What Is the Cloud Computing Market?

The global cloud computing market encompasses the delivery of computing services—servers, storage, databases, networking, software, analytics, and intelligence—over the internet. Vendors charge on a pay-as-you-go basis, allowing organizations to scale capacity up or down without large capital expenditure on physical hardware.

Market research firms regularly publish reports that analyze this space by deployment model, service type, geography, and vendor. Understanding these segmentation dimensions is essential for engineers and architects making platform decisions, as well as for procurement and finance teams evaluating vendor lock-in and total cost of ownership.

Cloud Deployment Models

Cloud infrastructure is typically categorized by who controls and accesses the underlying resources:

• Public Cloud: Infrastructure owned and operated by a third-party provider and shared across multiple tenants. Examples include AWS, Microsoft Azure, and Google Cloud. Public cloud offers the greatest elasticity and the lowest entry cost.
• Private Cloud: Dedicated infrastructure operated solely for one organization, either on-premises or hosted by a provider. Private cloud trades some flexibility for greater control over security, compliance, and customization.
• Hybrid Cloud: A combination of public and private environments connected by orchestration, allowing workloads to move between them based on policy, cost, or regulatory requirements. Hybrid is the dominant model for large enterprises.
• Community Cloud: Shared infrastructure provisioned for a specific group of organizations with common concerns (e.g., regulatory compliance in healthcare or financial services). Less common than the other three models, but important in regulated industries.

Cloud Service Categories

Beyond deployment model, cloud services are segmented by the level of abstraction they provide to the consumer:

• Infrastructure as a Service (IaaS): Provides virtualized compute, storage, and networking. The consumer manages the operating system and above. AWS EC2, Azure Virtual Machines, and Google Compute Engine are canonical examples.
• Platform as a Service (PaaS): Provides a managed runtime environment for developing and deploying applications without managing the underlying infrastructure. Examples include Google App Engine, Azure App Service, and Heroku.
• Software as a Service (SaaS): Delivers fully managed software applications over the internet. The provider handles everything from infrastructure to application updates. Examples include Salesforce, Microsoft 365, and Google Workspace.

A fourth category—Function as a Service (FaaS) or serverless computing—has grown significantly since these traditional three were codified, and is now a material segment of many cloud vendors’ revenue.

Key Vendors in the Cloud Computing Market

The cloud market is dominated by a small number of hyperscale providers, with a longer tail of specialist and regional vendors. The following organizations are consistently cited as significant market participants:

• Amazon Web Services (AWS) — the longest-established hyperscaler and largest by market share as of the time of writing
• Microsoft Azure — strong enterprise presence, particularly with organizations already invested in the Microsoft ecosystem
• Google Cloud — significant in data analytics, machine learning, and Kubernetes (Google originated the project)
• IBM Cloud — focused on hybrid cloud and enterprise workloads, strengthened by the Red Hat acquisition
• Oracle Cloud — prominent in database workloads and enterprise application hosting
• SAP — dominant in ERP SaaS delivery
• Cisco Systems — networking infrastructure underpinning both on-premises and cloud environments, plus its own cloud management and security offerings
• Akamai Technologies — content delivery and edge cloud services
• Citrix Systems — virtualization and desktop-as-a-service
• HP / HPE — hybrid cloud hardware and GreenLake cloud services

Vendors such as CA Technologies (now Broadcom), Yahoo, and Flexiant, which appear in older market reports, have either been acquired, pivoted, or reduced their cloud footprint significantly since 2020.

Key Questions for Evaluating Any Cloud Market Analysis

When reviewing a cloud computing market report—whether from an analyst firm or a vendor—engineers and architects should scrutinize the following dimensions:

1. What is the forecast horizon and base year? Market projections published in 2020 with a 2029 horizon should be treated as directional, not precise, given the pace of change in this sector.
2. How is market size defined? Reports vary in whether they count end-user spend, vendor revenue, or infrastructure investment. Compare like with like.
3. Which geographies are included? North American and Western European markets behave differently from APAC and emerging markets in terms of growth rate, regulatory environment, and dominant vendors.
4. What segmentation methodology is used? Some reports conflate IaaS and PaaS, or treat managed services separately from raw cloud infrastructure. Understand the taxonomy before drawing conclusions.
5. Who funded the report? Vendor-commissioned research may emphasize segments where the commissioning vendor is strongest.
6. What are the identified market risks? Credible analysis will address regulatory risk (data sovereignty, GDPR), concentration risk (dependency on one hyperscaler), and security risks such as misconfiguration.
7. Who are the distributors and channel partners? Cloud is increasingly sold through managed service providers (MSPs) and system integrators rather than directly, which affects pricing and negotiation leverage.

Market Dynamics Driving Cloud Adoption

Several structural forces continue to accelerate cloud adoption across industries:

• Operational agility: Cloud enables teams to provision and deprovision resources in minutes, compressing the time from idea to production deployment.
• Cost model shift: Moving from capital expenditure (CapEx) on data center hardware to operational expenditure (OpEx) on cloud consumption aligns IT spending with business activity.
• Global reach: Hyperscalers operate dozens of regions worldwide, allowing organizations to serve users with low latency and meet data residency requirements without building their own facilities.
• Managed services: Databases, message queues, container orchestration, and machine learning platforms are available as managed services, reducing the operational burden on engineering teams.
• Remote work and distributed teams: The shift to distributed workforces accelerated cloud adoption for collaboration tools, VPN alternatives (ZTNA), and cloud-hosted development environments.

Factors That Can Restrain Growth

• Data sovereignty and compliance: Regulations such as GDPR, HIPAA, and sector-specific rules restrict where data can be stored and processed, complicating multi-region cloud strategies.
• Egress costs: Moving large volumes of data out of a cloud provider is expensive. This creates lock-in and can make hybrid or multi-cloud architectures costlier than anticipated.
• Security and misconfiguration risk: The shared responsibility model means organizations bear responsibility for securing their own workloads. Misconfigured storage buckets and overly permissive IAM policies remain leading causes of cloud data breaches.
• Skills gap: Demand for cloud architects, DevOps engineers, and cloud security specialists continues to outpace supply, constraining how quickly organizations can migrate and optimize workloads.
• Cost overruns: Without disciplined FinOps practices, cloud bills can grow unexpectedly. Reserved instance planning, right-sizing, and tagging strategies are essential to cost control.

Summary

The cloud computing market is large, growing, and segmented across multiple dimensions—deployment model, service type, geography, and industry vertical. For DevOps and platform engineering teams, understanding this landscape matters not just for vendor selection but for architecture decisions, security posture, and long-term cost management. Any market report you evaluate should be read critically, with attention to its methodology, funding source, and the pace at which the underlying technology is evolving.

Background: Why Election Security Monitoring Matters

Election infrastructure — including voter registration systems, voter information portals, and back-office administrative networks — represents a high-value target for foreign adversaries and cybercriminals. For years, the Department of Homeland Security’s Cybersecurity and Infrastructure Security Agency (CISA) has worked alongside state and local governments and the non-profit Center for Internet Security (CIS) to monitor the security of election-related data and detect threats before they can cause damage.

What Is Albert Network Monitoring?

Albert is a network security monitoring system developed and operated by CIS. It works by examining internet traffic and connection attempts on networks owned and managed by election officials. Albert sensors are designed to surface both basic and advanced network threats, including:

• Attempted intrusions by foreign state-sponsored actors
• Attacks by cybercriminals targeting election infrastructure
• Malicious traffic patterns and anomalous connection attempts

Data collected by Albert sensors is forwarded in near-real-time to the CIS Security Operations Center (SOC), which is staffed around the clock by expert cybersecurity analysts. This continuous monitoring gives election officials timely alerts so they can respond quickly to potential threats.

The Problem: Albert and Cloud Computing Were Not Compatible

Despite the effectiveness of the Albert monitoring system, a significant gap existed: Albert sensors were not compatible with major cloud computing platforms such as Microsoft Azure. This forced election officials into a difficult trade-off. They could either:

• Migrate workloads to the cloud and gain scalability, cost savings, and built-in cloud security capabilities — but lose Albert’s specialized election-security monitoring; or
• Keep data on local, on-premises servers and retain Albert monitoring — but forgo the operational and security advantages of modern cloud infrastructure.

For resource-constrained county and state election offices, this was a meaningful barrier to modernization.

The Solution: Microsoft Azure Becomes Albert-Compatible

In June 2020, Microsoft and CIS announced a partnership to resolve this compatibility gap. Through collaborative engineering work between Microsoft’s Azure Global engineering team and CIS’s engineering team — in partnership with Microsoft’s Defending Democracy Program — Microsoft Azure became compatible with Albert network monitoring sensors for the first time.

This integration means election officials can now host election-related workloads on Microsoft Azure while still receiving the real-time network security monitoring and threat alerting that Albert provides. They no longer have to choose between cloud capabilities and specialized election security oversight.

Pilot Program: Starting With Florida Counties

The initial rollout of this capability began as a pilot with 14 county Supervisors of Elections in Florida. The pilot was intended to validate the integration in a real-world election environment before broader availability. According to the announcement, Microsoft and CIS planned to extend the capability to additional states and jurisdictions across the United States following the pilot phase.

What This Means for Election Officials and DevOps Teams

For DevOps and infrastructure teams supporting election offices, this integration changes the architectural calculus in several practical ways:

• Cloud migration is no longer a security downgrade: Moving voter registration or election management systems to Azure no longer means losing specialized network threat monitoring.
• Centralized SOC visibility extends to cloud workloads: The CIS SOC can monitor cloud-hosted election infrastructure the same way it monitors on-premises networks, maintaining consistent analyst coverage.
• Operational benefits of cloud are preserved: Teams can take advantage of Azure’s elasticity, managed services, compliance certifications, and built-in DDoS protection alongside Albert’s election-specific monitoring.
• Reduced operational burden on local IT staff: Smaller county election offices with limited IT resources can offload infrastructure management to cloud providers without sacrificing federally-supported security monitoring.

Broader Context: Securing Election Infrastructure

This partnership is one component of a larger ecosystem of efforts to harden U.S. election infrastructure. CISA, the Election Assistance Commission, state election offices, and technology vendors have all been increasing investment in election cybersecurity since at least 2016. Making cloud platforms compatible with established monitoring tools like Albert represents a pragmatic approach — extending proven security controls into modern infrastructure rather than requiring election offices to choose between security and modernity.

The announcement noted that Microsoft intended to share further details in subsequent months about additional work to help secure the 2020 elections and future elections in the United States and internationally.

Key Takeaways

• CIS’s Albert Network Monitoring system provides near-real-time threat detection for election infrastructure, feeding into a 24/7 staffed SOC.
• Prior to this partnership, Albert sensors were incompatible with cloud platforms, forcing election offices to choose between cloud benefits and Albert monitoring.
• Microsoft Azure is now compatible with Albert, eliminating that trade-off for the first time.
• The capability launched as a pilot with 14 Florida county election offices in June 2020, with plans for broader U.S. availability.
• The integration was produced by joint engineering work between Microsoft’s Azure Global team, CIS, and Microsoft’s Defending Democracy Program.

UK public sector bodies including the NHS will be offered discounted rates for Google Cloud services under a new government agreement.

NHS organizations will be among the UK public sector bodies eligible for a discount to Google Cloud services under a new agreement struck between the software giant and the UK government.

A memorandum of understanding (MoU) between Google Cloud and the UK’s Crown Commercial Service (CCS) will see local councils, charities, NHS and government departments and other qualifying public sector bodies able to apply for discounted rates to the Google Cloud Platform.

TOP CLOUD PROVIDERS

Top cloud providers in 2020: AWS, Microsoft Azure, and Google Cloud, hybrid, SaaS players
Top cloud providers in 2020: AWS, Microsoft Azure, and Google Cloud, hybrid, SaaS players

The cloud computing race in 2020 will have a definite multi-cloud spin. Here’s a look at how the cloud leaders stack up, the hybrid market, and the SaaS players that run your company as well as their latest strategic moves.

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Eligible organizations will be offered access to Google Cloud’s full suite of services, including infrastructure, analytics, artificial intelligence, application modernization and development, and collaboration solutions.

SEE: Google Cloud VP discusses unprecedented computing needs because of the coronavirus (TechRepublic)

It also covers access to Google Cloud’s managed and serverless offerings, including its hybrid and multi-cloud Anthos platform.

The advertising to cloud software giant said the agreement would “provide the best value and terms for public sector agencies investing in cloud technology” while encouraging innovation and digital transformation in the UK public sector.

Mark Palmer, head of public sector EMEA at Google Cloud, said: “This is a significant milestone for us, as we see the results of our focused investment in cloud services and solutions primed and tailored for the public sector.”

