Red Hat and AWS Deepen Their Partnership with a Managed Kubernetes Offering

Red Hat and Amazon Web Services (AWS) have extended their long-standing collaborative partnership to release Amazon Red Hat OpenShift, a jointly engineered, managed, and supported Kubernetes service delivered through AWS. The announcement, made in May 2020, targets enterprise organizations seeking to accelerate cloud-native application development without sacrificing security, compliance, or operational consistency.

What Is Amazon Red Hat OpenShift?

Amazon Red Hat OpenShift is a fully managed service built on Red Hat’s enterprise Kubernetes platform, OpenShift Container Platform. It brings together Red Hat’s container and Kubernetes expertise with AWS’s cloud infrastructure and services. The goal is to give IT teams a single, integrated experience for deploying and operating containerized workloads at scale on AWS.

Key operational features announced for the service include:

• Managed Red Hat OpenShift cluster creation and lifecycle management
• Native integration with the AWS Console for cluster visibility and control
• On-demand, hourly billing consolidated into a single AWS invoice
• Unified support through AWS, covering both the infrastructure and the OpenShift platform
• Native access to more than 170 AWS cloud-native services from within containerized applications

Why This Matters for Enterprise Kubernetes Adoption

One of the persistent friction points for enterprises adopting Kubernetes is the operational complexity of managing the platform alongside the underlying infrastructure. A jointly managed service removes much of that burden, allowing development and platform engineering teams to focus on application delivery rather than cluster administration.

Sathish Balakrishnan, Red Hat Vice President of Hosted Platforms, explained the rationale at launch:

“We often see IT decision-makers seeking prescriptive guidance around the changing requirements of IT operations and application development in a containerised world. By blending Red Hat’s and AWS’ decades of enterprise IT knowledge and experience into Amazon Red Hat OpenShift, IT organisations will be able to launch cloud-native systems that can retain enterprise-grade security, be more agile and see improved performance while driving cost efficiencies.”

Sathish Balakrishnan, VP of Hosted Platforms, Red Hat

Key Benefits for Enterprise Teams

According to Red Hat, the primary benefits of Amazon Red Hat OpenShift include:

• Enterprise-grade Kubernetes: Access to the same OpenShift platform used by more than 2,000 enterprise customers globally at the time of announcement.
• Deep AWS integration: Native connectivity to AWS managed services, enabling teams to build applications that leverage the full AWS portfolio without leaving the OpenShift environment.
• Hybrid cloud readiness: Consistent APIs and tooling across on-premises OpenShift deployments and the AWS-managed version, supporting hybrid cloud architectures.
• Open-source foundation: Built on upstream Kubernetes and Open Container Initiative (OCI) standards, avoiding proprietary lock-in at the platform layer.

Red Hat’s Stance on Enterprise Kubernetes Architecture

Balakrishnan was explicit about Red Hat’s architectural philosophy, pushing back against approaches that layer Kubernetes on top of legacy virtualization stacks or proprietary middleware:

“Contrary to some industry vendors, we do not see enterprise Kubernetes as being truly successful if it’s based on legacy virtualisation infrastructure or layered across proprietary technologies. Red Hat views the cloud-native platform for the enterprise as fully open and highly-scalable in a model that embraces upstream innovation safely for use in production.”

Sathish Balakrishnan, VP of Hosted Platforms, Red Hat

This positions Amazon Red Hat OpenShift as a direct counter to managed Kubernetes offerings that rely on hypervisor-based node isolation or vendor-specific control plane components.

Availability

At the time of the original announcement in May 2020, Amazon Red Hat OpenShift was entering an early access program. Interested organizations were directed to the Red Hat OpenShift website to register for preview access. For the current availability status and regional support, consult the official Red Hat OpenShift and AWS product pages, as the service has continued to evolve since this initial launch.

Watch: OpenShift on AWS Overview

Summary

Amazon Red Hat OpenShift represents a significant step in making enterprise Kubernetes accessible to organizations that want the power of OpenShift without the operational overhead of self-managing the platform. By combining Red Hat’s Kubernetes expertise with AWS’s infrastructure scale and managed service tooling, the offering addresses two of the most common enterprise objections to Kubernetes adoption: operational complexity and integration with existing cloud services. For DevOps and platform engineering teams evaluating managed Kubernetes options on AWS, it is a solution worth evaluating alongside Amazon EKS and other alternatives.

