blueprints preloaded to deploy ISV-based solutions. The solution can securely onboard NativeEdge endpoints and deploy applications to those devices. Its open architecture enables connections to multicloud environments and supports application deployment across them.

Dell Technologies was classified as an Outperformer given consistent improvements across the product capabilities and partner ecosystem.

Opportunities

Dell Technologies has room for improvement in a few decision criteria, including:

• Cloud integrations: While NativeEdge can manage workloads in vSphere and Kubernetes clusters that reside in core data centers or public clouds, the vendor can improve by developing features such as cloud bursting or leveraging cloud services such as analytics, observability, and security.

• Development environments and SDKs: While Dell NativeEdge provides a solution blueprint written in YAML based on TOSCA standards, it could further improve this by natively offering developer-friendly features such as IDEs and SDKs.

• Visibility and monitoring: Dell’s NativeEdge platform offers centralized dashboards that display the health and status of endpoints, VMs, containers, and deployments, along with detailed hardware metrics and logs. However, the platform would benefit from enhanced visualization capabilities, such as global map‑based views of deployment locations and topological representations of edge, cloud, and on‑premises environments.

Purchase Considerations

Dell Technologies’ Edge Design Program is an exclusive feedback program for Dell NativeEdge that gives customers and partners early access to software and opportunities to engage directly with product managers. Dell Technologies is currently working with its ISV partners to develop blueprints for Dell Validated Designs. These blueprints will be preinstalled in the NativeEdge catalog, where Dell will maintain offerings from both Dell Technologies and its partners, and customers will be able to add them to their own catalogs.

Use Cases

The Dell NativeEdge solution can support use cases such as running in air-gapped environments with limited or unstable network connectivity, latency-sensitive operations needing near-real-time processing, and those requiring data to remain local to comply with legislative or regulatory requirements.

Google Cloud: Distributed Cloud Edge*

Solution Overview

Distributed Cloud Edge is a fully managed, integrated hardware-software solution that delivers applications equipped with AI, security, and open source at the edge. Google Distributed Cloud Edge uses a cloud-backed control plane that provides a consistent management experience for edge devices. Administrators can use the same tools, policies, and processes they use in GCP for mission-critical use cases running on the edge.

Distributed Cloud Edge is available in two form factors. Distributed Cloud Edge Rack is a rack of six Distributed Cloud Edge servers and two top-of-rack (ToR) switches. This configuration supports both local control plane and cloud control plane clusters. Distributed Cloud Edge Server is a standalone device that connects directly to the local network through the existing network hardware. This form factor supports only local control plane clusters.

Google Cloud remotely manages the physical machines and ToR switches that constitute the Distributed Cloud Edge installation. This includes installing software updates and security patches and resolving configuration issues. Network administrators can also monitor the health and performance of Distributed Cloud Edge clusters and nodes and work with Google Cloud to resolve any issues.

Distributed Cloud Edge can run Google Kubernetes Engine (GKE) clusters on dedicated hardware provided and maintained by Google that is separate from the traditional GCP data center.

Google Cloud is positioned as an Entrant and Forward Mover in the Maturity/Platform Play quadrant of the full-stack edge deployments Radar chart.

Strengths

Google Cloud scored well on a number of decision criteria, including:

• Plug-and-play provisioning: Distributed Cloud Edge hardware comes preconfigured with hardware components, GCP services, and network settings specified when ordering. Google Cloud installers complete the physical installation, and a system administrator connects Distributed Cloud Edge to the local network. Once the hardware is connected, it communicates with GCP to download software updates and establishes a connection with the associated Google Cloud project. At that point, node pools can be provisioned and workloads deployed on Distributed Cloud Edge.

• Cluster management: This includes configuring permissions, logging, and provisioning workloads for each cluster. The cluster administrator assigns nodes to node pools and node pools to Distributed Cloud Edge clusters. As a result, users can define clusters that span multiple nodes and can be deployed across geographies.

• Edge security: Distributed Cloud Edge supports hardware security modules such as Trusted Platform Module (TPM), a platform certificate, and port lockdown. It uses Linux Unified Key Setup (LUKS) to encrypt the logical volumes on each Distributed Cloud Edge-connected node. Network traffic between Distributed Cloud Edge-connected hardware and GCP is encrypted using MASQUE tunnels or TLS using per-machine certificates, which are rotated on a regular schedule.

Opportunities

Google Cloud has room for improvement in a few decision criteria, including:

• Cloud integrations: Although Distributed Cloud Edge is deeply integrated with the GCP portfolio and ecosystem, there are no out-of-the-box ways of integrating with non-GCP environments.

• Marketplace and services catalog: While Google Cloud offers a marketplace of proprietary and third-party services, only a few of these are suitable for the Distributed Cloud Edge product portfolio, meaning the range of third-party services available for this product is limited.

• Non-x86 compute: While the solution supports GPUs, it doesn’t currently support other architectures, such as ARM, DPUs, FPGAs, or ASICs.

Google Cloud was classified as a Forward Mover given its slow release cadence and few year-on-year developments

Purchase Considerations

Google Cloud’s full-stack edge deployment solution is particularly suitable for customers who already have a GCP footprint. The service is designed to extend Google Cloud’s capabilities to remote locations or on-prem data centers. Organizations that already have the skills and knowledge to manage GCP environments can realize a shorter time to value by deploying this solution.

Distributed Cloud Edge nodes are not standalone resources and must remain connected to GCP for control plane management and monitoring purposes. The Distributed Cloud Edge hardware and workloads can continue to run for up to seven days if Distributed Cloud Edge is disconnected from GCP.

Distributed Cloud Edge places several restrictions on workloads and features. For example, GKE Enterprise does not support Anthos Service Mesh, except for the ConfigSync feature of Config Management.