Neither Google nor the CCS was able to offer specific financial details on the level of discount that would be offered, although Google said discounts would be based on aggregated cloud service demand and expected spend.

The MoU follows a similar agreement struck between the UK government and Microsoft in late April, which sees all eligible public sector organizations in the UK offered discounted access to Microsoft Azure business tools.

Google, Microsoft and Amazon all offer cloud services to the UK public sector under the UK government’s G-Cloud framework, under which public sector bodies can purchase cloud services from a ‘digital marketplace’ without having to run a full procurement process.

SEE: Google wants to make it easier to analyse health data in the cloud

In the NHS, Google has agreements with a handful of organizations, a number of which were transferred over to the tech giant following its acquisition of British artificial intelligence firm, DeepMind.

Royal Free London NHS Foundation Trust is using the Google Cloud Platform as part of its work with Streams, an app designed to alert doctors to early signs of acute kidney injury in patients and prevent their health from deteriorating further.

Google said its latest agreement marked the beginning of “an expanded relationship” with the CCS that would also focus on providing support for UK SMEs that partner with public sector agencies, “no matter where they are in their cloud adoption journeys.”

Palmer added: “The UK public sector is a major focus for Google Cloud, and this is an opportunity to further support Her Majesty’s government in their digital transformation.”

Bengaluru: DevOps Enabler & Co., the company behind an exclusive startup bundle for organizations that want to scale their operations, today announced it has attained a gold Microsoft DevOps And Cloud Platform competency, demonstrating a “best-in-class” ability and commitment to meet Microsoft Corp. customers’ evolving needs in today’s mobile-first, cloud-first world and distinguishing itself within Microsoft’s partner ecosystem.

DevOps Enabler & Co., the company behind an exclusive startup bundle for organizations that want to scale their operations, today announced it has attained a gold Microsoft DevOps And Cloud Platform competency, demonstrating a “best-in-class” ability and commitment to meet Microsoft Corp. customers’ evolving needs in today’s mobile-first, cloud-first world and distinguishing itself within Microsoft’s partner ecosystem.

To earn a Microsoft competency, partners must successfully complete exams (resulting in Microsoft Certified Professionals) to prove their level of technology expertise, and then designate these certified professionals uniquely to one Microsoft competency, ensuring a certain level of staffing capacity. They must also submit customer references that demonstrate successful projects, meet a performance (revenue and or consumption/usage) commitment (for most gold competencies), and pass technology and/or sales assessments.

The DevOps Enabler and Co. startup bundle provides customers with cloud migration and enablement, DevOps implementation, remote working security, and migration of their applications to containers.

DevOps Enabler & Co., an ISO 27001:2013 certified organization, provides software delivery automation to help companies grow their business. With expertise in cloud computing and DevOps, the organization helps its customers manage cloud platforms, provides DevOps automation, big data implementation, and management and staffing services. The Microsoft DevOps and Cloud Platform Gold Competency provides DevOps Enabler & Co. with access to Microsoft technical team members and advisors who can help it make the most of Microsoft technologies for its clients.

“This Microsoft Gold DevOps and Cloud Platform competency is a demonstration of our testament to providing nothing but the best to our customers, even in these uncertain times. It shows our commitment to the technology market and the skill and expertise of Microsoft products that we share,” said Ram Suresh, Vice President for DevOps Enabler & Co. “This gold competency shows how our teams have the knowledge and experience to accelerate our customers’ success. It is a major step in our journey, as it brings the power of DevOps and the cloud to our Windows community and especially our Microsoft-based enterprise customers.”

“By achieving a gold competency, partners have demonstrated the highest, most consistent capability and commitment to the latest Microsoft technology,” said Gavriella Schuster, corporate vice president, One Commercial Partner (OCP) at Microsoft Corp. “These partners have a deep expertise that puts them in the top of our partner ecosystem, and their proficiency will help customers drive innovative solutions.”

The Microsoft Partner Network helps partners strengthen their capabilities to showcase leadership in the marketplace on the latest technology, to better serve customers, and to easily connect with one of the most active, diverse networks in the world.

DevOps Enabler & Co. is focused on providing the best of DevOps services to help its customers in the best way possible. The startup bundle isn’t just a consultation; it’s a holistic growth solution designed for companies that want to scale quickly and tackle the challenges that come their way. DevOps Enabler & Co. provides software delivery automation, cloud enablement and management, and big data and staffing services to its customers.

Remote Work and DevOps: An Unlikely Acceleration

For many organizations, DevOps has long felt aspirational rather than operational. The DevOps Institute has reported that more than half of organizations still struggle to scale DevOps effectively — and that was before distributed work became the default. When teams suddenly went remote, the conventional wisdom was that alignment between development and operations would become harder, not easier.

What many teams discovered instead was the opposite: remote work quietly removed several of the cultural obstacles that had been holding DevOps back. Understanding why requires stepping back from tooling and automation, and focusing on what DevOps is really about — communication, collaboration, and shared ownership of outcomes.

Why Physical Proximity Was Holding DevOps Back

One of the most persistent barriers to DevOps adoption is organizational silos. When development teams and operations teams sit in separate parts of an office — or worse, separate floors or buildings — an implicit “us versus them” dynamic tends to form. Informal clusters reinforce tribal thinking. Hallway conversations happen within teams, not across them.

Remote work dissolves those physical boundaries. When every team member joins the same video call or Slack channel, proximity bias disappears. A developer in one city and an SRE in another are, from a collaboration standpoint, on equal footing. The playing field is flattened by default.

This structural change matters because DevOps transformation is fundamentally a people and culture problem, not a technology problem. Introducing CI/CD pipelines or infrastructure-as-code does not automatically produce a DevOps culture. Genuine cross-functional collaboration does.

How Distributed Work Drives Cross-Functional Collaboration

In a remote environment, collaboration must be intentional and structured. Spontaneous hallway conversations no longer derail processes or create information asymmetry. Instead, communication happens through scheduled meetings, video calls, and persistent messaging channels — all of which are inherently more inclusive and auditable.

This intentionality aligns well with core DevOps principles:

• Transparency: Decisions made in Slack threads or recorded video calls are visible to the whole team, reducing the hidden knowledge that fuels silos.
• Shared responsibility: When everyone joins the same digital workspace, ownership of the full software delivery lifecycle becomes less territorially guarded.
• Continuous feedback: Asynchronous communication tools make it easier to document retrospectives, post-mortems, and process improvements in a form the entire organization can reference.

The economic pressures that accompanied the shift to remote work also played a role. Faced with urgency to innovate and serve rapidly changing customer needs, organizations began treating DevOps as a strategic necessity rather than an IT experiment. That executive buy-in — historically one of the hardest things to secure — became far easier to obtain when digital delivery was the only delivery channel available.

The Risk: Reverting When the Office Reopens

The critical question is whether the cultural gains from remote work will survive a return to the office. History suggests that organizational culture is sticky in both directions — it is hard to change, but it is also hard to un-change once genuinely embedded.

CIOs and engineering leaders have a window of opportunity. The collaborative habits formed during distributed work can be preserved if leadership actively reinforces them rather than defaulting to pre-pandemic norms. The risk is that without deliberate effort, physical co-location will gradually rebuild the silos that remote work dissolved.

4 Practical Tips for CIOs to Sustain a DevOps Culture

Whether your organization is fully remote, hybrid, or returning to the office, these four practices can help preserve the collaborative culture that accelerated DevOps adoption.

1. Prioritize Video Over Voice

Video calls are significantly more effective than audio-only communication for cross-functional collaboration. Non-verbal cues — body language, facial expressions, engagement — are visible, which reduces misunderstandings and keeps participants more accountable. Where possible, default to video for team meetings and cross-functional syncs, even after returning to a shared office for some sessions.

2. Resist the Pressure to Rush Back to Old Habits

If asynchronous communication and distributed collaboration have improved how your teams work, do not abandon those practices simply because the office is available again. Gather feedback from your engineers, SREs, and product managers. Measure delivery metrics before and after any structural change. Let data and team input guide decisions about how and where work happens, rather than defaulting to pre-pandemic norms.

3. Make Communication a Structural Priority

DevOps is a people transformation before it is a technology transformation. Active listening, clear escalation paths, and regular cross-functional retrospectives are not soft skills — they are engineering infrastructure. Build them into your team rituals deliberately. Document decisions. Share context widely. Treat communication as a first-class engineering concern, not an afterthought.

4. Celebrate DevOps Milestones Visibly

Recognizing progress matters. When a deployment pipeline reduces release cycle time, or when a post-mortem process prevents a repeat incident, call it out. Share the business impact. Connect engineering outcomes to customer or revenue outcomes wherever possible. Visible celebration reinforces the behaviors you want to sustain and gives teams a concrete reason to remain invested in the transformation.

Key Takeaways

• Remote work removed physical silos that were actively blocking DevOps adoption at scale.
• Intentional, structured communication in distributed teams aligns naturally with core DevOps principles.
• The cultural gains from distributed work are not automatic — they require deliberate reinforcement from engineering and business leadership.
• CIOs who treat this period as a foundation rather than a temporary exception will be better positioned to sustain long-term DevOps maturity.

The relationship between remote work and DevOps success is not coincidental. The same disciplines that make distributed teams function well — transparency, shared ownership, structured communication, and continuous improvement — are the disciplines that make DevOps work. Leaders who recognize this overlap and protect it will be the ones who carry the transformation forward.

What Are DevOps Tools?

DevOps tools are software platforms and utilities designed to break down silos between development, operations, and quality-assurance teams. By standardising and automating the movement of code through build, test, and deployment pipelines, they accelerate the software delivery lifecycle while reducing human error. Common functional categories include:

• Source control and collaboration – version control systems and code-review platforms
• Continuous integration / continuous delivery (CI/CD) – automated build and release pipelines
• Configuration management and infrastructure-as-code – tools that provision and maintain environments declaratively
• Monitoring and observability – platforms that provide real-time insight into application and infrastructure health
• Container orchestration – systems that manage containerised workloads at scale

Market Overview

The global DevOps tool market experienced sustained expansion heading into 2020, driven by enterprises accelerating their digital transformation programmes. The market spans tooling sold as on-premises software, cloud-based SaaS, and hybrid deployments, and is consumed across virtually every industry vertical — from financial services to healthcare to retail.

COVID-19 introduced both disruption and acceleration. Organisations that had invested in DevOps practices and cloud-native tooling were better positioned to shift their workforces to remote work quickly, maintain deployment velocity, and adapt products to rapidly changing customer needs. This dynamic is widely credited with accelerating cloud and DevOps adoption among organisations that had been slower to modernise.

Key Market Drivers

Several forces are pushing enterprise investment in DevOps tooling:

• Enterprise digitalisation – Organisations across manufacturing, retail, and financial services are automating business processes, increasing demand for reliable software delivery pipelines.
• Agile and cloud adoption – The widespread shift to agile methodologies and cloud-native architectures requires tooling that supports iterative, frequent releases.
• Collaboration between IT disciplines – Growing pressure to reduce mean time to recovery (MTTR) and accelerate feature delivery is pushing teams toward shared toolchains and shared responsibility models.
• IoT, mobility, and BYOD initiatives – Expanding device ecosystems increase the complexity of software environments, making automated testing and deployment more valuable.
• Cost reduction through automation – Automating repetitive tasks in testing, provisioning, and deployment delivers measurable reductions in engineering overhead.
• R&D investment by major vendors – Leading platform vendors continue to invest in new capabilities — particularly around AI-assisted operations (AIOps), security integration (DevSecOps), and low-code pipeline configuration.

Market Restraints

Despite strong tailwinds, the market faces meaningful challenges:

• Skills shortage – Demand for engineers with deep DevOps expertise consistently outpaces supply, slowing adoption particularly in mid-market organisations.
• Lack of standardisation – The DevOps toolchain ecosystem remains fragmented. Organisations frequently assemble bespoke stacks from dozens of point solutions, increasing integration complexity and total cost of ownership.
• Cultural resistance – Tooling adoption alone does not produce DevOps outcomes; organisational change management is a persistent barrier that technology vendors cannot solve on their own.

Market Segmentation

By Deployment Mode

Cloud-based (SaaS) DevOps tools are projected to record the strongest growth across the forecast period. The primary reasons are lower upfront capital expenditure, elastic scalability, built-in high availability, and reduced operational burden on internal IT teams. Cloud deployment also aligns naturally with modern containerised and microservices architectures, making it the default choice for greenfield projects and cloud-migration programmes.

