What Is the DevSecOps Market?

DevSecOps — the practice of integrating security controls directly into the DevOps software development lifecycle (SDLC) — has moved from a niche discipline to a mainstream enterprise priority. Rather than treating security as a gate at the end of the release pipeline, DevSecOps shifts responsibility left, embedding security testing, policy enforcement, and compliance checks at every stage of development and operations.

Market research from Infoholic Research characterises the global DevSecOps market as one of the fastest-growing segments in the broader ICT industry, driven by escalating security breach incidents, wider cloud adoption, and the growing complexity of modern software supply chains.

Market Growth and Key Drivers

According to Infoholic Research, the global DevSecOps market was projected to grow at a compound annual growth rate (CAGR) of 33.7% over the forecast period 2020–2026. Several forces are cited as the primary demand drivers:

• Rising frequency and severity of security breaches — Attackers increasingly exploit vulnerabilities introduced during development rather than at the perimeter, making earlier security integration a business necessity.
• Need to reduce SDLC waste — Security defects caught late in the delivery pipeline are far more expensive to fix. DevSecOps tooling aims to surface issues early, reducing rework and accelerating release cycles.
• Increased investment activity — Venture capital and corporate M&A in security tooling have expanded the ecosystem of available platforms and integrations.
• Adoption of AI, automation, and cloud technologies — Automated static analysis, dynamic testing, container scanning, and cloud-native policy enforcement are becoming standard components of CI/CD pipelines.
• Regulatory and compliance pressure — Government mandates around data protection (particularly in the US, UK, Germany, and France) are pushing enterprises to formalise security practices within their development workflows.

Major Vendors and Competitive Landscape

The DevSecOps market is served by a broad mix of established enterprise software vendors, cloud platform providers, and specialist security tooling companies. Major players identified in the Infoholic Research report include:

• Broadcom — Enterprise software portfolio including CA Technologies security tools
• IBM Corporation — Application security testing and DevSecOps integrations within IBM Cloud and its broader security product family
• Micro Focus — Fortify static and dynamic application security testing (SAST/DAST) suite
• Synopsys Inc. — Black Duck software composition analysis and Coverity SAST
• Microsoft — Azure DevOps, Defender for DevOps, and GitHub Advanced Security
• Google — Cloud-native security tooling within Google Cloud Platform
• Palo Alto Networks — Prisma Cloud for container and cloud workload security
• Qualys Inc. — Cloud-based vulnerability management and compliance scanning
• Chef (Progress) — Infrastructure-as-code automation with InSpec for compliance-as-code
• Puppet — Configuration management and policy enforcement at scale
• Splunk Inc. — Security information and event management (SIEM) with DevOps pipeline integrations
• CyberArk Software Ltd. — Privileged access management within CI/CD pipelines
• Contrast Security — Runtime application self-protection (RASP) and interactive application security testing (IAST)
• Checkmarx Ltd. — SAST and software composition analysis
• Aqua Security Software Ltd. — Container and Kubernetes security
• Sumo Logic — Cloud-native log management and security analytics
• WhiteHat Security — Application security testing as a service
• AlgoSec — Network security policy management
• CloudPassage (Halo) — Cloud infrastructure security and compliance automation
• Continuum Security, Entersoft, Rogue Wave Software, 4ARMED LIMITED, DOME9 Security

Competitive Dynamics

The competitive landscape is characterised by two parallel trends. Larger vendors are pursuing partnerships and acquisitions to absorb specialist tooling capabilities and expand platform breadth. Simultaneously, smaller and emerging startups continue to attract customers with focused, developer-friendly tools that integrate easily into existing CI/CD pipelines. The net effect is a market where both point solutions and integrated platform offerings coexist, giving engineering teams a wide range of adoption strategies.

Regional Analysis

Americas

The Americas — led by the United States and Canada — represent the largest regional market. Factors cited include high baseline investment in advanced technology, stringent security regulations, and a mature enterprise software buying culture. The US in particular has a large installed base of regulated industries (financial services, healthcare, defence) with strong mandates for application security.

Europe

Europe is the second-largest region. The UK, Germany, and France are the primary revenue contributors. GDPR and national cybersecurity frameworks have created strong compliance-driven demand for tools that can demonstrate security controls within the development process.

Asia Pacific

Asia Pacific is highlighted as an important growth market. India, China, Japan, Singapore, and the Philippines show increasing enterprise interest in DevSecOps platforms, driven by rapid digitisation and a growing awareness of software supply chain risks. The region is positioned as a key expansion target for global vendors.

Middle East and Africa

MEA is characterised as an emerging market, with the primary growth window identified as 2020–2023 in the original report. Investment in digital infrastructure across the Gulf states is a contributing factor.

Deployment Models: Cloud vs. On-Premises

Most DevSecOps vendors offer both cloud-hosted (SaaS) and on-premises deployment options. Historically, large enterprises — particularly in regulated industries — preferred on-premises deployments to maintain data sovereignty and integrate with existing internal security tooling.

However, the trend is clearly towards cloud-native and SaaS delivery. The Infoholic Research analysis projected that the cloud deployment model would account for the larger share of market revenue over the forecast period, reflecting the broader enterprise migration to cloud infrastructure and the ease of integrating SaaS security tools into cloud-hosted CI/CD pipelines. Cloud providers in this space must still address concerns around data residency, multi-tenancy isolation, and supply chain trust.

Enterprise Segment Analysis

DevSecOps adoption spans enterprises of all sizes, but the dynamics differ significantly by segment:

Large Enterprises

Large enterprises currently lead market revenue. They have the budget to invest in comprehensive DevSecOps platforms, dedicated security engineering teams, and the organisational capacity to implement shift-left practices at scale. However, their complexity — legacy systems, large codebases, multiple teams — also makes full DevSecOps adoption a long-term programme rather than a quick deployment.