Use Cases

Distributed Cloud Edge solutions are suitable for applications that require a stable network connection and cannot tolerate the traffic disruptions that often occur when transferring data over the internet. They are also well‑suited for latency‑sensitive workloads and for applications that generate large volumes of data that would be performance‑ or cost‑prohibitive to move to and from GCP. Another key use case involves compliance with local laws or regulations that require data to remain on‑prem or within a specific geographic jurisdiction.

Litmus: Industrial Data Foundation Platform

Solution Overview

Litmus Industrial Data Foundation Platform is designed to address industrial edge compute use cases. It comprises three parts. Litmus Edge is the backbone, collecting data from heterogeneous industrial environments, building data pipelines enriched with context, analyzing the data at the edge, and enabling enterprise-scale data initiatives. Litmus UNS provides a collaboration layer for standardization and governance across IT, data, and operational teams. Litmus Edge Manager serves as the command center, simplifying remote management and enabling large‑scale rollout of the Industrial DataOps Suite. It is tightly integrated with Litmus Edge and is also used to manage Litmus UNS.

Litmus Edge can run as an OS directly on bare metal hardware (such as an Intel or Arm gateway) or as a VM or containerized deployment. This architecture creates an air gap between OT assets and IT systems. Litmus Edge can securely connect to plant‑floor environments while still allowing IT teams to manage the deployment. In a hybrid model, local OT teams can manage edge data use cases, while a central enterprise IT team oversees edge IT data pipelines, devices, security, and infrastructure from the cloud.

Litmus Edge offers multiple features to ensure high availability, disaster recovery, business continuity, and data protection at the edge. Litmus Edge can be deployed on redundant hardware configurations (including mirrored storage and failover capabilities) to minimize downtime due to hardware failures. It can configure automatic failover between edge nodes and edge-to-cloud failover configurations. Data can be backed up in the cloud for off-site protection.

The solution supports horizontal and vertical scalability. For horizontal scaling (or scaling out), customers can add additional Litmus Edge instances to distribute workloads and process data demands efficiently. This allows for scaling compute, storage, and networking resources as needed. For vertical scaling, customers can upgrade individual instances with more powerful hardware configurations (such as more CPUs, RAM, and storage) to handle increased data volumes within a single node.

Litmus is positioned as a Leader and Fast Mover in the Maturity/Feature Play quadrant of the full-stack edge deployments Radar report.

Strengths

Litmus scored well on a number of decision criteria, including:

• Cluster management: Administrators can define and manage Litmus Edge clusters in Litmus Edge Manager. This allows grouping-related deployments based on location, function, or other criteria. Cluster-level management enables applying configurations, deploying applications, and performing updates consistently across all members of the cluster. Data exchanges between isolated instances allow applications to access data or interact with components in other clusters.

• Cloud-like management: Litmus Edge Manager serves as the primary platform for managing multiple deployments. It provides a unified interface to view and monitor all Litmus Edge instances across diverse locations, deploy and update applications remotely across different instances, manage configurations and access control for each instance, and gather data and aggregate insights from all deployments.

• Visibility and monitoring: Litmus provides IoT network diagnostics for all devices connected to Litmus Edge, including CPU usage, resource utilization, and memory. It includes Grafana‑based visualization by default, can discover and map services and applications, and can monitor containerized applications and data models across all Litmus Edge instances.

Opportunities

Litmus has room for improvement in a few decision criteria, including:

• Edge-native runtime: Even though Litmus can run on devices with a small footprint (like Raspberry Pi), it does not currently provide an edge-native runtime, which is designed to run applications or resource-constrained devices to optimize performance and resource usage.

• Marketplace and services catalog: While Litmus offers a prebuilt marketplace catalog with popular applications preloaded, it can further improve the breadth of services on its catalog and provide third-party vendors a mechanism for publishing their products onto the marketplace.

• Edge security: While Litmus offers good security features such as AES 256 encryption for all communication between its devices, API security, and token-based authentication, it does not currently offer traffic inspection and filtering or hardware-based security.

Purchase Considerations

Litmus offers three tiered packages: Foundation, for customers looking solely for a common data layer at the edge to collect, process, store, forward, and integrate data; Growth, for customers that want to run applications and analytics at the edge and use Litmus’s management platform; and Scale, for customers that want to orchestrate applications and do ML, digital twins, vision processing, and more advanced functions. The tiers allow customers to select the best package based on their stage of development and grow into newer features and functionality as they advance in their journey.

As a software-only solution, Litmus does not provide its own integrated hardware-software appliances. This means customers need to procure and manage their own hardware deployments, working with third-party suppliers or leveraging their existing infrastructure.

Use Cases

Mainly targeting industrial, IoT, and OT use cases, Litmus’s solution can run as an OS, VM, or containerized application on any gateway, VM, or local server. It can connect to any system or machine directly or via the local network. The vendor has demonstrated deployments in verticals such as aerospace, automotive, food and beverage, precision manufacturing, mining, oil and gas, electronics manufacturing, and agriculture.

Microsoft: Azure Stack Edge and Azure Local*

Solution Overview

In late 2024, Microsoft updated its HCI product, launching Azure Local. It replaced all preexisting edge infrastructure products and is part of the adaptive cloud approach. Azure Local is cloud-connected infrastructure that can be deployed at physical locations under the customer’s operational control.

Azure Stack Edge is a purpose-built integrated hardware-software solution that includes devices such as Pro R, a ruggedized data center-grade appliance with a built-in NVIDIA T4 GPU; Pro, a 1U rack-mountable appliance, optimized for conditions in a data center or branch location; and Pro 2, a compact form factor optimized for edge and branch locations. Flexible mounting options and Mini R, a ruggedized, battery-operated small device designed for harsh environments and disconnected scenarios, are available.