By Enterprise Size

Large enterprises account for a significant share of market revenue. Their complex, multi-team engineering organisations and high deployment frequency create both the need and the budget for comprehensive, integrated DevOps platforms. However, mid-market and SMB adoption is growing as SaaS delivery models reduce the barrier to entry.

By Industry Vertical

The IT sector is the largest consumer of DevOps tooling — unsurprisingly, given that software delivery is the core business. Banking, Financial Services, and Insurance (BFSI) is the second-largest vertical, driven by the rapid growth of online and mobile banking, rising consumer expectations for digital services, and the competitive pressure to release features faster than fintech challengers.

Regional Analysis

North America and Europe

North America holds the largest share of global DevOps tool market revenue, reflecting the density of large technology companies, mature cloud infrastructure, and early enterprise adoption of agile and DevOps practices. Europe follows, supported by significant digital investment across financial services, manufacturing, and the public sector, as well as active regulatory pressure to improve software quality and auditability.

Asia Pacific

Asia Pacific represents the fastest-growing regional market. Rapid smartphone penetration, expanding digital infrastructure, and technology investment by governments and large enterprises in China, India, Japan, and South-East Asia are all contributing to rising demand for DevOps tooling. The region’s large base of software development talent is also a structural advantage for DevOps adoption.

Competitive Landscape

Key vendors operating in the global DevOps tool market are pursuing several strategic approaches to maintain and grow market position:

• Mergers and acquisitions – Platform vendors are acquiring specialist point-solution providers to broaden their toolchain coverage and reduce the integration burden on customers.
• Partnership ecosystems – Collaboration with cloud hyperscalers, system integrators, and consultancies extends market reach and embeds products into large enterprise programmes.
• Innovation labs and centres of excellence – Investment in internal research and partnerships with academic institutions is producing advances in AI-driven testing, predictive deployment risk assessment, and automated remediation.

COVID-19 Impact on the DevOps Tool Market

The pandemic accelerated several trends already present in the market:

• Remote-first engineering – Distributed teams increased reliance on cloud-hosted CI/CD pipelines, collaborative code-review platforms, and chat-integrated deployment workflows.
• Faster digital product iteration – Businesses pivoting to digital channels needed to ship new features and integrations at unprecedented speed, raising the value of automated delivery pipelines.
• Budget reallocation – Some organisations deferred on-premises hardware refreshes in favour of cloud-based tooling, accelerating the SaaS deployment trend.
• Security scrutiny – The sudden expansion of remote access surfaces increased focus on DevSecOps — integrating security scanning and policy enforcement directly into the delivery pipeline.

Key Takeaways

The global DevOps tool market in 2020 is characterised by strong structural growth, a fragmented but consolidating vendor landscape, and an accelerating shift to cloud-based delivery. The COVID-19 pandemic acted as a forcing function for digital transformation, benefiting organisations with mature DevOps practices while exposing gaps in those still reliant on manual, on-premises processes. For engineering leaders, the near-term priorities are toolchain consolidation, investment in developer skills, and integrating security earlier in the software delivery lifecycle.

Source: Originally reported by 3wnews.org, citing research by Trusted Business Insights.

Development Activity Held Steady—But Hours Increased

In the wake of the COVID-19 pandemic, the overall volume of software development activity remained relatively constant, even as developers shifted almost entirely to working from home. A GitHub report from the period showed a brief, regional dip in activity as the pandemic spread, followed by a quick recovery on the public GitHub repository.

More notably, the time each developer spent on the platform increased. Kelly Stirman, Vice President of Product Strategy and Marketing at GitHub, attributed this in part to developers no longer commuting and to extended working days that blended professional and personal tasks. His conclusion was straightforward: “More time to code increases productivity.”

Whether that extra screen time translates directly into higher output is harder to measure. Home environments introduce distractions, caregiving responsibilities, and irregular schedules. The data reflects time on platform, not necessarily lines of production-ready code shipped.

Digital Transformation Projects Became Business Continuity Priorities

Providers of other DevOps platforms reported similar upticks in activity during the same period. Moritz Plassnig, Senior Vice President and General Manager for Software Delivery Management and Software Delivery Automation Cloud at CloudBees, observed that many organizations had already been investing in custom application development to drive digital transformation. The pandemic compressed that timeline dramatically.

“The current situation has only accelerated that trend,” said Plassnig. Projects that were once medium-term strategic initiatives became immediate operational necessities, elevating the importance of reliable, scalable DevOps pipelines.

The Core Challenge: Making Sure the Right Code Gets Built at the Right Time

Distributed teams introduce coordination risk. Fred Simon, Chief Data Scientist at JFrog, identified feedback loops as the central problem: “Everyone needs feedback.”

Without the informal communication that happens naturally in a shared office—a quick question at a desk, overhearing a colleague’s conversation—developers can spend time on the wrong feature, duplicate work, or miss a blocking dependency. To compensate, engineering managers turned to:

• Video conferencing tools such as Zoom for synchronous check-ins and code reviews.
• Cloud-hosted DevOps platforms to ensure every team member has consistent access to CI/CD pipelines, artifact repositories, and shared tooling regardless of location.
• Asynchronous communication embedded directly in DevOps workflows, reducing the need to schedule meetings for every decision.

Asynchronous Collaboration Matters More Than Video Calls

Emrah Samdan, Vice President of Product at Thundra—a platform for securing and observing cloud-native microservices—argued that the communication tooling built into a DevOps platform is more important than ever in a remote-first environment. Developers collaborating asynchronously can share code snippets, review pull requests, and leave contextual comments on their own schedule, which often produces more thoughtful feedback than a rushed video call.

Samdan was direct about the hidden cost of synchronous-first culture: “A lot of productivity is being lost setting up Zoom calls.” The implication for DevOps teams is clear—invest in platforms and workflows that make asynchronous collaboration the default, and reserve synchronous meetings for decisions that genuinely require real-time discussion.

Survey Data: Remote Work Is Becoming Permanent—But Reactions Are Mixed

The organizational question is no longer whether remote work is possible, but whether it becomes the default. A PwC survey of chief financial officers conducted around this time found that nearly half of respondents (49%) planned to make remote work a permanent option for roles that support it.

Developer sentiment, according to a GitLab survey of workers from the same period, was split:

• 52% of employees reported being more productive working remotely.
• 48% reported being more efficient remotely.

The flip side of those numbers is significant: roughly half of the workforce did not share that positive view. Remote work is not universally beneficial, and engineering leaders need to account for varying preferences, home environments, and working styles when designing distributed team structures.

Teams With Remote Experience Had a Head Start

Brendan O’Leary, Senior Developer Evangelist at GitLab, pointed out that the pandemic did not create remote DevOps teams from scratch—it accelerated a trend that was already underway. Many software development teams had been gradually shifting toward more distributed work before COVID-19, building the tooling, documentation, and cultural practices needed to sustain it.

O’Leary noted that resources such as the GitLab Remote Playbook saw a significant increase in downloads during the early months of the pandemic, as teams scrambled to build structure around their new reality.

His broader observation is worth carrying forward: “I think it is a time that will see a real separation of bosses and leaders.” Effective engineering leadership in a remote context goes beyond tracking ticket velocity or deployment frequency. It means creating psychological safety, removing blockers asynchronously, and enabling teams to ship innovative software even without physical proximity.

What DevOps Teams Should Take Away

The experience of 2020 made several things clear for DevOps practitioners operating in distributed environments:

• Cloud-hosted tooling is table stakes. Teams dependent on on-premises infrastructure faced avoidable bottlenecks when offices closed. Migrating CI/CD, artifact management, and monitoring to cloud services reduces location dependency.
• Asynchronous-first workflows reduce coordination overhead. Design your processes so that the majority of decisions can be made and documented without scheduling a meeting.
• Feedback loops must be explicit and fast. In a co-located office, feedback is ambient. In a distributed team, it must be deliberately built into every stage of the pipeline—automated tests, code review processes, and deployment notifications all serve this function.
• Not every developer thrives remotely. Blanket policies in either direction—fully remote or fully in-office—will fail some portion of the team. The best outcomes come from intentional flexibility combined with strong async communication norms.
• Leadership matters more, not less, at a distance. Metrics are necessary but insufficient. Leaders who invest in clarity, autonomy, and documented decision-making will outperform those who rely on physical presence as a substitute for genuine management.

DevOps teams that had already adopted distributed practices before the pandemic demonstrated a measurable advantage. Building those practices into the fabric of how a team works—rather than treating them as an emergency adaptation—is the most durable lesson from this period.

What Is the Azure Sphere Research Challenge?

Microsoft launched the Azure Sphere Research Challenge, a time-limited bug bounty program offering approved security researchers individual rewards of up to $100,000 for discovering and demonstrating dangerous exploits against Azure Sphere — its Linux-based platform designed for internet-connected (IoT) devices.

The challenge ran from June 1, 2020 through August 31, 2020 — a three-month window during which vetted researchers could probe the OS for critical vulnerabilities. It represented a significant expansion of Microsoft’s existing Azure Security Lab, which was announced at Black Hat in August 2019 with a top reward of $40,000.

What Is Azure Sphere?

Azure Sphere is Microsoft’s integrated security platform for IoT devices. It consists of three primary components:

• A custom Linux kernel and OS — a purpose-built operating system hardened for constrained IoT hardware.
• A connected microcontroller unit (MCU) — the Azure Sphere-certified chip that runs the OS and application code.
• A cloud-based security service — Microsoft’s backend infrastructure that handles remote device management, software updates, and protection against denial-of-service attacks and unauthorized firmware changes.

Typical Azure Sphere use cases include consumer appliances such as refrigerators and washing machines, as well as industrial IoT equipment that needs secure, remotely managed firmware. Azure Sphere reached general availability in February 2020.

Architecture: Normal World vs. Secure World

Understanding the challenge scope requires familiarity with how Azure Sphere partitions trust levels:

• Normal World — the Linux equivalent of user mode, where third-party applications run. This is comparable to user space in a standard Linux system.
• Secure World — sits below Microsoft’s custom Linux kernel. This is where the Security Monitor executes. Only Microsoft-supplied code is permitted to run in supervisor mode or in Secure World.
• Azure Pluton — Microsoft’s security subsystem and hardware root of trust embedded in the Azure Sphere chip, responsible for cryptographic operations and secure boot.

The highest reward tier — $100,000 — was reserved for researchers who could achieve code execution on Azure Pluton or within Azure Secure World, representing the most severe possible compromise of the platform’s security guarantees.

Reward Tiers and Scope

The challenge offered tiered rewards based on severity and target:

• Up to $100,000 — Code execution on Azure Pluton or Azure Secure World.
• Lower tiers — Other significant vulnerabilities within the defined challenge scope (specific lower amounts were not detailed in the original announcement summary).

Security vulnerabilities discovered outside the challenge’s defined scope — for example, bugs in the cloud-side Azure Sphere infrastructure — could still be submitted for consideration under the broader Azure Bounty Program. However, physical attacks were explicitly excluded from both the challenge and the general Azure Bounty Program.

What Resources Did Microsoft Provide to Researchers?

Accepted participants received a supported research environment, including:

• An Azure Sphere development kit (hardware).
• Access to Microsoft products and services for research purposes.
• Full Azure Sphere product documentation.
• Direct communication channels with the Microsoft engineering and security team.

This level of supported access was designed to give researchers the practical tooling needed to investigate deep vulnerabilities, rather than conducting purely black-box research.

Industry Partners Involved

Microsoft also engaged a number of established security firms with IoT research expertise to participate in the challenge. Named partners included:

• Avira
• Baidu International Technology
• Bitdefender
• Bugcrowd
• Cisco Talos
• ESET
• FireEye
• F-Secure
• HackerOne
• K7 Computing
• McAfee
• Palo Alto Networks
• Zscaler

Why This Program Matters for IoT Security

IoT devices have historically been among the least-secured endpoints in enterprise and consumer networks. Devices with long operational lifetimes, infrequent patching cycles, and direct access to physical infrastructure represent a significant attack surface. A platform like Azure Sphere — which embeds a root of trust at the hardware level and enforces strict code signing — addresses many of these concerns architecturally.

Inviting external researchers to attempt to break these protections under controlled conditions is a sound approach to validating security claims before adversarial actors find the same weaknesses in production deployments. The expanded reward pool (from $40,000 to $100,000) also signals that Microsoft considers the Secure World and Pluton boundaries to be genuinely high-value targets worth significant investment to protect.

As Microsoft noted at the time: “By expanding the Azure Security Lab, we’re providing more content and resources to better arm security researchers with the tools needed to research high-impact vulnerabilities in the cloud.”