Small and Medium Enterprises (SMEs)

SMEs are disproportionately targeted by attackers precisely because they often lack dedicated IT security infrastructure and expertise. While SMEs currently represent a smaller revenue share, the report projects that this segment will grow its contribution significantly as affordable, cloud-delivered DevSecOps tooling lowers the barrier to adoption. Managed security service providers (MSSPs) and integrated SaaS platforms are key enablers for SME adoption.

Why DevSecOps Matters: The Security Case

The underlying premise of DevSecOps is straightforward: the growing surface area created by rapid software delivery creates more opportunities for vulnerabilities to be introduced and exploited. Traditional perimeter-focused security models are insufficient when the application itself — its code, its dependencies, its configuration, and its runtime environment — is the primary attack surface.

DevSecOps addresses this by making security a shared, continuous responsibility:

• Developers receive immediate feedback on security issues through IDE plugins and automated scanning in pull request workflows.
• Security engineers define policy as code, enabling consistent enforcement across all pipelines without manual review bottlenecks.
• Operations teams monitor runtime behaviour and feed threat intelligence back into the development process.

This collaborative model — often described as “security as code” — is what distinguishes DevSecOps from simply adding a security scan at the end of a pipeline.

Summary

The DevSecOps market is large, fast-growing, and intensely competitive. Established vendors like IBM, Microsoft, Synopsys, and Palo Alto Networks compete alongside specialist tools from Checkmarx, Aqua Security, and Contrast Security, with cloud platforms increasingly bundling security capabilities natively. For DevOps and platform engineering teams, this means a rich but complex tooling ecosystem to navigate.

The consistent message from market analysts is that enterprise investment in DevSecOps is not discretionary — it is a structural response to the reality that software supply chains have become a primary attack vector, and that security cannot be effectively outsourced to a team reviewing code at release time.

Original market data sourced from Infoholic Research via openpr.com (2020). Vendor portfolios and market positions may have changed materially since publication.

Google Anthos Migrate Reaches General Availability

At the Cloud Next event held in London in late 2019, Google announced that Anthos Migrate — its tool for converting VM-based workloads into containers — was moving out of beta and becoming generally available. The announcement was part of a broader set of product updates that also included the general availability of Cloud Code and Apigee Hybrid.

For DevOps and platform engineering teams managing legacy infrastructure, this is a significant milestone. Anthos Migrate lowers the barrier to Kubernetes adoption by allowing teams to lift existing VMs into containers without requiring a full application rewrite.

What Is Anthos and Why Does It Matter?

Anthos is Google Cloud’s application modernization platform. It is built around Kubernetes and enables organizations to:

• Build new cloud-native applications
• Modernize existing VM-based workloads by converting them to containers
• Manage and deploy workloads consistently across on-premises data centers, Google Cloud, and other public clouds

The platform’s core value proposition is operational consistency. Regardless of where a workload runs — on AWS, Azure, Google Compute Engine, or an on-premises data center — Anthos provides a unified control plane through Anthos GKE, Google’s managed Kubernetes service integrated into the platform.

How Anthos Migrate Works

Anthos Migrate automates the process of moving virtual machine workloads into containers that run on GKE. Applications running on AWS, Azure, or Google Compute Engine can be migrated and run inside the Anthos GKE environment. The key benefit is that teams do not need to refactor or rewrite application code before migrating — the VM is containerized as-is, giving teams immediate access to Kubernetes operational tooling.

Jennifer Lin, Google’s VP of Product Management, explained the strategic value for enterprises:

“That really helps customers think about a leapfrog strategy, where they can maintain the existing VMs but benefit from the operational model of Kubernetes. So even though you may not get all of the benefits of a cloud-native container day one, what you do get is consistency in the operational paradigm.”

— Jennifer Lin, VP of Product Management, Google Cloud

This phased approach is particularly useful for large enterprises that cannot afford to halt operations for a full re-architecture. Teams can containerize first, then progressively modernize workloads over time.

The Role of Kubernetes in Data-Driven Applications

Lin also highlighted why Kubernetes has become the platform of choice for modern data-driven applications:

“A lot of the need to do data-driven applications — that’s where Kubernetes has really hit that sweet spot because now you have a number of distributed datasets and you need to put a web or mobile front end on them. You can’t do it as a monolithic app; you really do need to tap into a number of datasets, do real-time analytics, and then present it through a web or mobile front end.”

— Jennifer Lin, VP of Product Management, Google Cloud

This reflects a broader industry trend: as applications increasingly rely on distributed data sources and real-time processing, Kubernetes provides the orchestration layer needed to manage the underlying microservices and infrastructure reliably.

Other Key Announcements at Cloud Next London

Cloud Code Goes GA

Cloud Code is Google’s set of IDE extensions (for editors such as VS Code and JetBrains IDEs) designed to help developers build, debug, and deploy cloud-native applications more efficiently. Its general availability means developers get stable, production-ready tooling baked directly into their existing workflows, reducing friction in the inner development loop for Kubernetes-based applications.

Apigee Hybrid Goes GA

Apigee Hybrid brings Google’s API management platform to hybrid and multi-cloud environments. With general availability, enterprises can manage APIs across their own data centers and multiple cloud providers, and deploy API proxies back into Anthos. This addresses a long-standing pain point for organizations that need centralized API governance without being locked into a single cloud provider.

Enterprise Adoption and Customer Examples

Google cited growing enterprise interest as the primary reason for moving Anthos out of beta. Among the customers highlighted at the event were Turkey’s Denizbank and Germany’s Kaeser Kompressoren, representing adoption across financial services and industrial manufacturing sectors. These examples illustrate that Anthos is gaining traction beyond pure technology companies, reaching industries with complex legacy infrastructure requirements.