Azure Stack Edge can run containerized applications and VMs at the location where data is created and collected. It can analyze, transform, and filter data at the edge, sending only the data needed to the cloud for further processing or storage. Azure Stack Edge acts as a cloud storage gateway and enables eyes-off data transfers to Azure while retaining local access to files.

Azure Local hosts Windows and Linux VMs or containerized workloads along with their storage. It is a hybrid product that connects on‑prem systems to Azure for cloud‑based services, monitoring, and management. An Azure Local deployment consists of a server or a cluster of servers running the Azure Local OS and connected to Azure. Through the Azure portal, administrators can monitor and manage individual Azure Local systems as well as view all Azure Local deployments.

Microsoft is positioned as a Challenger and Forward Mover in the Maturity/Platform Play quadrant of the full-stack edge deployments Radar chart.

Strengths

Microsoft scored well on a number of decision criteria, including:

• Cloud integrations: Azure Arc is a bridge that extends the Azure platform to help build applications and services with the flexibility to run across data centers, at the edge, and in multicloud environments. It helps develop cloud-native applications with a consistent development, operations, and security model. Azure Arc runs on both new and existing hardware, virtualization and Kubernetes platforms, IoT devices, and integrated systems. It can do more with less by leveraging the existing investments to modernize with cloud-native solutions.

• Cloud-like management: The solution allows customers to create generalized or specialized VM images, which are prepared from a Windows generalized image from a virtual hard disk (VHD), a generalized image from an ISO, or custom VM images starting from an Azure VM. The solution can enable compute resources to be provisioned via the Azure portal using templates, Azure PowerShell cmdlets, Azure PowerShell scripts, Python scripts, or the Azure CLI. These device VMs can be managed through the Azure portal, via the PowerShell interface, or directly through the APIs.

• DevOps suitability: The solution uses Azure Stack and Arc to allow organizations to develop edge-native applications; integrate Azure monitoring, security, and compliance into DevOps toolkits; create policy-driven application deployments; and propagate configuration across environments. The solution can integrate with tools such as GitHub, Terraform, and Visual Studio. Organizations can develop applications with end-to-end solutions from local data collection, storage, and real-time analysis. Azure Arc-enabled SQL Managed Instance or PostgreSQL can be deployed on any Kubernetes distribution and on any cloud.

Opportunities

Microsoft has room for improvement in a few decision criteria, including:

• Marketplace and services catalog: Customers can use an Azure Marketplace image to create a VM image for Azure Stack Edge deployments, but this process is more difficult compared to other Azure and non-Azure cloud procurement processes.

• Cluster management: The solution does not provide some of the more advanced cluster‑management features, such as using the management platform as the primary interface for managing multiple deployments or supporting cross‑cluster application execution that enables secure data exchange and interaction between isolated instances.

• Non-x86 compute: While the solution supports the processor architectures such as GPUs, it doesn’t currently support other architectures such as ARM, DPUs, FPGAs, or ASICs.

Microsoft was classified as a Forward Mover given its slow release cadence and few year-on-year developments

Purchase Considerations

The Azure Stack product family is mainly designed for high-performance use cases, which means the solution’s scale-out capabilities (for handling a very large number of edge deployments) are limited, as the products are mainly suitable for scale-up. Similarly, the scale-down features only go as far as the Mini R, without further flexibility for lightweight deployments. Compared to other solutions featured here that offer a turnkey experience, the Azure Stack suite of products requires configuration and development, which results in a longer time to value.

Use Cases

When deployed in edge locations, Azure Local supports latency-sensitive use cases for near-real-time processing. Microsoft targets these solutions to verticals such as financial services, public sector, manufacturing, retail, and healthcare.

Scale Computing: Scale Computing Platform

Solution Overview

Acumera acquired Scale Computing in July 2025, and the combined company now operates as Scale Computing. The solution now combines both companies’ capabilities to deliver a full-stack edge deployment solution that spans both edge infrastructure and edge computing as a service.

The Scale Computing Platform integrates core edge infrastructure (virtualization, compute, storage, and built-in backup/disaster recovery through SC//HyperCore) with centralized fleet management via SC//Fleet Manager and application operations through the SC//Reliant Platform. Together, these components provide a container‑first environment for deploying and managing applications across distributed edge locations.

The Scale Computing HyperCore virtualization suite includes a fully integrated, KVM‑based hypervisor with a patented block‑access, direct‑attached storage system that provides full fault tolerance and automated tiering across hybrid‑flash storage architectures.

Scale Computing Fleet Manager is a cloud‑hosted edge‑orchestration platform designed for monitoring and managing hyperconverged edge infrastructure at scale.

Scale Computing Reliant Platform, an edge computing as a service (ECaaS) offering, supports the deployment and operation of container‑first applications across large, distributed environments.

Scale Computing is positioned as a Leader and Outperformer in the Maturity/Platform Play quadrant of the full-stack edge deployments Radar chart.

Strengths

Scale Computing scored well on a number of decision criteria, including:

• Cloud-like management: Autonomous Infrastructure Management Engine (AIME) is the orchestration and management engine that powers SC//HyperCore. It handles day-to-day administrative and maintenance tasks automatically; monitors the system for security, hardware, and software errors; and remediates those errors where possible. It identifies the root cause and minimizes the impact of those issues when it can’t repair them automatically, notifying users with a specific problem determination and actions (including actions to secure the environment) rather than just sending a stream of data that must be interpreted.

• Plug-and-play provisioning: SC//HyperCore enables seamless programmatic deployment of containers. To run containers on SC//HyperCore, users simply deploy a container-optimized OS with a container runtime system of choice. SC//Platform’s Red Hat‑certified Ansible collection enables automated container deployment and management. The idempotent nature of Ansible makes it well suited for deploying containerized applications at scale. Ansible also allows IT and application teams to manage their edge infrastructure using IaC workflows.