Application Timeline (Historical Reference)

For historical reference, the program’s key dates were:

• Application deadline: May 15, 2020
• Challenge period: June 1, 2020 – August 31, 2020
• Microsoft reviewed applications weekly and notified accepted researchers by email.

Note: This challenge has concluded. For current Microsoft bug bounty programs, refer to the official Microsoft Security Response Center Bounty Programs page.

The Evolution from On-Premise to Cloud

Mobile app development teams have not always had the luxury of elastic, remotely hosted infrastructure. Before cloud platforms matured, the standard approach was On-Premise: every server, storage array, and development tool lived in the same physical building as the engineering team. That model created predictable headaches — limited physical space, large upfront capital expenditure, complex licensing, and slow provisioning cycles that blocked development velocity.

The emergence of cloud computing introduced three foundational service models that progressively transferred infrastructure responsibility away from in-house IT teams:

• Infrastructure-as-a-Service (IaaS) — virtualised compute, networking, and storage delivered on demand.
• Software-as-a-Service (SaaS) — fully managed software applications delivered over the internet.
• Platform-as-a-Service (PaaS) — a managed environment that provides the runtime, middleware, development tools, and services a team needs to build, test, and deploy applications without managing the underlying infrastructure.

Each model sits at a different level of the stack, and choosing the right one depends on how much control versus convenience a team needs.

SaaS vs. PaaS: What Changed and Why

SaaS was the first widely adopted cloud model for many organisations. It replaced on-premise software licences with subscription-based, browser-accessible applications — think email, CRM, or collaboration tools. For end users this was transformative, but for development teams building custom mobile applications, SaaS alone was not enough. Developers needed a managed environment where they could write, test, and ship code, not just consume finished products.

PaaS filled that gap. Rather than purchasing and maintaining servers, configuring runtime environments, or negotiating separate tool licences, a development team can provision a PaaS environment and immediately start writing application logic. Key practical advantages PaaS offers over SaaS for mobile development teams include:

• Richer feature set — databases, messaging queues, authentication services, and CI/CD pipelines are available as managed add-ons.
• Reduced operational budget — infrastructure management overhead shifts to the cloud provider.
• Faster time to launch — pre-configured environments eliminate lengthy setup phases.
• Elastic scaling — storage, compute, and bandwidth can be adjusted as the application grows or shrinks.
• Location independence — resources are delivered from data centres irrespective of where the development team sits.

Mobile PaaS: Handling the Heavy Lifting

Mobile app development carries unique complexity compared with traditional web development. A single application may need to run correctly across multiple operating systems (iOS, Android, and various vendor-specific environments), screen sizes, hardware capabilities, and network conditions. That cross-platform burden can consume engineering attention that should be focused on product features rather than environment configuration.

Mobile PaaS (sometimes abbreviated mPaaS) addresses this by providing a managed backend and toolchain specifically optimised for mobile development workflows. The platform abstracts away infrastructure concerns so developers can focus on application logic and user experience.

Multi-Language and Framework Support

A well-configured PaaS environment supports a range of programming languages and frameworks out of the box — commonly including Python, Node.js, Java, and C# — along with popular frontend frameworks and UI toolkits. Development teams can select the language stack that matches their skills and project requirements, then provision the required runtime without manually installing or patching dependencies on physical machines.

Integrated Development Environment (IDE) Capabilities

Many PaaS offerings include or integrate tightly with cloud-hosted IDEs. This means the full development loop — writing code, debugging, running automated tests, and deploying to a staging or production environment — can happen within a single, consistent toolchain. The alternative — context-switching between local IDEs, separate test environments, and manual deployment scripts — introduces friction and increases the chance of environment-specific bugs surfacing only after release.

A cloud IDE also makes it straightforward to spin up sandbox environments for experimenting with new features or architectural approaches without disrupting the main development branch.

Security in a PaaS Model

Security for mobile applications is not limited to the application layer itself. Data in transit between the app and the backend, data at rest on servers, and access control across a distributed team all need to be managed. PaaS platforms typically provide several security capabilities as managed services:

• Centralised authentication — identity providers and OAuth flows managed by the platform, reducing the risk of custom-built authentication mistakes.
• Role-based access control (RBAC) — fine-grained permission assignment so developers, testers, and automated pipelines only access what they need.
• Single sign-on (SSO) — consistent identity management across services.
• Data encryption — encryption at rest and in transit handled by the platform, with key management often delegated to managed key stores.
• Backup and recovery — automated snapshots and point-in-time recovery reduce data loss risk without requiring a dedicated database administrator.

These capabilities allow smaller teams to maintain a security posture that would otherwise require dedicated security engineering resources.

Pay-As-You-Go (PAYG) Pricing Model

Traditional on-premise infrastructure required organisations to provision for peak load, which meant paying for capacity that sat idle during normal operation. PaaS platforms overwhelmingly use a consumption-based or pay-as-you-go pricing model, which aligns costs with actual usage.

For mobile app development specifically, PAYG pricing means:

• Server capacity can be scaled up ahead of a feature release or marketing campaign, then scaled back down afterwards.
• Storage can grow incrementally as the user base grows rather than being purchased in large fixed blocks.
• Teams are not locked into long-term hardware depreciation cycles.
• Licensing costs for middleware, databases, and tools are folded into a single cloud bill rather than managed as separate vendor contracts.

This model removes a significant administrative burden from development teams and gives finance teams more predictable, usage-correlated expenditure.

Choosing the Right Cloud Model for Your Mobile Project

IaaS, SaaS, and PaaS are not mutually exclusive. Many production mobile applications use PaaS for the application runtime and development toolchain, IaaS for specialised compute workloads (such as video transcoding or machine learning inference), and SaaS tools for project management, monitoring, or customer support. The key is understanding what each model manages on your behalf versus what your team retains responsibility for:

For most mobile app development teams, PaaS strikes the best balance: it eliminates low-value infrastructure management while preserving full control over application code and data design. Teams that need more control over OS-level configuration may lean toward IaaS; teams that only need pre-built tools without custom development can rely on SaaS.

Key Takeaways

• On-Premise infrastructure created space, cost, and licensing constraints that slowed mobile development teams.
• IaaS, SaaS, and PaaS each abstract a different layer of the stack, trading control for managed convenience.
• PaaS is particularly well-suited to mobile app development because it provides language runtimes, IDEs, security services, and elastic scaling without requiring teams to manage underlying infrastructure.
• Pay-as-you-go pricing aligns cloud spend with actual usage, removing the over-provisioning problem inherent in on-premise models.
• Security capabilities — authentication, RBAC, encryption, backup — are available as managed platform services on most PaaS offerings.

Why Is Automated Testing Important?

Automated software testing is both a toolset and a process. Tools such as Selenium, Appium, Cucumber, and Silk Test provide the mechanisms needed to execute tests against a software product. Tests can range from simple pass/fail scripts to complex behavioral simulations with large data sets. All of them share the same goal: verify that the software behaves as expected within defined parameters.

From a process perspective, automated testing integrates test execution into the regular development workflow. For example, a new build pushed to a repository can automatically trigger a test suite with no manual developer intervention. Results are logged, compared to previous runs, and surfaced for review. If tests pass, the build advances toward deployment; if they fail, it cycles back for remediation. This pattern is especially relevant in DevOps environments that rely on continuous integration and continuous delivery (CI/CD) pipelines.

Automated testing has not replaced manual QA. It complements it. A realistic automation plan requires a meaningful investment of planning, tooling, and ongoing maintenance. The two approaches work best when each is applied to the tasks it handles most effectively.

Advantages of Test Automation

• Accuracy: The same test runs the same way every time, eliminating human error and oversight.
• Scope: Automation can execute far more tests than a human tester could in the same period.
• Reusability: Once a test is written, its scripts, data, and workflows can be reused across future builds and related software projects.
• Consistent logging and reporting: Automated tools record every result systematically, unlike manual testers who may miss or incompletely document conditions.
• Better bug detection: Comparing results across runs makes it easier to detect regressions and measure fix effectiveness.
• Off-hours execution: Tests can run overnight or on weekends without developer intervention, accelerating feedback cycles.

Challenges of Automated Testing

Despite its advantages, test automation introduces real challenges that teams must plan for.

• Automation is not automatic: Implementing a tool does not produce tests. Developers must define requirements, write scripts, and build workflows before any value is delivered.
• Some tests resist automation: Visual design, layout appeal, and subjective user experience are difficult or impossible to validate programmatically. Rarely used features may not justify the cost of automating.
• Maintenance overhead: As software evolves, test scripts must be updated to stay valid. Outdated scripts produce false failures and erode confidence in the test suite.
• Upfront investment: Building a reliable automated test suite requires significant time and skilled personnel, particularly in the early phases of a project.
• Coordination complexity: Managing automated and manual efforts in parallel, especially under changing priorities, requires discipline and clear ownership.

Manual QA testing retains value precisely where automation struggles. Skilled testers bring subjective judgment, exploratory insight, and the ability to create documentation that supports help desk operations, training materials, and user guides. The best testing strategies use both methods deliberately.

Types of Automated Testing

Automated testing is not a single activity. Multiple test types can be layered or run in sequence to cover different aspects of software quality.

Regression Testing

Regression testing verifies that new code does not break existing functionality. It is typically repeated after every build and is one of the strongest candidates for automation because the same checks must be executed repeatedly over time.

Unit Testing

Unit tests target small, discrete pieces of code such as individual functions, methods, or modules. A passing unit test confirms that the tested code compiles and executes as intended. Unit testing is a cornerstone of test-driven development (TDD), where tests are written before the code they validate.

Smoke Testing

Smoke tests are lightweight go/no-go checks run against a new build. They verify that the most critical features still function — for example, that the application launches, key interfaces open, and core buttons respond. A failed smoke test signals that the build is too broken for deeper testing. These are also called build-verification tests or build-acceptance tests.

API and Integration Testing

API testing validates the request-and-response behavior of an application’s interfaces, covering endpoints such as databases, web services, message buses, and third-party APIs. It checks not only expected inputs and outputs but also edge cases, latency, security headers, and error handling. Integration testing extends this to confirm that multiple modules or components work correctly together — for example, simulating an end-to-end order from entry through billing to shipping.

UI and Input/Output Testing

UI testing exercises the front end of an application, validating that buttons, dialogs, forms, and navigation behave correctly. Input/output (I/O) testing supplies known input data and confirms that the resulting output matches expectations, verifying that underlying processing logic is correct. The two are often linked because data is frequently entered through the UI and results are displayed there.

Security and Vulnerability Testing

Security testing checks that data and application logic remain protected against both accidental faults and deliberate attacks. Common vectors tested include SQL injection, cross-site scripting (XSS), and unauthorized access attempts. Vulnerability testing is often performed on the code base before a build runs, scanning for known flaws such as missing error handling or insecure configuration settings. It overlaps with penetration testing to assess overall security readiness.

Performance Testing

Performance tests measure how an application behaves under load. Key indicators include response time, throughput, resource consumption, and scalability. Stress testing pushes the application beyond its normal operating parameters until it fails, establishing a baseline and identifying breaking points before production deployment.

Acceptance Testing

Acceptance tests validate that the software meets the criteria defined in a software requirements specification (SRS) or client documentation. They answer the question: is the project done? Because acceptance criteria can involve subjective or complex judgments, these tests are often difficult to automate and are typically performed late in the development cycle.

How to Perform Automated Testing Effectively

The goal of automation is to reduce the cost and time required to validate software quality without sacrificing accuracy. Organizations should be strategic about what they automate: automation delivers the highest return where test volume is high and test scope is well-defined.

A widely referenced model in agile teams is the test automation pyramid, which prioritizes a large base of fast unit tests, a smaller layer of integration tests, and a thin top layer of UI tests. This reflects the principle that lower-level tests are cheaper to write, faster to run, and easier to maintain than high-level end-to-end tests.

Many teams use record-and-playback tools to accelerate test creation. These tools capture user actions, map them to a library of objects, and generate executable test scripts. Tools with this capability — including Selenium and Ranorex — help QA teams build regression, UI, and integration tests that replicate real user workflows without writing every line of code manually. However, generated scripts still require human review and maintenance.

Practical Best Practices

• Automate fresh environments: Spin up a clean test environment for each test cycle to avoid state pollution from previous runs.
• Minimize variable repetition: Define shared objects and variables once at the top of a test file rather than scattering them throughout, so a single update propagates everywhere.
• Apply version control to tests: Treat test code with the same discipline as application code. Tests should live in version control alongside the software they validate.
• Favor standard languages: Write test scripts in common languages such as Python, Ruby, or Java. Proprietary scripting languages lock teams into a single tool and make migration costly.
• Keep tests singular and autonomous: Each test should validate one thing and be runnable independently. Interdependent tests are fragile and hard to debug.
• Build in logging from the start: Comprehensive, structured logs are essential for diagnosing failures quickly and comparing results across test cycles.