What This Means for DevOps Teams

The general availability of Anthos Migrate, Cloud Code, and Apigee Hybrid together represent a maturing ecosystem around Google’s hybrid cloud strategy. For DevOps engineers, the practical implications are:

• Faster migration paths: Anthos Migrate removes the need for a full rewrite before gaining Kubernetes operational benefits.
• Consistent operations: A single control plane across on-premises and multi-cloud environments reduces operational complexity.
• Better developer tooling: Cloud Code GA means tighter IDE integration for building and debugging Kubernetes workloads locally.
• Unified API management: Apigee Hybrid enables API governance across hybrid environments without architectural compromise.

For organizations still running significant VM-based workloads, Anthos Migrate provides a concrete, lower-risk starting point for a Kubernetes adoption strategy — without forcing an all-or-nothing modernization decision.

Original reporting by Forbes. Announcements were made at Google Cloud Next London, 2019.

Overview

Juniper Networks released Juke 2.2, an updated version of the multi-cloud container platform the company acquired when it purchased composable infrastructure firm HTBASE in late 2018. The 2.2 release focuses on two persistent pain points in enterprise Kubernetes deployments: multi-cloud cluster management and the lack of a robust persistent storage platform for containerized workloads.

What Is Juke?

Despite HTBASE’s origins in composable infrastructure, Juke itself is not a composable infrastructure product. According to Scott Sneddon, Senior Director and Evangelist for Multi-Cloud Solutions at Juniper Networks, it is best described as container-focused software-defined storage with orchestration capabilities.

“Juke is primarily a distributed, persistent storage solution. We’re really focused in on the storage problem that exists and would coexist with some of the composable solutions that are out there by delivering storage, and some orchestration capabilities to help manage containers — Kubernetes in particular.”

— Scott Sneddon, Juniper Networks

Where Juke intersects with composable infrastructure is at the multi-cloud boundary: it bridges the storage and orchestration gap that composable offerings often leave unaddressed when workloads span multiple clouds or sites.

The Two Problems Juke 2.2 Addresses

Sneddon identified two core challenges that Juke 2.2 is designed to solve:

1. Multi-cloud Kubernetes management complexity — Enterprises running Kubernetes across multiple cloud providers or hybrid environments struggle to manage clusters, workloads, and storage consistently across those boundaries.
2. The absence of a reliable persistent storage platform for Kubernetes — Kubernetes was originally designed around stateless workloads. Providing durable, distributed persistent storage for stateful containerized applications remains a significant engineering challenge.

Key Updates in Juke 2.2

Kubernetes and CSI Integration

The headline technical change in Juke 2.2 is native integration with Kubernetes and its Container Storage Interface (CSI). CSI is the standardized API that decouples storage systems from the Kubernetes core, allowing third-party storage providers to expose volumes to pods without modifying Kubernetes itself.

This integration enables Kubernetes cluster scale-out with both compute and storage nodes that can:

• Span multiple clouds and on-premises sites, or
• Remain local to a single cluster availability zone

Volume Snapshots and Clone Management

Juke 2.2 adds volume snapshot and clone management capabilities, improving data reliability and workload mobility. Administrators can snapshot persistent volumes and clone them for use in other environments, making data portability across clouds more practical.

Improved Deployment and Installation

The release also includes deployment improvements intended to simplify the installation process, reducing the operational overhead of getting Juke running in a new environment.

Intelligent Workload Placement via Latency Awareness

One of the more architecturally interesting aspects of Juke 2.2 is its ability to feed storage latency and performance data back into the Kubernetes orchestrator to inform scheduling decisions. Because Juke manages the distributed storage layer, it can measure the latency between running containers and their storage endpoints, then use that data to influence where future workloads are placed.

“Because we are able to orchestrate this persistent distributed storage, we can also understand the latency between running containers and their access to storage. So we can take that latency and performance information, feed that back into the orchestrator, and determine where is the best place to deploy that workload.”

— Scott Sneddon, Juniper Networks

Practical examples of this capability include:

• Cost arbitrage: Run workloads on-premises during low-usage periods (nights, weekends) and automatically burst to the cloud when demand spikes.
• Edge compute proximity: Automatically route workloads to the cloud region or on-premises site closest to the data or the end user, based on real-time latency measurements.
• Multi-cloud resource automation: Use the Juke UI to manage core Kubernetes storage objects and automate resource access decisions across clouds without manual intervention.

Summary

Juke 2.2 represents Juniper’s push to address two real and well-documented gaps in enterprise Kubernetes adoption: persistent storage and multi-cloud cluster management. By integrating with the Kubernetes CSI standard and adding latency-aware scheduling feedback, the platform aims to give platform engineering teams a unified control plane for stateful containerized workloads across heterogeneous infrastructure.

For teams evaluating Kubernetes storage solutions in multi-cloud environments, Juke 2.2’s combination of CSI compliance, snapshot support, and workload placement intelligence makes it a notable option from the networking and infrastructure vendor space.

Hybrid clouds are a necessary step — and often a transitional one — for enterprises evolving toward greater reliance on public cloud infrastructure. Rather than a hard cutover, most organizations spend years running workloads across on-premises data centers and one or more public clouds simultaneously. Succeeding in that in-between period requires investing in four key capability areas: container orchestration, integration middleware, DevOps pipeline tooling, and management automation.

Announcements from the 2018 Red Hat Summit illustrate how vendors were responding to this demand, through partnerships with Microsoft, IBM, and Hewlett Packard Enterprise, and through the integration of CoreOS technologies acquired by Red Hat. Each partnership targeted a distinct layer of the hybrid cloud stack.

Kubernetes Orchestrates Containerized Apps Across Hybrid Clouds

Container orchestration sits at the foundation of any hybrid cloud strategy. Kubernetes has become the de facto standard for scheduling and managing containerized workloads, and its portability across on-premises and public cloud environments makes it a natural fit for hybrid architectures.