• Visibility and monitoring: SC//Fleet Manager is a cloud-hosted monitoring and management tool built for hyperconverged edge computing infrastructure at scale. It can monitor fleets ranging from a single system to 50,000 SC//HyperCore‑based clusters, including centralized upgrade management and orchestration. SC//Fleet Manager can centrally stage clusters, eliminating the need to send technical resources on‑prem for installation, and it can securely access the SC//HyperCore UI for any cluster in the fleet without requiring complex remote access solutions.

Scale Computing was classified as an Outperformer given the Acumera acquisition, which provided a wide set of features for managing globally distributed locations.

Opportunities

Scale Computing has room for improvement in a few decision criteria, including:

• Marketplace and services catalog: While customers can provision native services in a cloud-like manner, Scale Computing currently lacks an integrated marketplace and services catalog that allows the purchasing and provisioning of third-party services.

• DevOps suitability: While the solution supports integrations with CI/CD tools and exposes features via APIs, there are discrepancies in these capabilities across the three products. The vendor can therefore improve the DevOps experience by integrating the products and providing feature parity for programmability.

• Non-x86 compute: While the solution supports the processor architectures such as GPUs, it doesn’t currently support other architectures such as ARM, DPUs, FPGAs, or ASICs.

Purchase Considerations

SC//HyperCore is sold as either a license based on the number of compute cores or a per-cluster site license for edge deployments. SC//Fleet Manager is sold as a license based on the number of clusters under management. Customers can purchase a full appliance up front or through a lease via a Scale Computing reseller partner, or they can lease the hardware. Customers can purchase hardware directly from a certified Scale Computing imaging partner to improve configuration flexibility.

SC//Reliant Platform pricing is based on the number of edge locations supported, and customers are charged monthly in a SaaS subscription model. Pricing scales based on the number of locations a customer deploys.

Use Cases

Use cases include IT operations simplification, mission-critical business app infrastructure, infrastructure automation, cost management and resource optimization, ROBO, or edge deployments.

Sidero Labs: Omni

Solution Overview

Sidero Labs’ Omni provides a SaaS or self-hosted solution for the simple and secure deployment, operation, and management of edge devices and Kubernetes clusters. It runs Talos Linux, an open source, hardened Linux OS purpose-built for Kubernetes. Edge devices running Talos Linux automatically create a secure, WireGuard-encrypted management network and automate and orchestrate the deployment and management of Kubernetes and Kubernetes applications, with integrated, secure enterprise authentication.

Omni is a platform for delivering hardened, robust Kubernetes on edge (or other) devices, without requiring local IT skills, and then enabling remote management, including deploying applications and services over secure WireGuard tunnels over the public internet. Omni and Talos Linux provide full API client libraries and command-line tools to automate authentication and administration tasks.

Cloud instances are fully labeled with cloud provider, machine type, availability zone, and region, enabling workloads to be intelligently targeted. Multiple appliance deployments can be managed using a labeling system or device-direct attributes to allocate nodes to classes of machines or clusters. Configuration of machines and clusters can be defined globally, per cluster, per class of machine, per role, such as control plane or worker, or with other labels.

Nodes can operate in offline mode. If the entire cluster is local to a site that is offline, there will be no impact from being disconnected. Upon reconnection, Omni will reconcile the disconnected cluster to ensure it conforms to the desired state. If the disconnected site is a member of a distributed cluster and the node can no longer reach the control plane nodes, the worker will continue its operations in offline mode.

One of Sidero’s recent releases includes an automatic provisioning capability called infrastructure providers that simplifies cluster creation. New security features include API auditing, Federal Information Processing Standards (FIPS)–compliant builds, and additional hardening of Talos to make critical services more robust.

Sidero Labs is positioned as a Challenger and Fast Mover in the Maturity/Feature Play quadrant of the full-stack edge deployments Radar chart.

Strengths

Sidero Labs scored well on a number of decision criteria, including:

• Plug-and-play provisioning: Nodes are provisioned automatically once they boot off an Omni image, and with no further configuration required, they will attempt to securely join the WireGuard network whose information is embedded in that image. Thus, as soon as they are powered on or joined to the network, they will register.

• Edge security: Omni-managed Talos Linux clusters offer good security services, including host and CNI-level firewalls, WireGuard encryption within the cluster and with the Omni platform, a Kernel Self-Protection Project (KSPP)-hardened OS, SecureBoot, TPM support, and certificate-based authentication.

• Cluster management: Omni manages Kubernetes clusters across cloud and edge environments and supports hybrid cluster topologies, such as placing control planes in the cloud while running workers at the edge. It also enables edge clusters to burst into the cloud for temporary capacity expansion. Omni automates backups to cloud storage, providing a foundation for disaster recovery automation.

Opportunities

Sidero Labs has room for improvement in a few decision criteria, including:

• Marketplace and services catalog: While customers can provision services via the Sidero UI, the solution does not currently provide customers with self-service mechanisms to purchase and deploy first- and third-party services.

• Visibility and monitoring: The solution could improve this feature by offering monitoring of firmware and drivers to ensure the infrastructure is kept up to date, geographical displays of the deployment locations on global maps, topological views of edge deployments, and visibility into cloud and on-prem environments.

• Cloud integrations: Sidero could develop features such as defining failover and failback scenarios to cloud services in the event of failures or configuration issues with the edge deployment, integrating with public cloud services (such as AWS S3 and Azure Blob Storage) for storage tiering and moving stale data to low-cost cloud environments, and leveraging cloud-based services such as analytics, observability, security, or identity management.

Purchase Considerations

Sidero Labs offers professional services to support the design of the Omni, Talos, and Kubernetes architectures. Technical support follows a three-tier model and is offered with US East business hours support or enterprise 24/7 support, with SLAs.