Test Automation Frameworks

A test automation framework is the set of guidelines, coding standards, reporting conventions, processes, and tooling that define the test execution environment. Selecting and configuring an individual tool is not enough; the framework determines how tests are written, stored, executed, and maintained across an organization.

A well-designed framework delivers several benefits:

• Consistent test creation and execution across teams and projects
• Better code reusability, reducing duplicated effort
• Easier onboarding for new testers and developers
• Reduced manual intervention in test execution
• Structured maintenance processes that keep the test suite accurate over time

A framework plan should document who writes and executes tests, who maintains the tools, when specific test types run, where test artifacts are stored, and how test versions are managed. Strong reporting and logging policies are essential so developers can quickly locate results, identify root causes, and prioritize fixes for the next build.

How to Choose an Automated Testing Tool

The market includes dozens of automated testing tools, both commercial and open source. Evaluating them requires a structured approach rather than defaulting to the most popular name.

• Define the problem first: Identify the specific test types you need to support — UI, API, regression, performance, security — and filter tools to those that address your actual requirements.
• Check platform and language compatibility: Confirm that the tool supports your operating systems, application stacks, and programming languages. Also verify integration with your existing version control, CI/CD, and issue-tracking tools.
• Evaluate language requirements: Prefer tools that use languages your team already knows. Tools that require proprietary scripting languages add learning overhead and create migration risk.
• Assess licensing and support: Open source tools reduce license costs but require internal expertise for maintenance and upgrades. Commercial tools offer vendor support but carry ongoing costs and potential vendor lock-in.
• Run a proof of concept: Most vendors offer free trials. Test the tool against real test cases in parallel with your existing tooling, and compare the results and developer experience before committing.
• Plan for a toolbox, not a single tool: Most teams end up with multiple tools to cover different test types — unit, API, UI, performance — across different application types and platforms.

The Future of Test Automation

Automated testing tools are evolving rapidly. Two trends are reshaping what is possible: artificial intelligence and robotic process automation (RPA).

AI and Machine Learning in Testing

AI capabilities are beginning to appear in testing tools, with the goal of autonomously generating test cases, identifying gaps in test coverage, and prioritizing testing effort toward the code areas most likely to contain defects. Machine learning models trained on historical test data can suggest where new tests are needed and flag anomalies that pattern-based scripts would miss. These capabilities are still maturing, but they point toward a future where test creation requires significantly less manual effort for routine cases, freeing engineers to focus on complex, high-judgment scenarios.

Robotic Process Automation (RPA)

RPA technologies are designed to replicate human interactions with software systems, making them applicable to end-to-end testing scenarios that span multiple applications or business processes. In a testing context, RPA can simulate multi-step user workflows across disparate systems — validating the full customer journey rather than individual component behavior. RPA is also emerging in low-code development platforms, where it can automate the assembly and testing of complex business processes built from reusable templates.

Both AI and RPA in testing are still developing. Their long-term value will depend on how effectively they can encode business context — the domain knowledge of what software is supposed to do — into automated processes. Human expertise in understanding business requirements remains essential for guiding these tools toward meaningful tests.

Summary

Test automation is not a switch you flip — it is a discipline that requires planning, investment, and ongoing maintenance. The teams that get the most out of it are those that match the right test types to automation, maintain their test suites with the same rigor as application code, and combine automated tools with skilled manual testers for the work that requires human judgment. Starting with a clear framework, choosing tools that fit your stack and languages, and iterating on your strategy as the software evolves will put your team on a sustainable path toward higher-quality, faster-shipping software.

CircleCI Secures $100 Million to Scale Its CI/CD Platform

San Francisco-based CircleCI announced the close of a $100 million Series E funding round, one of the largest single raises by a DevOps-focused company at the time. The round was led by IVP and Sapphire Ventures, pushing the company’s total funding to $215 million. CEO Jim Rose told CRN the investment would be used to expand the product portfolio, grow the company’s international presence, and formalize enterprise sales motions.

Why CI/CD Attracted This Level of Investment

Continuous integration and continuous delivery (CI/CD) had moved well beyond early-adopter startup culture by 2020. Rose described the shift as a “phase change” that had accelerated over the previous six to eight quarters, with CI/CD evolving from an aspirational practice into a non-negotiable requirement for software teams at enterprises of all sizes.

“Continuous delivery has gone from being a glimmer in a startup’s eye to an aspirational North Star,” Rose said, reflecting the broader industry momentum that made CircleCI an attractive investment target.

CircleCI’s customer base spanned both web-scale technology companies — including Facebook and Spotify — and more traditional enterprises such as Ford and Unilever. The breadth of that customer mix signaled that CI/CD adoption had crossed into mainstream enterprise IT, not just digital-native organizations.

Product Roadmap: Change Validation and Cloud Architecture Complexity

A significant portion of the new capital was earmarked for product development, with a particular focus on change validation capabilities. As organizations adopt increasingly complex cloud-native architectures, the ability to automatically validate and model the downstream effects of code changes becomes critical to maintaining delivery velocity without introducing instability.

“Being able to map and model and take care of all those sources of change is important,” Rose said. “We’re helping customers deal with the complexity of these new modern cloud architectures.”

CircleCI delivers its CI/CD tooling primarily as a Software-as-a-Service (SaaS) platform, which allows development teams to integrate pipelines without managing the underlying infrastructure themselves.

International Expansion: Tokyo and London Offices

CircleCI had already begun building an international footprint before the Series E closed. The company opened an office in Tokyo to serve Asia-Pacific customers and subsequently opened a London office to support European demand. The new funding was intended to accelerate that expansion further.

“We’ve been trying to drive growth with our existing customers and new customers, ranging in all sizes and types,” Rose said.

How CircleCI Fits Into Enterprise Digital Transformation

Rose described a consistent pattern in how large enterprises engage with CircleCI during digital transformation projects. The typical journey unfolds in three phases:

1. Cloud adoption: A systems integrator establishes a relationship and executes the initial migration to cloud infrastructure.
2. Source code consolidation: The organization rationalizes its version control and code management tooling.
3. Pipeline modernization: As development teams increase their delivery cadence, they recognize the need to replace legacy tooling sitting between source control and the data center — which is where CircleCI enters the picture.

“As developers pick up the pace, they realize they then need to replace tooling that sits between the code source and the data center,” Rose said, “and that’s where we get involved.”

Company Background: From Distiller to CI/CD Leader

CircleCI was founded in 2011 and built its early growth on a developer-led, bottoms-up adoption model — individual engineers signing up and using the platform before formal enterprise procurement. That grassroots approach gave the company deep penetration across development teams globally before the enterprise sales layer was formalized.

In 2014, three years after founding, CircleCI acquired Distiller, a startup focused on CI/CD for mobile applications. Jim Rose was CEO of Distiller at the time; his team literally walked across a street in San Francisco to meet with CircleCI leadership, and the two companies agreed to merge after recognizing that the market would eventually demand a comprehensive, multi-platform CI/CD solution. Rose subsequently became CEO of the combined company.

Key Takeaways for DevOps Teams

• The $100 million Series E reflected significant investor confidence in CI/CD as a foundational enterprise software category, not a niche developer tool.
• CircleCI’s dual focus on web-scale internet companies and traditional enterprises illustrated how broadly CI/CD adoption had spread by 2020.
• Investment in change validation capabilities points to a maturation of CI/CD from pipeline automation toward intelligent release governance.
• The SaaS delivery model remained central to CircleCI’s value proposition, reducing operational overhead for engineering teams.
• International expansion — particularly into APAC via Tokyo — signaled that the DevOps tooling market had become genuinely global.

Denis Žoljom, the automation representative for the WordPress theme review team, proposed moving much of the theme review system over to GitHub. The idea is to move the interactive parts of the review away from the current system. The proposal claims such a move would streamline the process via automated tools and make the manual, reviewer-to-author interactions easier.

Currently, the theme review process takes place on Trac. For many theme authors and reviewers, the software can feel archaic. It does not have the feature set that developers have become accustomed to with other solutions. With the move of major parts of WordPress, such as feature plugin development, to GitHub over the past few years, it might be time to reevaluate other areas of the core project.

Žoljom noted in the proposal that reviews handled through Trac are cumbersome. As a former theme review team lead and reviewer, I know how many on the team feel. Right now, much of the review process is manual. It is handled via a lot of back-and-forth communication between the reviewer and author. There is no good way to leave a note or comment on a specific line of code when there is an issue. This ongoing discussion between reviewer and author is sometimes hindered by a language barrier. The experience of comparing changes between code updates is lackluster at best. Plus, the only automated check is handled by the Theme Check plugin.

Handling reviews on GitHub opens a new world of possibilities that could make reviews more efficient and provide a better experience on both ends of the process.

From 2015 through 2019, I ran a side business with a partner where we performed code reviews on plugins and themes. The majority of that business was working with commercial theme shops. Around 90% of the reviews were handled on GitHub.

At the same time, I was still volunteering with the WordPress theme review team. There was no comparison in terms of user experience. GitHub won hands down. This experience turned me away from wanting to perform reviews for the official directory. Nothing there was streamlined. It was tougher to point to specific code issues and check if a problem was corrected when an author sent in an update.

Žoljom’s initial proposal outlines a system where the theme author submits their theme through the WordPress theme upload page. If it passes the initial check, the system would automatically create a new GitHub repository with the theme. Theme authors can then fork this newly-created repository to work on code changes based on reviewer feedback. Theme updates would also work through GitHub.

“I think that forks are a good middle ground,” said Žoljom. “Authors have forks on their own GitHub accounts and can update those, and make updates that way even. That would definitely help us with people who are trying to trick the system by updating their themes once they go live with things that break the requirements.”

The proposal is merely an idea to explore at this stage. Much of the process could change if it is given the green light to proceed.

“In the end, I hope this initiative will go live and help us bring the review queue down,” said Žoljom. He has hopes the team can integrate more automated checks such as the WordPress Coding Standards. With more automation, it could also mean the theme review team could focus on other projects it works on, such as preparing for the future of full-site editing and continuing education.

Beyond the Review Process
One of the primary issues the team has faced over the years is educating authors on writing cleaner and more secure code. Some issues like sanitizing data on input and escaping it on output are still prevalent during reviews. The team’s history of communication via Trac has not seemed to help with education on the whole.

“The main point is the fact that many people can be involved in the review process, and then you have experienced reviewers who can show why some code is not good or needs improvement,” said Žoljom. “This provides a way for many people to see what is wrong with the code and how they can improve it. Plus the forked theme stays in their repo, so they can see the changes that they made and why.”

Žoljom has a few items in reserve for the long-term wish list. One possibility for the future may be setting up themes on Packagist for installation via Composer. He admits that it is a longshot at best.

“I see this as an opportunity to bring one aspect of WordPress up to par with other modern PHP frameworks like Laravel,” he said. “Plus, utilize automatic tools at our disposal. From PHP CodeSniffer, PHPStan, ES Lint, and a plethora of other tools, we could also show authors how they can set these tools on their projects and make their coding skills better. Maybe throw some automatic integration tests in the mix. The possibilities are really endless.”

Overview

Delta Risk, a San Antonio-based provider of SOC-as-a-Service and managed security services, announced an expanded partnership with VMware Carbon Black, a provider of cloud-delivered, next-generation endpoint security. The integration connects Delta Risk’s ActiveEye security platform with the VMware Carbon Black product suite, enabling managed detection and response (MDR), threat hunting, and 24×7 monitoring for organizations of all sizes.

What the Integration Delivers

The enhanced integration between ActiveEye and VMware Carbon Black Cloud solutions targets four operational outcomes:

• Detection and Response: Analytics, orchestration, and automation are used to prioritize threats and reduce time-to-response.
• Executive Reporting via KPIs: Key Performance Indicators are surfaced in the ActiveEye portal, giving security teams and board-level stakeholders a consistent view of program progress.
• Unified Data View: A single pane of glass provides access to data and reports across multiple security controls, with a stated focus on customer experience.
• Streamlined Onboarding: Deep platform knowledge across the Carbon Black ecosystem is intended to reduce time-to-value for new customers and managed service providers (MSPs).