A Summit Unlike Any Other

Red Hat Summit 2020 was the company’s first-ever fully virtual event — a direct consequence of the COVID-19 pandemic. Attendees watched keynotes delivered from home offices rather than a convention-center stage. Paul Cormier, who had taken over as Red Hat President and CEO only weeks before the event, delivered the opening keynote address remotely, setting the tone for a summit defined by adaptability.

“Perhaps more than ever before, the unique needs of every organization are in sharp focus — some need to scale operations immediately to meet relentless services demand while others seek to strengthen and maintain core IT operations.”

Paul Cormier, President and CEO, Red Hat

COVID-19 Response: Extended Product Lifecycles

One of the more immediately practical announcements at the summit was Red Hat’s decision to extend product lifecycle support for customers affected by the pandemic. The company acknowledged that forcing customers to perform upgrades or migrations during an economic crisis would place an unfair burden on already-strained IT teams.

“Our priority is to provide extensions to our products that would have had an end of maintenance (EOM) phase in the near future so our customers are not forced to perform upgrades or migrations while reeling from the impact of COVID-19.”

Red Hat, Summit 2020

The specific products and extended timelines were published separately in Red Hat’s official lifecycle documentation at the time of the announcement.

OpenShift Virtualization: Bringing VMs to Kubernetes

A central technical theme of the summit was the convergence of traditional virtual machine workloads with cloud-native container environments. Red Hat cited its own Enterprise Open Source Survey, which found that more than 55% of enterprises surveyed expected to increase their use of containerized applications in 2020. Yet a significant portion of production workloads still ran on traditional VMs — and migrating all of them immediately was neither practical nor desirable.

OpenShift Virtualization addresses this gap by enabling organizations to run VM-based workloads directly on Kubernetes, alongside containers and serverless functions. Rather than forcing full containerization, teams can migrate VMs to OpenShift and modernize them incrementally — or keep them running as VMs indefinitely, depending on business needs.

Key Developer Benefits of OpenShift Virtualization

• Unified development experience: Developers can manage VMs, containers, and serverless functions through a single Red Hat OpenShift interface, eliminating the workflow silos that typically separate traditional and cloud-native stacks.
• Flexible VM migration: VMs can be migrated to OpenShift without requiring immediate containerization. They can power mixed workloads and be containerized over time — or remain as VMs if the business case does not justify refactoring.
• Windows VM support: Full support for Windows VMs running versions back to Windows Server 2008, with a migration path toward Windows containers and Windows Server 2019 over time.

At the time of the summit, OpenShift Virtualization was available as a Technology Preview, with general availability planned for later in 2020.

OpenShift 4.4: Continued Evolution of the Enterprise Kubernetes Platform

Red Hat also announced OpenShift 4.4 at the summit, the latest update to its enterprise Kubernetes platform. The release continued the project’s use of the Kubernetes Operators model to automate platform management and the services that support containerized application deployments.

A notable addition in 4.4 was an enhanced developer console, including new metrics and monitoring views. These give developers and application operators better visibility into workload performance directly from the OpenShift interface — without requiring separate observability tooling for day-to-day checks.

The release also continued Red Hat’s focus on serverless application architectures, reflecting growing adoption of event-driven and function-based development patterns among OpenShift users.

Red Hat Advanced Cluster Management for Kubernetes

Perhaps the most significant new product announcement at Summit 2020 was Red Hat Advanced Cluster Management (ACM) for Kubernetes. Designed specifically for hybrid and multi-cloud environments, ACM addresses the operational complexity that arises when organizations run Kubernetes clusters across multiple providers and on-premises environments simultaneously.

Red Hat positioned ACM as a purpose-built solution — developed from the ground up for container environments, rather than adapted from legacy tools designed for physical servers or virtual machines. The product supports clusters running on Red Hat OpenShift (on-premises, bare metal, and on major public clouds), as well as native managed Kubernetes services from Amazon Web Services, Google Cloud Platform, IBM Cloud, and Microsoft Azure.