Use Cases

Sidero Labs’ solution can serve multiple use cases, as it can scale to hundreds of single and multinode clusters. The solution can support edge use cases at remote industrial sites, on factory floors for automation, in laboratory edge devices serving the healthcare and pharmaceutical verticals, and in retail locations. It can also support low-latency compute, local data processing, and data residency compliance.

Siemens: Industrial Edge

Solution Overview

Siemens Industrial Edge is an open, ready-to-use edge computing platform consisting of applications, OT and IT connectivity, devices, and a central management system for each. It offers integrated hardware-software solutions that simplify the collection, processing, and analysis of data from industrial assets, enabling fast and reliable software rollout on the shop floor and insightful decision-making.

Siemens’ solution places edge devices close to production or automation lines and runs the Industrial Edge Apps, which are either Siemens-developed or third-party applications for data analysis and other use cases.

The hardware and software components are centrally managed using Industrial Edge Hub, which offers a global app repository and software license monitoring, and Industrial Edge Management for lifecycle management of app software, hardware firmware, and all related configurations. The latter also offers mass rollout capabilities and roles and rights management.

The underlying platform components of the Industrial Edge Management and Hub are developed by Siemens, while the runtime is available on Siemens or third-party devices. Industrial Edge Virtual Device (IEVD) offers the Industrial Edge device functionality without the need for physical hardware devices, running on ESX.

The solution can send data to the cloud via MQTT to Insights Hub, including AWS, Azure, Alibaba Cloud, and all MQTT endpoints. A bidirectional asset model sync with Insights Hub will be released soon. The Industrial Edge applications are deployed and run on Industrial Edge devices, which can be physical or virtual.

Mendix is a low-code IDE that can be used to develop Industrial Edge applications seamlessly with a plug-in. All components offer several APIs that are currently made public and collected in libraries. CI/CD pipelines can be set up for deployment, management, and setup of applications and infrastructure. Siemens’ solution supports customer-built applications via the app publisher (a tool to migrate any Docker image to an Industrial Edge application) via UI or CLI.

Siemens is positioned as a Challenger and Fast Mover in the Maturity/Feature Play quadrant of the full-stack edge deployments Radar chart.

Strengths

Siemens scored well on a number of decision criteria, including:

• Marketplace and services catalog: One of Siemens’ strengths is the extensive catalog of applications and services available for Industrial Edge. The solution provides a centralized list of available services that users can provision through self‑service provisioning. The solution’s marketplace and services catalog includes identity management, resource monitoring and management, databus framework, cloud connectors, digital twins, and AI-based applications for use cases such as anomaly detection. The marketplace is based on an open ecosystem that includes third-party app providers, device builders, solution partners, and SIs.

• Visibility and monitoring: Industrial Edge Hub can monitor both hardware and software metrics. For hardware, the solution has visibility over memory, CPU, and storage, while the software monitoring provides visibility for application status and network traffic.

• Plug-and-play provisioning: Industrial Edge Management supports several provisioning mechanisms, including a CLI and Helm charts. Industrial Edge Management‑Virtual is configured through a virtual machine template. Both are then connected to the user’s Industrial Edge HUB tenant using an onboarding file.

Opportunities

Siemens has room for improvement in a few decision criteria, including:

• Cloud integrations: The solution integrates with AWS and Azure to establish bidirectional communication with cloud environments; support cloud failover, bursting, backup, and disaster recovery; and enable applications to run across both the edge deployment and the cloud. However, it can expand on this capability by adding support for GCP and other cloud providers.

• DevOps suitability: The solution can further develop these features by supporting declarative configurations via languages such as YAML, scripting via languages such as Python and JavaScript, using CLI tools to manage edge clusters, or using configuration management programs such as PowerShell.

• Edge-native runtime: While the IE Device Runtime is the basis for Industrial Edge OS on the devices, the solution could improve this by reducing the CPU and memory footprint of the runtime to make it suitable for deployments on resource-constrained or lightweight devices.

Purchase Considerations

Industrial Edge applications are priced per instance in a yearly subscription model, with self-development running at no additional cost. Essential applications for managing the solution, such as local data lakes or cloud connectors, are included for free.

Use Cases

Industrial Edge offers a set of tools and extensions to create microservice-based industrial applications for use cases such as performance analytics, energy monitoring and management, field-to-cloud connectivity, data consolidation, data model creation and semantics, anomaly detection of production components, AI/ML applications, virtualized controllers and sensors, and secure remote access.

SoftIron: HyperCloud

Solution Overview

SoftIron HyperCloud is a private cloud solution designed to provide a true cloud experience for on-prem and sensitive workloads. HyperCloud is an integrated hardware-software solution built as a single, elastic, and resilient cloud architecture that can consolidate a customer's entire data center infrastructure.

Customers can also purchase the software-only component to provide virtualization for running workloads on commodity hardware without a hardware refresh. It uses an underlying software stack similar to HyperCloud’s. With these two products, organizations can deploy a cloud-like experience at their preferred locations without buying into a wider public cloud ecosystem.

The solution’s multitenancy capabilities are notable and particularly important in large-scale deployments that must support multiple business units. These are supported via ACLs that offer granular policies, such as granting specific users or groups access to certain hosts, networks, and storage, or limiting users or groups to specific VM operations, such as allowing reboot but not undeploy. The virtual data center is a combination of a group and resources, where users can execute instances on the hardware in attached resource providers but do not have visibility of the hardware itself.

Over the last year, HyperCloud has added several major capabilities, including post-quantum full disk encryption with hardware token support, an in-browser air-gapped AI chatbot, expanded GPU and vGPU support (including Qualcomm Cloud AI inference cards and NVIDIA GRID 19), and new features such as UEFI Secure Boot, TPM 2.0, Windows 11 and Windows Server 2025 guest support.