Supported VMware Carbon Black Products

Delta Risk’s Managed Detection and Response coverage extends across the full VMware Carbon Black product line, including:

• VMware Carbon Black Cloud™
• VMware Carbon Black Cloud™ Endpoint Standard
• VMware Carbon Black Cloud™ Enterprise EDR
• VMware Carbon Black Cloud™ Audit and Remediation
• VMware Carbon Black Cloud™ Managed Detection
• VMware Carbon Black App Control
• VMware Carbon Black EDR (on-premises)

Partner and Customer Impact

According to Victor Baez, Senior Director of WW Channel for VMware Carbon Black, the integration has enabled incident response (IR) partners and MSPs to deliver 24×7 post-breach monitoring. He noted that dozens of IR monitoring engagements were being managed continuously through ActiveEye within a 90-day period at the time of the announcement.

Jason Peoples, Director of Partnerships at Delta Risk, highlighted the scale of the ActiveEye platform, stating that it processes more than six billion events daily, with automation resolving more than 95 percent of those events. The intent is to reduce alert fatigue for security teams and allow analysts to focus on genuine threats rather than false positives.

About the ActiveEye Platform

ActiveEye is Delta Risk’s proprietary security operations platform, built around Security Orchestration, Automation, and Response (SOAR) principles. The platform provides a co-managed model, meaning customers retain visibility into SOC activities through the ActiveEye Portal rather than handing off security operations entirely. This approach is designed to give organizations of any size access to enterprise-grade threat detection and response capabilities without requiring a fully staffed in-house security operations center.

About Delta Risk

Delta Risk delivers Security Operations Center (SOC)-as-a-Service and broader managed security services, covering endpoint, network, and cloud security threat monitoring and response on a 24×7 basis. The company positions its co-managed model — combining its expert SOC team with the customer-facing ActiveEye Portal — as an accessible path for organizations seeking a modern security operations posture without fully outsourcing control.

What Is Fake Fingerprint Detection?

Fake fingerprint detection — also called fingerprint liveness detection or anti-spoofing — refers to the capability of a biometric system to distinguish between a genuine, live human finger and an artificial spoof artifact. Spoof artifacts can include gelatin molds, silicone overlays, printed latent fingerprints, or thin film replicas designed to fool optical, capacitive, or ultrasonic fingerprint sensors.

As fingerprint-based authentication has expanded from physical access control into mobile payments, border control, and enterprise identity management, the risk posed by presentation attacks has grown significantly. Effective liveness detection is now considered a baseline requirement for high-assurance biometric deployments.

How Fake Fingerprint Detection Works

Anti-spoofing techniques generally fall into two categories:

Hardware-Based Liveness Detection

• Pulse oximetry and blood flow detection: Sensors measure blood oxygen levels or micro-vibrations caused by a heartbeat, which synthetic materials cannot replicate.
• Ultrasonic imaging: Ultrasonic sensors (such as those used in some modern smartphones) can image sub-surface skin structures, making it far harder to spoof with a surface-level artifact.
• Sweat pore analysis: High-resolution sensors detect sweat pore activity, which is absent in silicone or gelatin replicas.
• Electrical impedance sensing: Capacitive sensors can measure the electrical properties of the material touching the sensor; live skin has a distinct impedance profile.

Software-Based Liveness Detection

• Texture analysis: Machine learning models analyse micro-texture patterns in fingerprint images that differ between live skin and fabricated materials.
• Deep learning classifiers: Convolutional neural networks (CNNs) trained on large datasets of genuine and spoofed fingerprint images can classify presentation attacks with high accuracy.
• Challenge-response methods: The system requests a specific action (e.g., slight finger movement or pressure variation) to confirm liveness dynamically.

Industry Standards and Evaluation

The primary international standard governing presentation attack detection for biometric systems is ISO/IEC 30107, which defines terminology, detection mechanisms, and testing methodologies. Part 3 of the standard (ISO/IEC 30107-3) specifically addresses evaluation metrics such as the Attack Presentation Classification Error Rate (APCER) and the Bona Fide Presentation Classification Error Rate (BPCER). Vendors who claim conformance to ISO/IEC 30107-3 have had their systems evaluated against a defined set of spoof materials and conditions.

Key Vendors in the Biometric Anti-Spoofing Space

The original market report cited the following organisations as notable participants in the fake fingerprint detection market. Brief context is provided for each based on their publicly known activities:

• VIRDI (Union Community Co., Ltd.): A South Korean biometric hardware manufacturer producing fingerprint access control terminals with built-in fake finger detection features.
• Goodix Technology: A semiconductor company supplying optical and ultrasonic under-display fingerprint sensors, including liveness detection capabilities, widely used in smartphones.
• Precise Biometrics: A Swedish company specialising in fingerprint software and algorithm development, including anti-spoofing algorithms for both mobile and smart card platforms.
• Anviz: A manufacturer of time-attendance and access control devices that incorporates fingerprint sensors with fake-finger rejection.
• FocalTech Systems: A fabless semiconductor company active in touch and fingerprint sensor IC design.
• ievo Ltd: A UK-based manufacturer of fingerprint readers for physical access control, marketed with multi-spectral imaging to improve liveness detection.
• Bosch: Active in security and building technology; produces access control and surveillance systems that can integrate biometric authentication.
• SPEX Forensics: Specialises in forensic fingerprint examination tools and consumables, relevant to the law-enforcement and forensic investigation segment rather than commercial access control.
• GitHub: Listed in the original report; GitHub (the code hosting platform) is not a biometric hardware or software vendor. Its inclusion likely refers to open-source fingerprint liveness detection research repositories hosted on GitHub, rather than the company itself as a market participant.
• LockSmithLedger: A trade publication covering physical security and locksmithing; its inclusion as a “vendor” in a biometric detection market report is unclear and may reflect a data-sourcing or classification error in the original report.

Application Areas

Fake fingerprint detection technology is deployed across a range of industries and use cases:

• Physical access control: Office buildings, data centres, and government facilities require high-assurance fingerprint readers that reject spoofed credentials.
• Mobile device security: Smartphones and tablets use fingerprint sensors for device unlock and payment authorisation; liveness detection prevents access via a lifted latent print.
• Border control and e-passport verification: National border agencies use fingerprint biometrics for traveller identification; anti-spoofing is critical in this context.
• Banking and fintech: Know Your Customer (KYC) processes and payment authentication increasingly rely on biometric verification with anti-spoofing safeguards.
• Forensic investigation: Law enforcement agencies use fingerprint comparison and detection tools for criminal investigation, where distinguishing genuine from artificially deposited prints is relevant.
• Time and attendance: Workforce management systems use fingerprint clocking to prevent buddy-punching; liveness detection closes this loophole.

Key Technical Challenges

Despite significant advances, fake fingerprint detection remains an active research problem. Several challenges persist:

• Unknown attack types: Anti-spoofing classifiers trained on known spoof materials may fail against novel materials not present in the training set.
• Cross-sensor generalisation: A model trained on images from one sensor type may not generalise well to another sensor’s image characteristics.
• Trade-off between security and convenience: More aggressive liveness detection thresholds increase false rejection of legitimate users, degrading the user experience.
• Adversarial attacks: Sophisticated attackers can iteratively refine spoof materials based on partial knowledge of the detection algorithm.

Evaluating Anti-Spoofing Claims

When assessing fingerprint reader products that advertise fake finger detection, DevOps and security engineers should look for the following:

• Published APCER and BPCER rates tested under ISO/IEC 30107-3.
• Clarity on which spoof material categories were tested (e.g., gelatin, silicone, wood glue, ecoflex).
• Whether testing was performed by an independent third-party laboratory.
• Sensor technology (optical, capacitive, ultrasonic, multispectral) and its inherent resistance to surface-level spoofs.
• Whether liveness detection is hardware-based, software-based, or a combination.

Market research reports can provide a useful landscape overview, but technical procurement decisions should be grounded in vendor-supplied test data and, where possible, independent evaluation.

Why Hybrid Skills Now Matter More Than Pure Automation

In earlier DevOps adoption cycles, automation skills dominated hiring requirements. That has shifted. The DevOps Institute survey found that process skills and process knowledge have overtaken automation skills as the primary competency organizations seek. This makes sense: once teams understand what automation tools can do, the harder challenge becomes defining the processes, governance, and cultural norms that determine how those tools are used effectively.

As the survey authors put it, early DevOps adopters focused on understanding the technology and its potential. The next logical step is establishing the processes and rules by which that technology is applied consistently and responsibly across the organization.

DevOps Hiring Is Genuinely Difficult

With approximately 52% of companies actively building DevOps competency through hiring, demand for qualified candidates is high — and supply is limited. Around 65% of respondents reported difficulty finding candidates with the right combination of skills. This is not simply a shortage of technically proficient engineers; it reflects the rarity of professionals who can bridge development, operations, automation, security, and business communication simultaneously.

More than half of all survey respondents described themselves as still in the early or mid stages of their DevOps journey. The reasons are both technical and organizational. DevOps requires dismantling deeply entrenched accountability silos, which can feel threatening to teams and individuals whose roles and authority are defined by those silos. Organizational transformation — not tool selection — is often the hardest part.

Common DevOps Team Topologies

The survey identified three team models most commonly in use:

1. The DevOps team silo (or promotion model): A dedicated DevOps team acts as an intermediary between development and operations. This is the most widely adopted model, though it risks recreating the silos DevOps is meant to eliminate.
2. The DevOps collaboration model: DevOps is treated as everyone’s responsibility across development and operations, with no separate dedicated team. This is the closest to the original DevOps vision.
3. The DevOps toolkit model: A central DevOps team owns and manages shared tooling used by other teams. This is common in larger enterprises standardizing their CI/CD and observability stacks.

Each model has tradeoffs, and the right choice depends on organizational size, culture, and maturity level.

The DevOps Hybrid Specialist Profile

The survey paints a clear picture of what an ideal DevOps candidate looks like today: someone with a holistic understanding of developer concerns, operational realities, and automation capabilities. Human skills, process knowledge, functional IT expertise, and automation proficiency are treated as equally important rather than ranked in a fixed hierarchy.

DevOps hybrid specialists typically draw from a wide area of expertise — from cloud infrastructure and platform engineering, to frameworks like Agile and Site Reliability Engineering (SRE), to cross-functional communication and business awareness.

Key Skills Sought in DevOps Roles

The following data is drawn from the DevOps Institute survey of 1,260 IT managers.

Most Important DevOps Hybrid Abilities

• Process competencies and process knowledge — 68%
• Automation skills — 67%
• Soft skills — 61%
• Functional knowledge (networking, storage, etc.) — 36%
• Specific automation tool expertise — 29%
• Business skills — 23%

Core DevOps Functional Skills

• Compute and operational knowledge — 52%
• Security practices — 52%
• IT infrastructure knowledge — 50%
• Application design and development — 45%
• Quality assurance knowledge — 32%
• Business continuity and disaster recovery awareness — 31%
• Testing knowledge — 31%
• Networking knowledge — 28%

Core DevOps Technical Skills

• CI/CD toolchain proficiency — 69%
• Cloud platform and cloud environment knowledge — 63%
• API knowledge — 41%
• Analytical and observability knowledge — 31%
• Multiple programming languages — 28%
• Experience with UI, web services, and middleware — 28%

What This Means for DevOps Teams and Hiring Managers

The takeaway is clear: organizations building DevOps capabilities cannot hire purely for tool knowledge. The engineers who drive lasting DevOps success understand why processes exist, can communicate across organizational boundaries, and are comfortable operating in ambiguous, fast-changing environments. Soft skills — often undervalued in technical hiring — rank third overall in the survey, ahead of functional and business-specific knowledge.

For practitioners looking to advance in DevOps roles, broadening your skill set beyond any single toolchain or platform is increasingly important. Familiarity with SRE principles, Agile delivery frameworks, cloud-native architectures, and security practices will differentiate candidates in a competitive hiring market.

Survey data referenced in this article is attributed to the DevOps Institute. The original report was published prior to March 2020. Photo credit: Joe McKendrick.

What Does “AI in DevOps” Actually Mean?

“AI in DevOps” is an umbrella term covering a range of techniques — machine learning, natural language processing, anomaly detection, and predictive analytics — applied to the software development and operations lifecycle. In practice, this means:

• Predictive incident detection: Models trained on historical metrics and logs surface anomalies before they become outages.
• Intelligent test selection: ML classifiers identify which tests are most likely to catch regressions in a given change, reducing CI pipeline duration.
• Automated root-cause analysis: Correlation engines link symptoms across distributed services to probable causes, cutting mean time to resolution (MTTR).
• Release risk scoring: Models assess deployment risk based on change size, author history, and service dependency graphs.
• Capacity forecasting: Time-series models project infrastructure demand, informing auto-scaling policies and cost planning.
• AI-assisted code review and generation: Large language models suggest fixes, flag security patterns, and accelerate onboarding.