Core Capabilities of Advanced Cluster Management for Kubernetes

• Unified multi-cluster management: A single control plane to create, update, and decommission clusters across physical, virtual, private cloud, public cloud, and edge environments. This eliminates the need to manage separate consoles, logins, and interfaces for each cluster — a common source of configuration drift and operational errors at scale.
• Policy-based governance, risk, and compliance: Administrators can define security and configuration policies centrally and enforce them automatically across all clusters. New clusters inherit compliance policies at creation time, helping organizations maintain regulatory or corporate standards as their fleet scales.
• Advanced application lifecycle management: Using open standards, teams can automate application deployments with placement policies that integrate into existing CI/CD pipelines and governance workflows — enabling consistent, repeatable deployments across environments.

The Broader Picture: Open Source and Kubernetes Strategy

Throughout the summit’s media sessions, Red Hat executives were consistent in articulating the company’s strategic positioning: a belief that Kubernetes is the future of enterprise infrastructure, combined with a conviction that customers benefit from a unified, open-source-driven control plane rather than an ecosystem of additional proprietary add-ons layered on top.

Red Hat has staked its post-IBM-acquisition identity on deep, long-term open source collaboration — including contributions to upstream Kubernetes, OpenShift, and the broader cloud-native ecosystem — as the foundation of its enterprise value proposition.

For DevOps and platform engineering teams evaluating enterprise Kubernetes platforms, the Summit 2020 announcements reinforced Red Hat’s direction: tighter integration between VM and container workloads, improved developer observability tooling, and centralized multi-cluster governance as first-class platform features.

China’s Cloud Market Was Already Growing Fast

Before the COVID-19 outbreak reshaped global demand patterns, China’s cloud infrastructure market was already on a steep growth trajectory. According to data from analyst firm Canalys, the market grew by almost 67% in 2019. The three dominant providers — Alibaba Cloud, Tencent Cloud, and Baidu AI Cloud — accounted for the majority of total spend, at 46.4%, 18%, and 8.8% respectively.

This concentration of capability meant that when COVID-19 began accelerating in early 2020, these companies were well-positioned to respond at scale.

Rapid Response: Cloud as Critical Infrastructure

Canalys senior analyst Yih Khai Wong noted that all three providers moved quickly to support governments, businesses, research institutions, and students as the crisis escalated:

“The benefits of cloud computing were demonstrated by the leading cloud service providers in response to the escalating coronavirus crisis. They rapidly deployed continuity measures for organizations and established resource-intensive workloads to analyze vast datasets. Cloud companies opened their platforms, allowing new and existing customers to use more resources for free to help maintain operations. They provided free access for research institutions to accelerate drug screening for potential vaccines. Students also benefited by accessing online learning tools set up on the platforms after schools were closed. This set the precedent for technology companies around the world that offer cloud-based services in their response to helping organizations affected by coronavirus.”

— Yih Khai Wong, Senior Analyst, Canalys

Alibaba Cloud: Credits, Compute, and AI for Research

Alibaba Cloud took several targeted steps to address the immediate needs of organizations affected by the outbreak:

• Free credits were offered to organizations wishing to use Alibaba Cloud services, including Elastic Compute and cybersecurity tools.
• AI-powered research support: Alibaba Cloud made its AI platform available at no cost to research institutions working on gene sequencing, protein screening, and other work related to treating and preventing COVID-19.

By providing on-demand compute alongside AI tooling, Alibaba Cloud enabled researchers to run workloads that would otherwise require significant upfront infrastructure investment.

Tencent Cloud: Remote Work and University Research

Tencent Cloud focused its response on two key areas: enabling remote collaboration and supporting academic research.

• Cloud Office Portfolio: Tencent launched a remote-working bundle that included Tencent Meeting and WeChat Work. Both audio and video communication tools were made available for free during the epidemic period to support businesses and students.
• University research access: Tencent Cloud opened its platform to research teams from universities across China, giving them access to compute resources needed to support COVID-19-related studies.

Baidu AI Cloud: Medical Consultation, Mapping, and Open AI

Baidu AI Cloud deployed its capabilities across public health, research, and epidemiological tracking:

• Medical consultation platform: Baidu made its AI-powered medical consultation service available for free to handle public health queries. The platform reportedly received more than 15 million interactions during the outbreak.
• Open AI algorithms: Baidu opened its platform and AI algorithms to research institutions at no charge, supporting work on diagnostics and treatment.
• Spread-tracking tools: Baidu developed mapping tools to track the initial spread of the virus, providing data to aid local response and containment efforts.