SoftIron is positioned as a Leader and Fast Mover in the Innovation/Platform Play quadrant of the full-stack edge deployments Radar chart.

Strengths

SoftIron scored well on a number of decision criteria, including:

• Plug-and-play provisioning: As an integrated hardware-software solution, HyperCloud has good plug-and-play capabilities, enabling devices to integrate into the HyperCloud fleet on power-up. Once they find their HyperCloud, they download the latest software and are immediately added to the compute pool. They also contribute to the private cloud’s distributed fleet intelligence. Certificates, DNS, and authentication can be managed using simple commands.

• Cloud-like management: HyperCloud provides a public cloud-like experience with APIs and tools for self-service and cost-effective resource provisioning and use. The solution is managed as a single, self-maintaining cloud, making it easy to upgrade and maintain the entire system even when fully disconnected from the public cloud.

• Edge security: HyperCloud is designed with security as a foundational system property, enforced at the infrastructure layer. This includes native support for post-quantum cryptography (PQC) for data at rest and data in transit, ensuring long-term confidentiality and integrity even in environments with extended operational lifetimes or delayed upgrade cycles. HyperCloud systems implement hardware-rooted trust to ensure platform integrity from power-on through runtime. Secure boot, hardware authenticity validation, and cryptographic trust anchors are used to verify firmware and system components before execution. Runtime protections ensure only validated software operates on the platform, preventing unauthorized modification of the hardware or firmware stack. If a system is booted without access to the rest of the cluster, it is unusable.

Opportunities

SoftIron has room for improvement in a few decision criteria, including:

• Visibility and monitoring: This could be improved by offering geographical displays of the deployment locations on global maps; topological views of edge deployments, cloud, and on-prem environments; and interactive dashboards with the ability to drill down into specific clusters and access detailed metrics and diagnostics from a unified interface.

• Edge AI inference: While the solution supports AI inference at the edge and offers native implementation of AI runtimes such as ONNX, it can further improve by offering additional tools and workflows (such as PyTorch, Tensorflow, Keras, TFLite, or scikit-learn) to optimize trained AI/ML models and reduce the model size and memory footprint.

• Cluster management: Even though SoftIron has very good capabilities for managing its deployments in a cluster, these mainly target single-location rather than large scale-out deployments.

Purchase Considerations

Organizations considering SoftIron’s HyperCloud need to consider its space, power, and cooling requirements. Deploying these devices in colocation environments can help with scaling, as large colocation providers can support new space and power requests. Deploying them in on-prem data centers will depend on the types of facilities available and will likely require displacing current data center hardware.

Use Cases

The HyperCloud solution is well suited to organizations that need a private cloud for on-prem or sensitive workloads but want to avoid the management overhead and siloes associated with deploying multiple public cloud instances. Examples could include enterprises in regulated industries, government agencies, or organizations with large technical workloads that require low latency.

Synadia: NATS.io and Synadia Platform

Solution Overview

Synadia are the creators and maintainers of the open source technology NATS, which allows applications to securely communicate across any combination of cloud vendors, on-prem, edge, web, mobile, or IoT devices. Synadia offers an enterprise-grade distribution of NATS, Synadia Platform, and Synadia Cloud, which provide the operational tools and support to manage their NATS deployment.

The solution is a set of software components that can be deployed on any supported hardware, operating system, or architecture. Its foundational elements include NATS for connectivity and messaging; JetStream for persistence; Nex, a NATS‑native execution engine for hosting and managing workloads; Control Plane, which provides centralized management and observability for a NATS system; Private Link, a sidecar process that establishes an outbound connection to Control Plane; an HTTP Gateway for client interaction with NATS and JetStream; and connectors that integrate NATS with external services such as cloud platforms, databases, and other protocols.

Synadia is positioned as a Challenger and Fast Mover in the Innovation/Feature Play quadrant of the full-stack edge deployments Radar chart.

Strengths

Synadia scored well on a number of decision criteria, including:

• Plug-and-play provisioning: An operator can deploy Control Plane centrally and then deploy NATS or Nex on the hosts or devices where connectivity, data, and workloads need to be available. If Control Plane and NATS are on the same network, Control Plane can directly connect to the NATS servers, and they will be visible in the interface. However, if Control Plane can’t communicate with the NATS servers directly, the Private Link component can run locally alongside the NATS server, establish an outbound connection to Control Plane, and self-register.

• Cloud-like management: Nex provides a uniform interface for running NATS-powered workloads (including containers, event-driven functions, and jobs) on various cloud and edge-native runtimes. NATS eliminates the need to work with IP addresses or fully qualified domain names (FQDNs). Networking functions are implemented by NATS with pub/sub, streaming, Key-Value, and the rest of its functionalities.

• Cluster management: Synadia Cloud is a global supercluster spanning multiple cloud providers worldwide. Its use by automotive, networking, retail, and gaming organizations demonstrates its ability to support large-scale, distributed deployments. Beyond superclustering, leaf nodes provide extensive scalability and can even be daisy-chained.

Opportunities

Synadia has room for improvement in a few decision criteria, including:

• Cloud integrations: Synadia could develop features such as defining failover and failback scenarios to cloud services in the event of failures or configuration issues with the edge deployment. It could integrate with public cloud services (such as AWS S3 and Azure Blob Storage) for storage tiering, move stale data to low-cost cloud environments, and leverage cloud-based services such as analytics, observability, security, or identity management.

• Marketplace and services catalog: While Synadia offers a range of client libraries and provides NATS through cloud marketplaces such as AWS, it does not currently offer a marketplace of applications and services that customers can self-serve for procurement and deployment.

• Visibility and monitoring: While the Synadia Control Plane provides purpose-built monitoring, it could offer features such as monitoring of firmware, driver, and OS versions to ensure the infrastructure is kept up to date; geographical displays of the deployment locations on global maps; and topological views of edge deployments as well as cloud and on-prem environments.