At the 2018 Red Hat Summit, Red Hat and Microsoft announced a jointly managed OpenShift Container Platform offering on Microsoft Azure — at the time described as the first fully managed Kubernetes-based platform to support both Linux and Windows Server container workloads in a single hybrid environment. Red Hat OpenShift on Azure was designed to let customers connect on-premises OpenShift clusters to Azure services more rapidly and securely.

Key capabilities announced for Red Hat OpenShift on Azure included:

• Public cloud onramp: Deployment of Red Hat OpenShift Container Platform on Microsoft Azure Stack, providing access to Azure services such as Azure Cosmos DB, Azure Machine Learning, and Azure SQL DB from on-premises environments.
• Heterogeneous container support: Flexibility to move cloud-native applications between on-premises orchestrated containers and the Azure public cloud, with support for Windows Server containers alongside Red Hat Enterprise Linux. The solution also aimed to support automated, real-time load balancing across containers running in private clouds, Azure, and AWS.
• Developer integration: RHEL Linux credits for Visual Studio Enterprise and Visual Studio Professional subscribers, plus access to OpenShift container management features from .NET, Java, and other popular frameworks.
• Joint vendor support: Fully co-managed by Red Hat and Microsoft, with colocated support teams covering containerized applications, operating systems, infrastructure, and orchestration layers including Red Hat Enterprise Linux on Azure, Microsoft Azure Stack, and Microsoft Azure Kubernetes Service.

Middleware Threads Through Hybrid Clouds

Building, connecting, and managing hybrid cloud applications requires a supporting layer of middleware that has been optimized for containerized microservices. Without this layer, integrating data services, messaging systems, and application runtimes across environments becomes a significant operational burden.

Red Hat and IBM announced an expansion of their partnership at the same summit, integrating OpenShift with IBM’s hybrid cloud middleware portfolio. Specifically, IBM committed to extending IBM Cloud Private and IBM Cloud Private for Data to run on Red Hat OpenShift Container Platform.

The stated goals of the Red Hat and IBM partnership included:

• Deploying containerized apps to a common hybrid cloud foundation integrating Red Hat OpenShift with IBM Cloud Private.
• Providing a unified container platform in which IBM Cloud Private delivers a single view of all enterprise data.
• Running containerized versions of IBM Cloud Private services as Red Hat Certified Containers.
• Leveraging IBM Cloud Private’s self-service big-data catalog and operational management tools on Red Hat OpenShift across all hybrid cloud footprints, including the IBM public cloud.
• Running containerized instances of IBM’s AI, IoT, and blockchain services with IBM Cloud Private on Red Hat OpenShift.

The partners also announced that IBM PowerAI would be available on RHEL for on-premises deployments, and outlined plans to accelerate containerized versions of Red Hat solutions on IBM Power Systems. IBM was simultaneously executing a broader program to migrate its middleware and application portfolio to containers for hybrid cloud deployment.

DevOps Drives Operationalization of Containerized Apps in Hybrid Clouds

DevOps practices — continuous integration, automated testing, and streamlined deployment pipelines — are essential for managing the pace and complexity of hybrid cloud application delivery. Without them, organizations risk accumulating operational debt as they scale their containerized workloads.

Red Hat and Hewlett Packard Enterprise announced a joint solution set targeting DevOps adoption in hybrid cloud environments, covering infrastructure, tooling, and professional services:

• Infrastructure: HPE Synergy software-defined infrastructure was positioned as a single platform for running containers on virtual machines and bare metal alongside traditional non-containerized applications.
• Tooling: The HPE Synergy Composer tool, used in conjunction with Red Hat Ansible Tower and Red Hat OpenShift Container Platform, was designed to automate container provisioning and quality-of-service management within a DevOps workflow. It also covered persistent storage provisioning, backup, and restore on HPE 3PAR and HPE Nimble storage arrays, with support for public cloud storage via HPE Cloud Volumes and HPE Cloud Bank Storage. Reference architectures and Ansible playbooks were included for tasks such as high availability, networking, load balancing, OS security, and QoS.
• Professional services: The HPE Pointnext team offered migration services to help customers adapt their existing DevOps practices to Red Hat OpenShift Container Platform for cloud-native apps in hybrid environments.

Automation Keeps the Hybrid Cloud Humming

At the scale of enterprise hybrid clouds, manual management is not viable. Automation is the mechanism by which teams maintain visibility, enforce policy, and reduce operational toil across environments that span on-premises data centers and multiple public clouds.

Red Hat used the 2018 Summit to announce how it planned to integrate CoreOS technologies — acquired earlier that year — into its existing portfolio. The three announced integration tracks were:

• Kubernetes automation: Red Hat announced integration of CoreOS Tectonic, an enterprise Kubernetes distribution, into OpenShift to automate container deployment, scaling, orchestration, and oversight. The CoreOS Operator Framework — a pattern for packaging and managing stateful Kubernetes applications — was also being integrated, with more than 60 software partners reported to have committed to supporting it at the time of the announcement.
• Registry automation: CoreOS Quay, an enterprise container registry, was being integrated into OpenShift to automate geographic replication, security scanning, rollbacks, and image pruning.
• Hosting automation: CoreOS Container Linux, a container-optimized operating system, was being integrated into OpenShift to automate container hosting tasks and align with the broader RHEL and Fedora ecosystems.

Is Hybrid Cloud a Destination or a Waypoint?

The investments described above demonstrate significant industry commitment to hybrid cloud infrastructure. However, analysts at the time — including researchers at Wikibon — argued that hybrid cloud architectures are better understood as a transitional phase than a long-term destination. The costs, operational complexity, and architectural rigidities of maintaining data assets partitioned across on-premises and public cloud environments impose real constraints on agility and economics.