Broader Implications for Cloud Adoption

Canalys framed the response of China’s cloud providers not just as crisis management, but as a turning point for enterprise cloud adoption. Organizations forced to rapidly shift workflows online — from remote working to digital learning to distributed research — gained direct experience with what cloud infrastructure can deliver under pressure.

The firm’s outlook for 2020 cloud growth remained positive, with the expectation that organizations would reassess their business continuity strategies after the crisis and accelerate migration of workloads to the cloud. In Canalys’s assessment: “The cloud companies that were quick to support existing and new customers over the last three months will stand to gain the most.”

Key Takeaways for DevOps and Infrastructure Teams

• Elasticity matters in a crisis: The ability to spin up on-demand compute quickly — without procurement delays — proved critical for both research and business continuity workloads.
• AI services are consumable infrastructure: Providers made AI APIs and platforms available as utility services, not just bespoke projects, enabling rapid deployment by research teams without deep ML expertise.
• Free-tier and credit programs accelerate adoption: Removing cost friction during a crisis enabled organizations to experiment and adopt services they might otherwise have delayed evaluating.
• Business continuity planning should include cloud strategy: The COVID-19 response demonstrated that organizations with existing cloud footprints were better positioned to adapt quickly than those relying solely on on-premises infrastructure.

Overview of Jenkins

Jenkins is a mature, open source continuous integration (CI) tool that automates software builds, testing, and deployment. It has been battle-tested in production environments across thousands of organizations and supports virtually any step in the software delivery lifecycle — from a single build job to a complex, multi-stage pipeline spanning development through production operations.

The core strength of Jenkins is its flexibility. A rich plugin ecosystem allows teams to integrate Jenkins with a wide variety of tools — source code management systems like Atlassian Bitbucket, artifact repositories like Artifactory, scripting environments like Microsoft PowerShell, and cloud providers like AWS. A typical multi-branch pipeline might pull source from Bitbucket, package artifacts in Artifactory, run PowerShell build scripts, and trigger deployment to a test environment in AWS — all orchestrated through Jenkins.

Jenkins also ships with a graphical user interface (GUI) that lets administrators manage infrastructure and jobs visually. The GUI provides configuration guidance and inline validation, which lowers the barrier for teams that are less comfortable working exclusively on the command line.

Overview of Jenkins X

Jenkins X originated as a sub-project of Jenkins under the working title “Jenkins X: Jenkins for Kubernetes CD,” where CD stands for continuous delivery. While it was designed with Kubernetes in mind, Jenkins X is not limited to Kubernetes workloads — it can automate software builds across the full DevOps pipeline.

The simplest way to contrast the two tools: think of Jenkins as a blank slate and Jenkins X as a pre-configured, opinionated toolkit. Where Jenkins requires teams to assemble and configure their own pipeline components, Jenkins X bundles best-practice defaults so teams can get started quickly without manual setup.

Jenkins X is operated entirely from the command line — there is no GUI. A single command such as:

jx import my-app

can trigger a chain of actions including:

• Building a pipeline that includes Kubernetes-specific components such as Helm
• Setting up a project repository in GitHub
• Deploying a software version to a staging environment

This out-of-the-box behavior is the central appeal of Jenkins X — less time spent on configuration, more time spent shipping software.

Key Differences: Jenkins vs. Jenkins X

Flexibility vs. Opinionated Defaults

Jenkins is the more flexible option. Its plugin architecture means teams can integrate almost any combination of tools into a working pipeline. This makes Jenkins well-suited for organizations with heterogeneous toolchains, legacy systems, or non-standard workflows.

Jenkins X trades that flexibility for speed of setup. Its opinionated defaults follow community best practices and eliminate much of the configuration work. The tradeoff is that those defaults may not align perfectly with every organization’s environment, and customizing them can erode the simplicity advantage that Jenkins X was supposed to provide.

GUI vs. Command Line

Jenkins provides a web-based GUI that helps administrators configure jobs, view pipeline status, and diagnose errors. The interface presents configuration options in a structured way and validates inputs in real time.