Purchase Considerations

Synadia’s enterprise-grade distribution of NATS can be delivered as cloud-based SaaS, self-hosted, or a managed platform. Cloud SaaS pricing is based on a tiered consumption model, while self-hosted and managed platform pricing is based on the scale of the deployment.

Use Cases

Synadia’s NATS can be used for connecting distributed applications across multiple geographies, clouds, or out to the edge without requiring external software dependencies to run a production system. It can handle edge deployments by syncing data between the cloud and edge, including data mirrors and sourcing.

VMware: VMware Cloud Foundation Edge (VCF Edge)

Solution Overview

VMware Cloud Foundation Edge (VCF Edge) by Broadcom is an edge computing product portfolio that enables organizations to build, run, manage, connect, and protect edge-native applications at both near- and far-edge locations. VCF Edge is cloud agnostic and supports multicloud environments, running VMs, containers, and Kubernetes workloads on a unified stack, and real-time workloads.

VCF Edge is a single product that is part of the VMware Cloud Foundation product portfolio. It can be deployed alongside VMware Cloud Foundation or in a standalone fashion.

The VCF Edge runtime system is made up of vSphere ESX (hypervisor), VMware vSAN (HCI storage) with a shared vSAN witness, an edge-optimized vSphere Kubernetes Service (VKS) container runtime, VCF Operations (formerly Aria Operations) management with entitlements to the full VCF stack, including VCF Automation (formerly Aria Automation), NSX networking, and HCX for data migration.

VCF Edge includes entitlements to the VCF ecosystem of tools, such as VKS Cluster Management, and services to operationalize the Kubernetes runtime through VCF Operations. VCF Edge supports GPU workloads, which are used for computer vision and ML at the edge and can be deployed using payment card industry (PCI) passthrough or GPU sharing through the ESX hypervisor. Customers can leverage GPUs for both VM and container workloads.

Some recent releases include lean edge deployments with single and two-node hosts, auto provisioning with GitHub desired state images, live patching with no host downtime, integrated Kubernetes control plane, flexible storage configurations with vSAN and VMFS, airgapped disconnected environments, fleet management supporting thousands of hosts, and vGPU sharing.

VMware is positioned as a Leader and Outperformer in the Innovation/Platform Play quadrant of the full-stack edge deployments Radar chart.

Strengths

VMware scored well on a number of decision criteria, including:

• Visibility and monitoring: VCF Edge telemetry provides insight into communication endpoints and traffic patterns related to applications deployed at the edge. VCF Operations, an integrated component of VCF Edge, provides end-to-end network monitoring, visibility into network traffic and user behavior to help detect anomalous activity and threats leveraging ML models, and recommendations to resolve operational issues.

• Cloud-like management: VCF Edge provides a unified operations layer to streamline operations across all edge sites to monitor, troubleshoot, and run diagnostics for edge sites from the central console without the need to connect to individual edge sites separately through a CLI. It provides an option to use either VMware vCenter or VCF Operations to manage workload domains or clusters across different edge sites. The solution offers a GUI, CLI, and API to provision compute instances based on VMs or containers, which can then be managed through the centralized management platform. It can also provision and manage storage services such as object, block, or file storage to ingest and store data and services such as DNS, load balancers, firewalls, and network address translation (NAT), among others.

• Cloud integrations: VCF Edge allows customers to extend their modern data center or cloud infrastructure to edge sites so that applications can use the data locally for quicker processing. VCF Edge is cloud agnostic and supports multicloud environments, running VMs, containers, and Kubernetes workloads on a unified stack, supporting real-time workloads. Consistent infrastructure across central data centers and edge locations minimizes risks and complexities while integrating edge sites with central data center VCF instances. Consistent operations across edge, data center, and cloud minimizes the learning curve for the staff and allows them to use the same tools, skill sets, and processes across the entire IT infrastructure landscape.

VMware was classified as an Outperformer due to the refresh of its edge compute portfolio and associated recent developments, such as improvements over the Cloud Integrations and Cloud-like Management key features.

Opportunities

VMware has room for improvement in a few decision criteria, including:

• Plug-and-play provisioning: While VMware supports features such as ZTP, programmable deployments, and wizard‑driven workflows, it is a software‑only offering and does not provide an integrated hardware‑software solution. Instead, it relies on OEM and ODM partners for end‑to‑end edge solutions. Customers may need to work with value-added resellers (VARs) and channel partners to achieve a full plug‑and‑play experience.

• Marketplace and services catalog: While VMware has a comprehensive third-party solutions catalog, the set of solutions available for VCF Edge is limited.

• Non-x86 compute: While the solution supports processor architectures such as GPUs, it doesn’t currently support other architectures such as ARM, FPGAs, or ASICs.

Purchase Considerations

Organizations interested in VCF Edge are buying into a wider portfolio of virtualization, orchestration, and networking services. This can be both a benefit and a deterrent: administrators can leverage existing VMware deployments and apply the platform across a wide range of use cases, but they must also manage multiple VMware products to meet all requirements for running workloads at the edge. The solution is not designed for scaling-down use cases that would, for example, run the edge compute stack on lightweight IoT devices or gateways.

Use Cases

The solution can support various use cases, including running real-time and non-real-time workloads, supporting GPU workloads at the edge, enabling industrial scenarios such as digital manufacturing platforms for IT and OT teams, and powering virtual radio access networks (vRANs) for telecommunications. Suitable verticals include retail, manufacturing, logistics, and energy.

ZEDEDA

Solution Overview

ZEDEDA delivers an open, distributed, cloud-native edge management and orchestration solution, simplifying the security and remote management of edge infrastructure and applications. ZEDEDA’s solution is composed of the commercial SaaS cloud controller and EVE-OS.