As an organization’s hybrid cloud deployment grows more complex — integrating orchestration platforms, middleware layers, DevOps toolchains, and automation frameworks from multiple vendors — the operational overhead can compound. At some point, the simplicity, provisioning speed, and pay-as-you-go economics of public cloud-native architectures become comparatively attractive. This tension does not invalidate hybrid cloud investment, but it does mean that engineering teams should design their hybrid tooling with eventual cloud consolidation in mind rather than treating heterogeneous environments as a permanent operating model.

The four capability areas examined here — orchestration, middleware, DevOps tooling, and automation — remain relevant to any organization navigating this transition. The specific products and partnerships will continue to evolve, but the architectural problems they address are durable.

Overview: What the Google–Scale Computing Partnership Delivers

Google and Scale Computing announced a partnership to make it significantly easier for small and mid-sized businesses to extend their on-premises data centers into Google Cloud Platform (GCP). The result is a joint platform called Cloud Unity, which bridges Scale’s HC3 hyper-converged infrastructure software with Google Compute Engine.

The target audience is clear: organizations that lack the engineering resources to architect and manage a complex hybrid cloud environment on their own. For those businesses, Cloud Unity aims to reduce the operational overhead of maintaining a separate, dedicated disaster recovery site.

What Is HC3 and How Does Cloud Unity Work?

Scale Computing’s HC3 is a hyper-converged infrastructure (HCI) product that merges compute, storage, and virtualization into a single, tightly integrated appliance. Rather than managing separate servers, storage arrays, and hypervisors, HC3 consolidates these layers into a cluster that is managed through a single interface.

Cloud Unity extends this model into Google’s infrastructure:

• Google-hosted cluster: Instead of purchasing additional physical hardware for a disaster recovery site, an organization can build an HC3-compatible cluster using Google Compute Engine resources.
• SD-WAN connectivity: Cloud Unity establishes a software-defined WAN (SD-WAN) connection between the on-premises Scale environment and the Google-hosted cluster, so the remote GCP environment appears as just another cluster on the same local area network.
• VXLAN encryption: Traffic between the customer’s site and Google’s data center is encrypted using VXLAN, providing a secure overlay network tunnel without requiring dedicated hardware VPN appliances.

The practical effect is that a small business can have a live, cloud-hosted mirror of its data center that behaves like an on-site cluster — without hiring specialized cloud networking staff to build and maintain it.

Why This Matters for Disaster Recovery and Backup

Traditional disaster recovery (DR) deployments have a well-known cost problem: dedicated DR hardware sits idle almost all of the time, consuming budget that could otherwise fund new projects. Cloud-based DR changes this equation by replacing a fixed capital expenditure with a variable operational cost — you pay for cloud resources primarily when you actually need them for failover or testing.

For smaller organizations, the specific advantages of Cloud Unity include:

• Lower capital cost: No need to purchase and rack a second set of physical hardware at a colocation facility.
• Operational simplicity: The Google-hosted cluster is managed through the same HC3 interface as on-premises nodes, reducing the learning curve.
• Elastic capacity: Cloud resources can be scaled up during a real disaster event and scaled back down during normal operations.
• Reduced idle cost: DR infrastructure no longer needs to run at full capacity around the clock just to be available.

Google Expands Its Hybrid Cloud Strategy

This partnership reflects a broader strategic shift for Google Cloud. Through most of its early cloud history, Google positioned GCP as a pure public cloud offering — a contrast with Microsoft Azure and, to a lesser extent, Amazon Web Services, both of which had made earlier investments in hybrid and on-premises integrations.

The Scale Computing deal, alongside a concurrent partnership with Nutanix (another major HCI vendor), signals that Google recognized an opportunity it had been slower to pursue: meeting enterprise and mid-market customers where their workloads actually live, rather than requiring a full migration to the public cloud.

Hybrid cloud is particularly important for organizations with regulatory constraints, latency-sensitive applications, or significant investments in existing on-premises infrastructure that cannot be migrated quickly or economically.

Scale Computing’s Technology Stack: KVM Over VMware

One notable technical detail is Scale Computing’s choice of hypervisor. Rather than VMware ESXi — the dominant hypervisor in enterprise data centers — Scale’s HC3 platform is built on KVM (Kernel-based Virtual Machine), the open-source Linux hypervisor. This keeps licensing costs lower and avoids VMware’s per-socket or per-core pricing, which is a meaningful advantage for cost-sensitive small and mid-market buyers.

The tradeoff is that HC3 operates in a different ecosystem from the large VMware installed base. Organizations already standardized on VMware vSphere would face a platform change if they adopted Scale’s solution — a factor that limits Scale’s ability to displace incumbents in large enterprises where HPE, Dell, and VMware have deep relationships.

However, for businesses that are not yet deeply committed to VMware, or that are evaluating HCI for the first time, the open-source foundation is a selling point rather than a limitation.

Key Takeaways

• Cloud Unity integrates Scale Computing’s HC3 HCI platform with Google Compute Engine, making GCP appear as a native cluster extension of an on-premises environment.
• SD-WAN and VXLAN are the core networking technologies enabling seamless, encrypted connectivity between sites.
• The solution primarily targets small and mid-sized businesses that need affordable disaster recovery without dedicated DR hardware.
• The partnership represents Google’s strategic move toward hybrid cloud, complementing a similar deal with Nutanix made around the same period.
• Scale Computing uses KVM rather than VMware, keeping costs lower but limiting direct interoperability with VMware-centric enterprise environments.

Cloud Is Reshaping Enterprise Business Intelligence

As once-common objections to cloud adoption — particularly around security — began to ease in the mid-2010s, organizations started identifying which IT and business initiatives would benefit most from cloud deployments. Business intelligence (BI) and analytics emerged as one of the clearest candidates. Research from that era consistently pointed toward cloud becoming the dominant delivery mechanism for analytics workloads. According to a joint Deloitte, EMA, and Informatica State of Cloud Analytics Report, 70.1% of respondents already considered cloud an essential part of their analytics strategy.