Key Market Drivers

Several converging forces are accelerating AI adoption inside DevOps organisations:

1. Growing Complexity of Distributed Systems

Microservices and cloud-native architectures generate volumes of telemetry that exceed human ability to parse manually. AI-driven observability platforms — ingesting logs, traces, and metrics in real time — are increasingly the only practical way to maintain visibility at scale.

2. Pressure to Accelerate Release Cycles

Business demands for faster feature delivery push teams toward automating every safe-to-automate gate in the pipeline. AI adds a layer of contextual judgment — distinguishing a routine deployment from a high-risk one — that rules-based automation alone cannot provide.

3. Talent and On-Call Fatigue

Alert noise and toil remain persistent problems. Teams are adopting AIOps platforms to reduce false-positive pages, correlate related alerts into single incidents, and auto-remediate known failure patterns — freeing engineers for higher-value work.

4. Maturing ML Infrastructure

Cloud providers (AWS, Azure, GCP) offer managed ML services that lower the barrier to embedding models in delivery pipelines. Teams no longer need dedicated data-science staff to leverage pre-trained anomaly detection or forecasting APIs.

Challenges and Constraints

Adoption is not without friction. Engineers and organisations routinely encounter the following barriers:

• Data quality: AI models are only as good as the telemetry they train on. Inconsistent labelling, missing context, and short history windows degrade model accuracy.
• Explainability: Operations teams are reluctant to act on recommendations from opaque models. Interpretable outputs — showing why a model flagged a deployment — are a prerequisite for trust.
• Integration complexity: Bolting AI tooling onto heterogeneous legacy pipelines requires significant engineering effort that can outweigh near-term benefit.
• False positives and automation risk: An AI system that triggers an auto-rollback incorrectly can cause more disruption than the defect it was trying to prevent. Confidence thresholds and human-in-the-loop gates require careful tuning.
• Skill gaps: Most DevOps practitioners are not ML engineers. Operationalising and retraining models over time demands capabilities that many teams lack.
• Vendor lock-in: Tight coupling to a specific AIOps or ML platform creates migration risk as the market consolidates.

Competitive Landscape: Categories of Players

The AI-in-DevOps ecosystem spans several overlapping categories. Rather than reproducing a specific vendor list that would date quickly, it is more useful to understand the segments:

• AIOps platforms: Tools focused on ingesting observability data and applying ML to reduce noise and surface insights (e.g., Moogsoft, BigPanda, Dynatrace, New Relic).
• CI/CD intelligence layers: Products that analyse pipeline history to predict test failures, optimise parallelisation, and flag risky changes (e.g., Launchable, Harness).
• Security and shift-left AI: Static analysis enhanced with ML to prioritise findings by exploitability and context (e.g., Snyk, Semgrep).
• AI-assisted coding tools: LLM-based assistants embedded in IDEs and code review workflows (e.g., GitHub Copilot, Amazon CodeWhisperer, Tabnine).
• Cloud-native ML services: Managed services from major cloud providers that DevOps teams embed directly in pipelines for custom model training and inference.

Strategic activity in this space includes acquisitions (observability vendors absorbing AIOps startups), partnerships between cloud providers and specialist toolmakers, and ongoing open-source development (OpenTelemetry, for example, improves the raw data quality that AI models depend on).

Regional Adoption Patterns

North America continues to account for the largest share of enterprise AI-in-DevOps investment, driven by a dense concentration of cloud-native companies and mature DevOps practices. Europe follows, with increasing regulatory attention (particularly around AI transparency and data residency) shaping purchasing decisions. Asia-Pacific is the fastest-growing region, propelled by large-scale digital transformation programmes and significant cloud adoption in markets such as India, Japan, and Australia.

Emerging Technologies to Watch

Several technology trends will influence how AI is applied in DevOps over the next few years:

• Large language models in operations: Beyond code generation, LLMs are being applied to runbook interpretation, incident summarisation, and conversational querying of observability data.
• Causal AI: Moving beyond correlation to identifying causal relationships in system behaviour, enabling more reliable automated remediation.
• MLOps maturity: As teams deploy more models into production, MLOps practices (model versioning, drift detection, retraining pipelines) are becoming part of the standard DevOps toolkit.
• FinOps integration: AI-driven cost anomaly detection and rightsizing recommendations are converging with DevOps tooling to close the loop between engineering decisions and cloud spend.
• Autonomous deployment systems: Progressive delivery platforms are beginning to embed ML-driven traffic analysis into canary and blue-green deployments, enabling fully automated promotion or rollback decisions.

Questions Engineers Should Ask When Evaluating AI DevOps Tooling

When assessing whether an AI-driven DevOps tool is worth adopting, the following questions cut through marketing noise:

1. What data does the model train on, and how is it kept current? A model trained on stale or unrepresentative data will produce poor recommendations in production.
2. How does the tool explain its outputs? Operators need to understand why an alert was surfaced or a deployment was flagged before they can act confidently.
3. What is the false-positive rate in environments similar to mine? Ask for customer references in your industry or stack, not just headline accuracy numbers.
4. What happens when the model is wrong? Understand the failure modes and what guardrails exist before allowing any automated action.
5. How does this integrate with my existing pipeline and observability stack? AI features embedded in an incompatible toolchain rarely deliver their advertised value.
6. What is the total cost of ownership? Factor in data egress, model hosting, and the engineering time required to tune and maintain the system.

Conclusion

AI is becoming a practical, necessary component of mature DevOps practice — not because of hype, but because the scale and complexity of modern systems genuinely exceeds what rule-based automation and human attention alone can manage. The organisations gaining the most value are those treating AI tooling with the same rigour they apply to any production system: careful evaluation, incremental rollout, clear success metrics, and ongoing maintenance.

For DevOps engineers, the most important near-term skill is not building ML models — it is knowing how to evaluate, integrate, and operate AI-assisted tooling critically and safely.

As an industry, we spend a lot of time talking about the process and technology that make up a good DevOps practice. We have a strong focus on the development, IT operations, QA” and security roles. We debate tools and automation strategies. We wax philosophical about DevOps culture.

The Promise and the Problem

What if interacting with your devices felt as natural as chatting with a colleague? Voice is our primary communication channel as humans, and it’s increasingly the interface we expect from the machines around us — smart speakers, cars, wearables, home automation systems, and enterprise software alike.

Advances in speech recognition, neural networks, and raw compute power have made voice computing commercially viable. Virtual assistants now ship in everything from microwave ovens to eyeglasses. Yet despite this momentum, conversational AI still struggles with what toddlers do effortlessly: hold a simple, contextual conversation.

Understanding why that gap exists — and how engineers are closing it — is essential knowledge for anyone building or integrating AI-driven products today.

The Current Landscape

Modern AI can drive cars, optimize global supply chains, beat grandmasters at chess, and surface cancer-treatment candidates from medical literature. Yet most AI systems still falter at casual, open-ended conversation — the kind you’d have with a neighbor over a fence.

Projects like Google Duplex demonstrated that narrow, task-specific conversational AI (e.g., booking a restaurant reservation) can sound remarkably human. But generalized, contextually aware conversation remains an unsolved problem at scale.

Demand for conversational AI is accelerating regardless. According to eMarketer (figures cited in the original Forbes piece, dated early 2020), nearly 40% of internet users were already using voice assistants such as Google Assistant, Alexa, Siri, Bixby, and Cortana. The research firm MarketsandMarkets projected the global conversational AI market to grow at a compound annual growth rate of just over 30% between 2019 and 2024.

Gartner, also cited in the original article, predicted that by 2021 roughly one in six customer service interactions globally would be handled by AI — but also warned that approximately 40% of chatbot and virtual assistant applications launched in 2018 would be abandoned by 2020. That second figure is a useful reminder: deploying conversational AI is easy; making it good enough that users keep using it is hard.

How Conversational AI Actually Works: A Six-Stage Breakdown

To understand where conversational AI succeeds and fails, it helps to decompose a single human conversational exchange into its constituent stages. Each stage maps to a distinct technical problem.

Stage 1 — Speech Recognition (Speech-to-Text)

The system must capture audio, filter background noise, compensate for accents and speech impediments, and convert the acoustic signal into a sequence of words. This is the Automatic Speech Recognition (ASR) layer. Modern ASR engines — powered by deep learning architectures like recurrent neural networks (RNNs) and, more recently, transformers — have become remarkably accurate under controlled conditions. Real-world noise, multiple simultaneous speakers, and heavy accents still degrade performance significantly.

Stage 2 — Semantic Understanding

Once words are transcribed, the system must resolve ambiguity. The word bank means something different depending on whether the surrounding conversation is about fishing, finance, or billiards. Homophones (there/their/they’re), homonyms, and domain-specific jargon all require the system to lean on surrounding context rather than dictionary definitions. This is where many lightweight chatbots begin to fail.

Stage 3 — Natural Language Understanding (NLU)

NLU goes further than semantics. It requires the system to interpret intent by layering multiple types of context:

• Conversational context: What has already been said in this session?
• Personal context: Who is the speaker, and what is their history with the system?
• Situational context: What is happening in the user’s environment right now?
• World context: Are there external events (weather, news, calendar) that influence the meaning of the utterance?

A question like “Does it look like rain?” asked on a cloudy afternoon is rarely a meteorological inquiry in isolation — the user probably wants to know whether to pack an umbrella or reschedule outdoor plans. Capturing that underlying intent is what NLU aims to do, and it remains one of the hardest open problems in the field.

Stage 4 — Response Generation

Once intent is understood, the system must determine what kind of response actually satisfies the user’s need. Consider the query: “Do you accept American Express?” A literally correct answer — “No” — is not particularly helpful. A more useful response would be: “No, but we accept Visa and Mastercard.” Generating contextually appropriate, intent-satisfying responses requires the system to model the user’s goal, not just parse their words. Because NLU is imperfect, response generation inherits those imperfections.

Stage 5 — Natural Language Generation (NLG)

NLG is the process of expressing the intended response in fluent, natural-sounding language. Rule-based and template-driven systems produce output that sounds robotic precisely because they treat NLG as a fill-in-the-blanks problem. Neural approaches — including large language models — generate more natural text but can introduce hallucinations, inconsistencies, or off-topic content. Striking the right balance between fluency, accuracy, and safety is an active area of research and engineering.

Stage 6 — Speech Synthesis (Text-to-Speech)

The final stage converts the generated text back into audio. Modern Text-to-Speech (TTS) engines — including WaveNet-based and neural TTS systems — produce voices that are increasingly difficult to distinguish from human speech. Like Stage 1, TTS has matured considerably. The hard problems live in Stages 2 through 5.

Why This Pipeline Is Hard to Get Right

Humans execute all six stages simultaneously and unconsciously — often while performing other complex tasks — in a fraction of a second. We also do it adaptively, correcting misheard words mid-stream, adjusting our vocabulary for the listener, and reading emotional subtext.

Machines must do this explicitly, stage by stage, with each layer accumulating error from the previous one. A small ASR mistake at Stage 1 can cascade into a completely wrong response by Stage 4. And in noisy real-world environments — exactly where voice interfaces are often expected to work — ASR accuracy drops, magnifying downstream errors.

Research efforts like Google Brain’s Meena (introduced around early 2020) demonstrated that end-to-end neural conversational models — trained on massive dialogue datasets — could produce more coherent multi-turn conversations than earlier rule-based or retrieval-based chatbots. End-to-end training allows the model to learn conversational flow holistically rather than optimizing each stage in isolation. However, even these models struggled with factual accuracy and consistency across longer conversations.

Practical Applications Already Deployed

Despite these challenges, conversational AI is already delivering real value across a range of enterprise and consumer scenarios:

• Customer service automation: Chatbots and voice bots handle tier-1 support queries, reducing agent load and improving response times.
• Personalized customer experiences: Voice assistants adapt responses based on user history and preferences.
• Sentiment analysis: ML models score customer interactions to flag dissatisfaction or escalation risk in real time.
• Multilingual support: Neural machine translation integrated into conversational pipelines enables assistants to serve global audiences without separate per-language development efforts.
• Smart home and IoT control: Voice interfaces manage thermostats, lighting, security systems, calendars, and online shopping.
• In-vehicle assistants: Hands-free voice control for navigation, calls, and media in connected vehicles.

Most of these capabilities are now available as cloud APIs — from providers such as Google, Amazon, Microsoft, and IBM — meaning teams can integrate sophisticated conversational AI into their products without building NLU or NLG models from scratch.