ZEDEDA’s recently released Kubernetes App Flows offers a full-stack edge Kubernetes-as-a-service solution that extends a cloud-native deployment experience to distributed edge environments.

EVE-OS is a lightweight, open source, Linux-based edge OS with open orchestration APIs deployed on bare metal edge hardware. The ZEDEDA controller leverages the open APIs embedded within EVE-OS to orchestrate both the hardware below and applications above.

ZEDEDA extends the cloud experience to the edge by providing fleet-level management, network visibility, auditing, remote orchestration and management, ZTP, zero trust security, role-based access control (RBAC), and the ability to deploy and manage nodes at scale from a central location.

ZEDEDA Edge Access is a remote access solution built into the ZEDEDA offering that enables IT administrators and platform operations teams to instantly access any remote device from any location at any time. It is a simple solution that provides secure access, control, and audit tracing for edge deployments.

ZEDEDA’s Edge Kubernetes Service is a secure, managed Kubernetes service for moving Kubernetes from the data center to the edge, enabling organizations to deploy, manage, and modernize their edge deployments. ZEDEDA-managed containers enable customers to run container workloads natively on EVE-OS when infrastructure for running Docker, K3s, or K0s is too heavyweight for the edge.

ZEDEDA is positioned as a Leader and Fast Mover in the Maturity/Feature Play quadrant of the full-stack edge deployments Radar chart.

Strengths

ZEDEDA scored well on a number of decision criteria, including:

• Cloud-like management: ZEDEDA’s cloud controller provides orchestration and lifecycle management of both applications and hardware deployed at distributed edge locations. It includes ZEDEDA Edge Application Services, which are distributed, cloud-native, edge-first services that simplify the security and remote management of edge infrastructure and applications at scale.

• Edge security: The solution’s security features include ZTP with a hardware-backed zero trust workflow, remote attestation, third-party security appliances, wireline encryption, RBAC, identify provider integrations, user access controls, password control for users, and full policy-based key exchange (non-password) access for hosts.

• Marketplace and services catalog: ZEDEDA includes an embedded marketplace with an extensive collection of applications and solutions, including OSs, container runtimes, network security, network switching and routing, SD WAN, SASE, data connectivity, data transformation, data visualization, data preparation and tagging, MLOps, AI runtimes, observability, eventing and actioning, and more. ZEDEDA offers model hubs in its marketplace, partnering with a number of large AI/ML providers. It has introduced AI/MLOps into its pipeline to allow instances to be run at the edge with the ease of running a VM.

Opportunities

ZEDEDA has room for improvement in a few decision criteria, including:

• DevOps suitability: While ZEDEDA’s recent Kubernetes App Flow release supports GitOps-Based Continuous Delivery, it can further improve by offering integrations with tools such as Argo, Flux, Ansible, Puppet, or supporting config files such as YAML.

• Plug-and-play provisioning: While ZEDEDA has a very good provisioning mechanism for its instances, it does not include hardware in its offering, which means customers need to procure and manage the hardware component from a third party.

• Cloud integrations: Although the solution can integrate and has awareness of public cloud environments, it could improve by allowing customers to define applications that span ZEDEDA and cloud workloads.

Purchase Considerations

ZEDEDA is delivered as-a-service with a subscription-based enterprise license and a pay-as-you-grow model. Subscriptions are based on edge compute capabilities with additional options available. The EVE-OS component is governed by the Linux Foundation and licensed under Apache 2.0. ZEDEDA. It includes 24/7 support for EVE-OS.

Use Cases

ZEDEDA’s solution is suitable for managing globally distributed edge deployments, which are common in industries such as oil and gas, heavy industry, and retail. The solution can be used in both IoT and OT scenarios, including in air-gapped environments. It offers scale-down and scale-out capabilities, making it suitable for distributed compute use cases on resource-constrained devices.

One of the most interesting observations about the vendors featured in this report is the range of use cases and the differences among them. While all vendors offer a full-stack solution for running workloads at the edge of the network, there is a huge variety in the types of workloads that can run. For example, vendors can deploy a full data center’s worth of appliances using solutions from vendors such as AWS, Microsoft, or SoftIron, or very small workloads on lightweight devices supported by solutions such as ClearBlade’s.

Organizations can purchase integrated hardware-software solutions from Siemens, Scale Computing, and Dell, or software-only solutions from ZEDEDA, Broadcom, Synadia, or Acumera. Those that distribute content globally can even tap into a global network provided by Azion that runs dozens of PoPs.

This comes as no surprise considering the underlying technology required (the report’s table stakes) includes low-level technologies such as hypervisors and OSs that can provide a platform for building any type of service. However, when we assess the solutions in this report with comparable categories such as HCI, full-stack edge deployments offer most (if not all) the tools required to develop, deploy, and run applications, not just the platform to host them.

Full-stack edge deployments bring the lessons from a decade of public cloud experimentation to network edge locations, where centralized management and remote orchestration once seemed impossible using traditional infrastructure management practices. However, just as the cloud has not been the agile and cost-efficient dream we expected in the mid-2010s, the edge might face similar maturity challenges as markets adopt these solutions more and run them against new edge cases.

To learn about related topics in this space, check out the following GigaOm Radar reports:

• GigaOm Radar for Network Observability v6

• GigaOm Radar for Network Validation v4

• GigaOm Radar for Cloud Networking v5

*Vendors marked with an asterisk did not participate in our research process for the Radar report, and their capsules and scoring were compiled via desk research.

For more information about our research process for Radar reports, please visit our Methodology.

Andrew Green is an enterprise IT writer and practitioner with an engineering and product management background at a tier 1 telco. He is the co-founder of Precism.co, where he produces technical content for enterprise IT and has worked with numerous reputable brands in the technology space. Andrew enjoys analyzing and synthesizing information to make sense of today's technology landscape, and his research covers networking and security.

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