The 17 statistics below — drawn from leading research firms and industry surveys circa 2016–2017 — paint a picture of the momentum that was building behind cloud BI, and provide useful historical context for understanding how the market evolved.

Market Growth and Spending Trends

• Enterprise cloud spending was forecast to grow at a 16% compound annual growth rate (CAGR) between 2016 and 2026. — Wikibon 2017 True Private Cloud Forecast and Market Shares
• Adoption of cloud BI solutions grew 50% over three years, from 29% to 43%. — BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends 2017
• By 2020, more than $1 trillion in IT spending was projected to be directly or indirectly affected by the shift to cloud. — Gartner Market Insight: Cloud Shift — The Transition of IT Spending from Traditional Systems to Cloud

Strategic Adoption and Executive Priorities

• By 2020, any non-cloud strategy for new IT initiatives was expected to require formal justification at more than 30% of large enterprises. — Gartner Predicts 2017: Cloud Computing Enters Its Second Decade
• By 2021, more than half of global enterprises already using cloud were forecast to adopt an all-in cloud strategy. — Gartner Predicts 2017: Cloud Computing Enters Its Second Decade
• 74% of technology CFOs said cloud computing would have the most measurable business impact in 2017. — 2017 BDO Technology Outlook Survey
• CIOs ranked (1) security, (2) cloud computing, and (3) big data analytics and mobile as their top 2017 IT budget priorities. — Nomura Holdings CIO Survey Research

Why Organizations Are Moving BI to the Cloud

Understanding the motivations behind cloud BI adoption helps explain why momentum built so quickly. Research from KPMG and Deloitte identified both technical and business drivers.

Business Drivers

• The three top reasons CIOs adopted cloud were: (1) improve agility and responsiveness, (2) accelerate product development and innovation, and (3) reduce costs. — KPMG, Journey to the Cloud: The Creative CIO Agenda
• 29% of CIOs cited data center modernization as their primary reason for cloud adoption. — KPMG, Journey to the Cloud: The Creative CIO Agenda
• The top three business drivers of cloud analytics adoption were: enhancing business processes (23.5%), improving customer experience (22.2%), and better collaboration (19.1%). — Deloitte, EMA and Informatica State of Cloud Analytics Report

Technical Drivers

• The top three technical factors driving cloud analytics adoption were: reducing costs (19.5%), data security (15.1%), and collaboration (14.4%). — Deloitte, EMA and Informatica State of Cloud Analytics Report

Cloud BI Adoption and Organizational Readiness

• Cloud was an essential part of the analytics strategy for 70.1% of organizations; a further 21.6% said it was necessary as part of their analytics adoption path. — Deloitte, EMA and Informatica State of Cloud Analytics Report
• Nearly 50% of executive management teams viewed cloud BI as critical or very important to their information needs. — 2017 Cloud Computing and Business Intelligence Market Study, Dresner Advisory Services
• Financial services, higher education, business services, and retail were the four industries placing the greatest importance on cloud BI. — 2017 Cloud Computing and Business Intelligence Market Study, Dresner Advisory Services

How Organizations Use Cloud BI

• The top three cloud BI use cases were: dashboard-based reporting (76%), ad-hoc analysis and exploration (57%), and dashboard authoring (55%). — BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends 2017
• Top planned investments in cloud BI for the coming year included advanced and predictive analytics (53%), operational planning and forecasting (44%), and strategic planning and simulation (44%). — BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends 2017
• 69% of companies planned to increase their use of cloud BI, and more than one quarter of companies that had not yet implemented cloud BI said they would do so in the coming year. — BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends 2017

Key Takeaways for DevOps and Data Engineering Teams

Even viewed as historical data, these statistics underscore a structural shift that has continued well beyond 2017. Several patterns remain relevant for teams architecting modern data and analytics platforms:

• Agility and speed-to-insight outweigh pure cost savings as the primary justification for cloud BI — cost is a secondary benefit, not the lead argument.
• Security perception shifted from barrier to driver: by 2017, data security was already listed as a factor motivating cloud adoption, not blocking it.
• Dashboard reporting dominates near-term use cases, but predictive analytics investment was already the top planned priority — a trend that has accelerated significantly with modern ML platforms.
• Collaboration and cross-team access consistently appeared in both business and technical driver lists, pointing to the importance of governed, shared data platforms over siloed on-premise warehouses.

For teams evaluating or evolving their current cloud analytics stack, these foundational drivers — agility, collaboration, cost efficiency, and advanced analytics readiness — remain valid criteria for architectural decisions today.

Overview: DevOps for Federal Government

Delivering software quickly inside the U.S. federal government is notoriously difficult. Agencies must satisfy a dense web of compliance obligations — FedRAMP, FISMA, NIST SP 800-53, and agency-specific systems development lifecycle (SDLC) requirements — before any workload can reach production. eGlobalTech, a federal IT services firm, built its DevOps Factory framework specifically to reconcile agile delivery speed with those compliance demands.

After roughly a year of pilot testing at agencies including the Department of Health and Human Services (HHS) and the Federal Emergency Management Agency (FEMA), eGlobalTech made DevOps Factory broadly available to the public sector.

What Is DevOps Factory?

DevOps Factory is an end-to-end framework and toolchain designed to move federal agencies from idea to production release as quickly and securely as possible. According to Rajiv Kadayam, Senior Director of Technology Strategy at eGlobalTech:

“The ultimate core objective of DevOps Factory is the rapid transformation of nascent business ideas and requirements into functioning production release offerings.”

The framework was explicitly designed to align with federal policies and standards, including:

• SDLC deliverables — structured lifecycle documentation and governance checkpoints
• FedRAMP — the Federal Risk and Authorization Management Program for cloud security authorizations
• FISMA — the Federal Information Security Management Act
• NIST SP 800-53 — the security and privacy control catalog for federal information systems

The goal is to give agencies security and flexibility while enabling them to “continuously iterate and scale with changing business needs,” Kadayam said.