What Engineers and DevOps Teams Should Watch

If you’re evaluating or deploying conversational AI components, keep the following in mind:

• Failure modes cascade: Monitor each pipeline stage independently. An uptick in ASR word error rate will show up as degraded NLU accuracy before users start complaining explicitly.
• Abandonment is the real metric: A chatbot that works in a demo but frustrates users in production will be abandoned quickly. Instrument completion rates and fallback rates from day one.
• Context window management: Multi-turn conversation quality depends heavily on how well the system maintains and uses conversational history. Session context that is too short produces repetitive, disjointed responses.
• Template-driven NLG does not scale: Rule-based response templates become unmaintainable as use cases grow. Plan for a migration path to model-driven generation if you expect to expand the assistant’s capabilities over time.
• Latency matters for voice: Users tolerate much shorter response delays in voice interfaces than in text chat. End-to-end pipeline latency must be optimized, often requiring edge inference for the ASR stage.

The Road Ahead

Conversational AI has made extraordinary progress, but the gap between today’s systems and genuinely human-like conversation remains real and significant. The most useful near-term advances are likely to come from better contextual modeling, improved factual grounding, and tighter integration between the discrete stages of the pipeline.

For practitioners, the opportunity lies in building systems that are honest about their limitations — gracefully handing off to a human agent when confidence is low, rather than confidently generating a wrong answer. That engineering discipline, more than any single model breakthrough, is what will determine whether the next generation of conversational AI sticks or gets abandoned.

Overview

Amdocs (NASDAQ: DOX), a leading provider of software and services to communications and media companies, announced in February 2020 that Bell Canada — Canada’s largest communications company — had successfully implemented RevenueONE, a core component of Amdocs’ cloud-native customer experience suite CES20, running on Amdocs Microservices360. The deployment builds on a long-standing managed services relationship between the two companies and marks a significant step in Bell’s ongoing digital transformation strategy.

What Was Implemented and Why

Bell’s implementation centered on two key Amdocs technologies:

• RevenueONE: A billing and revenue management building block within the CES20 suite, designed to simplify retail engagement and support complex product and pricing models for communications service providers (CSPs).
• Microservices360: Amdocs’ core microservices platform that, combined with DevOps and Site Reliability Engineering (SRE) practices, is intended to shorten the code-to-production cycle and accelerate time-to-market for new products and services.

The goal was to give Bell both the technical agility to adapt to rapidly changing business conditions and the operational tooling to continuously deliver improvements to its digital retail experience for customers.

DevOps and CI/CD at the Core

A defining aspect of this implementation is its emphasis on DevOps culture and continuous integration/continuous delivery (CI/CD) methodology. Communications service providers like Bell are increasingly expected to behave like software companies — shipping features rapidly, recovering from failures quickly, and scaling infrastructure on demand.

Microservices360 is designed to serve as the enabling engine for exactly this kind of transformation:

• Microservices architecture: Breaking monolithic BSS/OSS systems into independently deployable services reduces the blast radius of changes and allows teams to deploy components on their own release cadence.
• CI/CD pipelines: Automated build, test, and deployment workflows reduce manual handoffs and enable frequent, lower-risk releases.
• Site Reliability Engineering (SRE): Applying SRE principles helps balance the pace of feature delivery against system reliability — a critical concern for a national carrier like Bell.
• Cloud-native design: CES20’s open, modular component model means Bell can adopt new capabilities incrementally without requiring a full platform replacement.

Industry Context: CSPs Transforming to DevOps Organizations

Telecommunications companies have historically operated large, tightly coupled BSS (Business Support Systems) and OSS (Operations Support Systems) stacks that are difficult and slow to modify. As competition from digital-native players intensifies and customer expectations rise — particularly in the lead-up to 5G service rollouts — CSPs like Bell face pressure to modernize at speed.

Adopting a cloud-native, microservices-based architecture directly addresses several of the most common pain points:

• IT velocity: Independent services can be updated without taking down the entire platform.
• Business agility: New product configurations, pricing models, and customer journeys can be launched faster.
• Scalability: Cloud-native workloads can scale horizontally to handle peak demand without over-provisioning permanent infrastructure.
• Vendor flexibility: Open, modular designs reduce lock-in and allow best-of-breed integrations.

Quotes from Key Stakeholders

“As the pace of digital transformation continues to accelerate, it’s critical to adopt cloud-native, microservices-based technologies and DevOps best practices and tools. Together with Amdocs, we are rapidly developing and deploying new capabilities to support Bell’s customer service leadership in a new era of integrated digital communications.”

— Saad Saade, Vice President of IT Delivery, Customer Experience, Bell Canada

“The ability to use open, modular components to enrich existing IT capabilities is a fundamental design principle of CES20 aimed at improving business agility and ensuring focus on areas of greatest business opportunity.”

— Anthony Goonetilleke, Group President, Amdocs Media, Network & Technology

Key Takeaways for DevOps Engineers

This implementation illustrates several broader principles relevant to DevOps practitioners working in or adjacent to the telecommunications industry:

1. Modernization doesn’t require a big bang. Bell extended an existing managed services relationship incrementally, adopting cloud-native capabilities as modular additions rather than replacing everything at once.
2. Platform and practice go together. Microservices360 is presented not just as infrastructure but as a combined platform-plus-methodology that bundles DevOps and SRE practices alongside the tooling.
3. SRE is becoming standard in telecoms. The explicit mention of Site Reliability Engineering alongside DevOps signals that reliability engineering is now expected even in traditionally operations-heavy industries.
4. CI/CD is the mechanism, not the goal. The pipeline exists to serve a business outcome — faster product launches and a better customer retail experience — not as an end in itself.

About Amdocs

Amdocs is a software and services company focused on the communications and media sector. As of fiscal year 2019, the company reported revenue of $4.1 billion and employed approximately 25,000 people serving customers in over 85 countries. Amdocs is listed on the NASDAQ Global Select Market (ticker: DOX). More information is available at www.amdocs.com.

This article is based on a press release issued by Amdocs on February 4, 2020, distributed via GlobeNewswire. Financial figures and employee counts reflect conditions at the time of the original announcement.

VMware’s Virtualization Legacy and the Container Challenge

VMware built its enterprise reputation on virtual machine (VM) technology. For decades, it was the dominant vendor in server virtualization, and large enterprises relied on VMware infrastructure management solutions to run their data centers. However, the rapid rise of container technology introduced a structural challenge: containers offered many of the same portability and isolation benefits as VMs, but with a lighter runtime footprint and faster startup times, making them increasingly attractive for modern application development.

Rather than leading the container movement, VMware was slow to respond. Its existing customer base and revenue model were deeply tied to the VM ecosystem, and containers — largely driven by open-source communities — represented a different development culture than VMware had traditionally served.

Why Enterprise Container Adoption Accelerated

Enterprise interest in containers grew rapidly in the late 2010s. According to a Gartner report cited in the original source, more than 75% of global organizations were projected to run containerized applications in production by 2022, up from fewer than 30% in 2018.

“Containers provide companies with much needed agility,” said Arun Chandrasekaran, distinguished vice president and analyst at Gartner. Containers divide application code into small, discrete packages that teams can compose, deploy, and update independently — reducing development cycles and enabling continuous delivery practices.

Market research firm IHS Markit projected that container software revenue would reach $1.6 billion by 2023, representing a roughly 30% compound annual growth rate from 2019, according to its Data Center Compute Intelligence Service.

VMware’s Slow Start in Containers

While Red Hat and Docker moved early to capture the container market, VMware stayed focused on its VM business. By the time VMware made substantive moves into containers, competitors had already established strong footholds. According to figures cited in the original reporting, VMware held approximately 6% of the container market, compared to IBM’s Red Hat at around 44% and Docker at roughly 23%.

The gap is partly explained by the different philosophies of these vendors. Red Hat built its enterprise business around open-source software and cultivated deep relationships with developer communities through consulting and training. Docker embraced open-source tooling from the start, making its runtime the de facto standard before Kubernetes emerged as the dominant orchestration layer. VMware, by contrast, had historically focused on proprietary infrastructure tooling and enterprise sales cycles rather than developer-led adoption.

“VMware built a legacy business based off of customers who work with its infrastructure management solutions, but lacked a strong presence in the developer community,” said Gary Chen, research director of software defined computing at IDC.

VMware’s Strategic Pivot: Key Acquisitions

Recognizing the risk of being left behind, VMware made a series of targeted acquisitions to build its container and Kubernetes capabilities:

• Heptio (November 2018): Heptio was founded by Craig McLuckie and Joe Beda, two of the original creators of Kubernetes at Google. The acquisition gave VMware both Kubernetes expertise and credibility within the open-source Kubernetes community. “The Heptio acquisition was geared as much [toward] gaining the company’s talent as its technology,” Chen noted. “The founders were very influential in the Kubernetes community.”
• Bitnami (May 2019): Bitnami specializes in application packaging for container and Kubernetes environments, simplifying the deployment of common workloads on Kubernetes clusters. This acquisition strengthened VMware’s developer-facing tooling.
• Pivotal (December 2019): VMware had partnered with Pivotal since 2018 before acquiring it outright. At the time of acquisition, Pivotal’s container platform was reported to have around 140 enterprise customers. Pivotal brought cloud-native application development expertise and its Cloud Foundry-based platform into VMware’s portfolio.

These moves signaled a deliberate effort to complement VMware’s infrastructure management strengths with modern container orchestration and developer tooling capabilities. VMware’s existing enterprise relationships — particularly its dominance in server virtualization and network management — gave it a natural upsell path for container management solutions.

Challenges Facing VMware’s Competitors

Red Hat and IBM Integration

IBM acquired Red Hat in October 2018 for approximately $34 billion, one of the largest technology acquisitions in history. While Red Hat’s OpenShift platform remained the leading enterprise Kubernetes distribution, integrating a company of Red Hat’s scale presented significant organizational complexity. At the time of the original reporting, IBM had made relatively few changes to Red Hat’s day-to-day operations, instead focusing on weaving Red Hat technology — particularly OpenShift — into its own cloud and software portfolio.

Docker’s Orchestration Pivot and Mirantis Acquisition

Docker’s own trajectory illustrates how fast the container market shifted. Docker developed Swarm as its native container orchestration tool, and it gained meaningful adoption. However, Kubernetes quickly became the industry standard for container orchestration, largely displacing Swarm in production environments.

In November 2019, Mirantis acquired Docker’s enterprise platform business, which reportedly served around 750 business customers. Mirantis, previously known for its OpenStack expertise, planned to incorporate the Docker Enterprise platform into its Kubernetes-as-a-Service offering. Mirantis also committed to supporting Docker Swarm for at least two more years post-acquisition.

However, Mirantis’s OpenStack background was viewed as a potential liability. “Throughout its history, Mirantis has been closely aligned with OpenStack; containers are much different,” Chandrasekaran said. Like VMware, Mirantis faced the challenge of pivoting its engineering culture and go-to-market approach to align with Kubernetes-centric enterprise needs.

VMs and Containers: Coexistence, Not Replacement

One factor working in VMware’s favor is that containers and virtual machines are not mutually exclusive. Many enterprises run containers on top of VMs, using VMs to provide hardware isolation and security boundaries while containers handle application packaging and deployment. This hybrid model plays to VMware’s existing strengths and gives it a natural entry point for adding container management capabilities to its existing customer accounts.

Even in organizations that fully embrace containers and microservices, underlying infrastructure — networking, storage, compute management — remains a requirement. VMware’s infrastructure management expertise remains relevant across both paradigms.

The Road Ahead

As of the original publication date (early 2020), the container market was still in a formative phase. VMware’s acquisitions had positioned it more competitively, but execution remained the key variable.

“VMware got a late start with containers, but they have been pivoting hard and outlined a vision that should appeal to their customer base,” Chandrasekaran said. “What they need to do now is execute on that vision.”

VMware’s primary advantages — a massive installed enterprise base, deep expertise in infrastructure management, and a trusted brand among operations teams — give it a credible platform to build on. Whether those strengths translate into container market share depends on how effectively VMware can close the gap with developer communities that Red Hat and Kubernetes-native vendors have cultivated over years.

Key Takeaways

• VMware entered the container market later than Red Hat and Docker, resulting in a significantly smaller market share as of early 2020.
• A series of acquisitions — Heptio, Bitnami, and Pivotal — were VMware’s primary mechanism for gaining Kubernetes talent and tooling.
• Red Hat leads the enterprise container market, but its absorption into IBM introduced integration complexity.
• Docker’s Swarm lost out to Kubernetes; its enterprise business was acquired by Mirantis in late 2019.
• Containers and VMs coexist in many enterprise environments, which provides VMware an organic expansion path from its existing customer base.
• Execution on strategy, not just the strategy itself, will determine VMware’s long-term standing in the container market.