Two Core Components: Espial and Cloudamatic

Espial: Shift-Left Penetration Testing

One of the most distinctive elements of DevOps Factory is Espial, a tool that automates and integrates penetration testing directly into the CI/CD pipeline. Traditional federal IT projects often defer penetration testing to an independent third party immediately before deployment — a practice that surfaces vulnerabilities far too late to fix cheaply.

Espial shifts that testing left, triggering early detection and remediation during development rather than at deployment gates. As Kadayam explained:

“Rather than having penetration testing done right before deployment by an independent third party — we still have that — we wanted to have early visibility to those issues that are typical in a web application project. We want to be able to detect those issues early in the process, and this tool helps us to do just that.”

Importantly, Espial does not replace the independent third-party assessment required for FedRAMP authorization — it supplements it by catching common web application vulnerabilities continuously throughout the development cycle.

Cloudamatic: Cloud Deployment Automation

Espial works alongside Cloudamatic, which eGlobalTech launched in 2016. Cloudamatic is compatible with Amazon Web Services (AWS) and Microsoft Azure and accelerates the automatic deployment of secure applications onto cloud platforms by handling:

• Provisioning and configuring cloud infrastructure
• Orchestrating complex, multi-component software configurations
• Managing virtual private cloud (VPC) topologies and security group/firewall settings
• Interconnecting systems across agency boundaries

Much of this orchestration happens behind the scenes, allowing developers to focus on writing and pushing code while the platform handles the complex infrastructure engineering underneath.

Real-World Results: HHS and FEMA

HHS: Cloud Release Times Cut from Hours to Minutes

At the Department of Health and Human Services, DevOps Factory reduced the time required to release workloads to the cloud from four to five hours down to approximately 30 minutes, according to Kadayam. This order-of-magnitude improvement in deployment speed is a direct result of automating provisioning and compliance checks that previously required manual intervention.

FEMA: Scalable Geospatial Disaster Management

At FEMA, the use case is more operationally critical. DevOps Factory supports a cloud-based geospatial disaster management environment used by all FEMA regions, the Army Corps of Engineers, and the Department of Homeland Security to coordinate responses to natural disasters. The platform enables users to publish and consume geo-coded data in near real time.

Elastic scalability is essential here. As Kadayam noted: “When an emergency happens, usage really spikes.” The platform must absorb sudden, unpredictable surges in demand — a scenario where manual infrastructure management would be untenable.

eGlobalTech also implemented a fully electronic change management process for FEMA developers using Jira (Atlassian’s issue-tracking tool), giving developers visibility into appeals and recordkeeping without the overhead of document-heavy, meeting-intensive approval workflows.

Security by Design: FedRAMP and FISMA Compliance

Because eGlobalTech supported FedRAMP from the framework’s inception, it was able to bake security and compliance directly into the DevOps Factory architecture rather than bolting them on afterward. This “security by design” approach includes:

• Automated enforcement of NIST SP 800-53 security controls
• Continuous compliance monitoring integrated into the CI/CD pipeline via Espial
• Electronic audit trails and governance records to satisfy FISMA documentation requirements
• Support for multi-VPC architectures with segmented security and firewall configurations

As Kadayam described it, the goal is to make traditionally paper-heavy compliance processes electronic: “Our approach is to be able to make a lot of those more and more electronic.”

Who Is DevOps Factory For?

DevOps Factory targets agile developers within federal agencies who need to push code changes to development and testing environments independently, without waiting on manual infrastructure or compliance processes. The framework gives those developers a self-service experience while the platform automates the complex, regulated engineering happening underneath — orchestrating deployments, managing cloud networking, and maintaining compliance posture continuously.

The framework’s flexibility is a deliberate design choice. As Kadayam put it: “The idea of DevOps Factory is to provide flexibility to agencies” in meeting their specific systems engineering and development lifecycle guidelines.

Key Takeaways

• Compliance and speed are not mutually exclusive — with the right automation, federal agencies can achieve both.
• Shift-left security testing (Espial) surfaces vulnerabilities during development, reducing costly late-stage remediation without replacing required independent assessments.
• Cloud deployment automation (Cloudamatic) handles complex multi-cloud provisioning, freeing developers from infrastructure toil.
• Electronic change management using tools like Jira can replace document-heavy governance workflows without sacrificing auditability.
• Elastic scalability is operationally critical for government systems that must respond to unpredictable demand spikes, such as disaster response platforms.

Original reporting by GCN.com. All quoted statements are attributed to Rajiv Kadayam, Senior Director of Technology Strategy at eGlobalTech, as reported by GCN.

Why Private and Hybrid Clouds Matter for the Enterprise

Public cloud adoption often grabs headlines, but private and hybrid cloud deployments continue to drive measurable business outcomes for large enterprises. According to IDG Enterprise’s 2016 Cloud Computing survey, 62% of respondent organizations were running a private cloud — accounting for roughly 23% of their total IT environment — and 26% had deployed a hybrid model combining private and public cloud services.

These are not merely infrastructure choices. The decision to adopt a private or hybrid model has direct consequences for agility, security posture, developer productivity, and the customer experience. Industry experts identify four categories where these benefits consistently materialise.

1. Increased Flexibility

There is no single cloud architecture that suits every workload. A private or hybrid model lets organisations move specific applications, infrastructure components, or workloads to the cloud at a pace and in a configuration that matches real business requirements.

Diana Nolting, formerly a product analyst at Bluelock, framed it as a shift from a one-size-fits-all model to a right-fit approach:

“As businesses begin to formally embrace multicloud strategies, they’re asking the question ‘What’s the right place to run this workload, based on its specific business needs?’ That means select workloads can take advantage of new technologies, like containerisation and microservice architectures, faster — without impacting traditional, incumbent applications that are hosted in their own right-fit infrastructure.”

Diana Nolting, Product Analyst, Bluelock

Moving to a managed cloud services model also frees internal IT staff from routine operational tasks, allowing them to focus on higher-value work. Keynote speaker and author Steve Prentice notes the practical effect: internal teams gain capacity for pressing responsibilities while also gaining access to the efficiencies of the as-a-service marketplace.

At a strategic level, Jan Plutzer, then COO of Apcera, summarised the compounding advantage:

“[Organisations] become more agile, avoid vendor lock-in, and get to leverage the power of the cloud so they can focus on their core business competencies.”

Jan Plutzer, COO, Apcera

2. Reduced Risk

The growth of bring-your-own-device (BYOD) policies and an increasingly sophisticated cybersecurity threat landscape make data governance a board-level concern. Private and hybrid cloud deployments address this by giving organisations finer-grained visibility and control over where data lives and who can access it.

Jessica Marie, then director of product marketing at Electric Cloud, described a common pattern:

“Hybrid cloud can give an organisation more data deployment options. For example, they can deploy an on-premises private cloud to host sensitive or critical workloads but use third-party public cloud providers to host less critical resources such as test and development workloads.”

Jessica Marie, Director of Product Marketing, Electric Cloud

Beyond data placement, hybrid clouds can accelerate compliance programmes. Kayne McGladrey, then director of information security services at Integral Partners, described a case where a customer met a regulatory compliance deadline by integrating their on-site Active Directory with their hybrid cloud environment — enabling rapid rollout of auditing, multifactor authentication (MFA), and privilege management without the cost and complexity of provisioning a separate identity provider.

Hybrid architecture also reduces risk at the infrastructure level. Jan Plutzer noted that when applications become portable across providers, organisations can distribute workloads across multiple cloud vendors — spreading dependency risk while keeping application behaviour consistent across environments. This portability also enables incremental modernisation of legacy applications without undertaking expensive, high-risk refactoring projects.

Cloud consultant Ibrahim Mohammed highlighted another under-appreciated benefit: for many organisations, a well-managed cloud environment is demonstrably more secure than an on-premises deployment, because third-party providers invest in security capabilities at a scale that most enterprises cannot match internally.

James Townsend, president of InfoStrat, pointed to disaster recovery as a concrete outcome: private and hybrid clouds create cost-effective options for reducing data centre footprint and establishing off-site failover to protect against localised outages or disasters.

3. Better Customer Experience

The speed at which organisations can deliver new applications and services directly affects their ability to compete. Cloud infrastructure — particularly hybrid models that balance public-cloud scale with private-cloud data control — enables faster release cycles and richer digital experiences.

Brian Gracely, then director of product strategy at Red Hat, described a pattern emerging among large enterprises:

“Fortune 500 companies are reshaping how they interact with their customers through mobile and interactive web experiences in completely new ways. We’re beginning to see many companies really understand the power of containers and container platforms to leverage multiple cloud resources. They can leverage the scale or geographic reach of public cloud services and maintain control of critical data in private cloud resources, all under their operational control.”

Brian Gracely, Director of Product Strategy, Red Hat

Toby Buechsenschuetz, VP at Maastricht Consultancy Day with SCOPE | 3MA, reported concrete eCommerce results from a cloud-enabled project: the ability to deliver personalised promotions and a customised product catalogue more than doubled conversion rates and tripled mobile revenue.

These outcomes reflect a broader shift. When development teams can provision infrastructure on demand, iterate quickly, and push changes without waiting weeks for hardware provisioning, the pace of customer-facing innovation accelerates proportionally.

4. Improved Collaboration and Productivity

Private and hybrid clouds provide a secure platform for coordinating work across geographically distributed teams, external contractors, and partner organisations — without exposing intellectual property or sensitive data through uncontrolled channels.

A representative of BrainBlender, a group of technology start-ups, described a recurring pattern:

“We have seen positive outcomes in hybrid deployment by allowing many global collaborators to work on projects, bringing in timing-specific skills without exposing their intellectual property. We have seen many businesses utilise [hybrid clouds] to give them better flexibility in adapting to changes in global trends.”

BrainBlender representative

Technical writer Will Kelly highlighted a case study in which a private cloud deployment enabled more than 1,000 engineers to collaborate on industrial machine designs with significantly reduced friction — a straightforward example of cloud infrastructure directly accelerating engineering productivity.

The productivity gains extend beyond engineering teams. Diana Nolting described the organisational shift that happens when business units are empowered to select the best-fit solution for their specific needs:

“The application owner achieves cost efficiency and stability, while the business’s IT team can focus on being a strategic partner to the organisation, rather than operating as a group that simply works to keep the lights on.”

Diana Nolting, Product Analyst, Bluelock

Kevin Jackson, then a technical fellow at Engility Corp., described the endpoint of this maturity curve: the most successful cloud deployments produce genuine alignment between business owners and technology service managers, enabling them to function as mutually supportive partners rather than as separate, often competing, organisational functions.

Key Takeaways

• Flexibility: Hybrid and private clouds let teams place each workload in the environment best suited to its requirements — on-premises, private cloud, or public cloud — without being locked into a single vendor or architecture.
• Risk reduction: Granular data placement controls, integrated identity management, and cloud-native disaster recovery options reduce both security and operational risk.
• Customer experience: Faster provisioning and the ability to combine public-cloud scale with private-cloud data governance accelerates the delivery of customer-facing applications and services.
• Collaboration and productivity: Secure, flexible cloud platforms enable global teams to collaborate effectively while protecting intellectual property and sensitive data.

The common thread across all four categories is pace. Private and hybrid cloud deployments, when designed thoughtfully, compress the time between a business need and the infrastructure or application that addresses it — and that compression is what makes them a genuine enabler of digital transformation rather than simply an infrastructure upgrade.