Jenkins X is command-line only. When something goes wrong, users receive an error message rather than guided feedback in a visual interface. For teams already comfortable in the terminal, this is a non-issue. For teams that rely on GUI-driven workflows, this represents a meaningful learning curve.

Plugin Ecosystem

Jenkins has a large, well-established plugin ecosystem covering hundreds of integrations. Jenkins X has its own set of integrations, but they are scoped to its opinionated toolchain. Teams that need integrations outside that scope will find Jenkins the more adaptable option.

Use Cases for Jenkins X

Kubernetes-First Deployments

Organizations running workloads on Kubernetes — or planning to — are the primary target audience for Jenkins X. Building the toolchain infrastructure needed to support Kubernetes from scratch requires significant effort. Jenkins X provides pre-built environments and configurations grounded in open source community best practices, enabling teams to get applications running on Kubernetes faster than they could by assembling the same toolchain manually with Jenkins.

The caveat is that the default Jenkins X configuration may not be a perfect fit for every Kubernetes implementation. Customizing those defaults reintroduces configuration work, which reduces the simplicity benefit.

Multiple Environment Management with GitOps

Production-grade DevOps setups require multiple environments — development, staging, production — to prevent untested code or broken configurations from reaching end users. Jenkins X manages multiple environments using Kubernetes Namespaces, which divide cluster resources among different pipeline stages. Each environment’s configuration is stored in a Git repository, and teams can promote a version from one environment to another in a controlled way.

This approach is rooted in GitOps — the practice of using version-controlled code as the single source of truth for infrastructure and application configuration. When something breaks after a change, teams can revert to a previous Git-tracked state. Jenkins X standardizes both application code and environment definitions into Git-managed repositories, giving the team a unified, auditable record of the entire system.

By contrast, a Jenkins-based setup might combine automated pipeline steps with manual processes and external scripts to manage environments. That distributed approach works, but it creates multiple sources of truth and makes rollbacks more complex.

Decision Guide: When to Use Each Tool

Summary

The decision between Jenkins and Jenkins X ultimately comes down to three factors: how committed your organization is to Kubernetes, how much pipeline flexibility you need, and whether your team requires a GUI to manage CI/CD infrastructure.

• Choose Jenkins X if your workloads run on Kubernetes, you want GitOps-driven environment management, and your team is comfortable operating from the command line.
• Choose Jenkins if you have diverse or legacy toolchains, need the broad plugin ecosystem, or want a GUI to guide pipeline configuration.

Neither tool is universally superior — the right choice is the one that fits your team’s existing skills, infrastructure strategy, and tolerance for configuration overhead.

Original source: searchitoperations.techtarget.com (2019)

What Are Autonomous Delivery Robots?

Autonomous delivery robots (ADRs) are ground-based, self-navigating vehicles designed to transport packages, food, or parcels from a dispatch point to a recipient’s door with little or no human intervention. Unlike drone-based systems, ADRs operate on pavements and roads, making them subject to pedestrian traffic regulations rather than aviation rules. They range in size from small sidewalk bots (roughly the footprint of a large suitcase) to larger cargo-carrying platforms.

Their primary use case is last-mile delivery: the final, often most expensive leg of a shipment’s journey from a distribution hub to the customer’s address. Last-mile logistics can account for a disproportionate share of total delivery cost, and ADRs are positioned as a way to reduce that cost while meeting growing consumer expectations for speed and convenience.

Market Overview and Growth Projections

As of 2019, analyst reports projected the autonomous delivery robots market would register a compound annual growth rate (CAGR) of approximately 49.5% between 2019 and 2024. Several converging forces were cited as drivers:

• E-commerce expansion: Online retail was growing rapidly in both developed and emerging markets, increasing parcel volumes faster than traditional courier networks could scale.
• Consumer expectations: Estimates at the time suggested 20–25% of consumers would choose same-day or instant delivery if offered at a low price point.
• Labor cost pressures: Rising wages in logistics hubs made automated alternatives increasingly attractive to operators.
• Technology maturation: Improvements in computer vision, sensor fusion, and edge computing made it more practical to deploy robots in semi-structured outdoor environments.

Key Players in the Autonomous Delivery Robot Space

A range of startups and established technology companies were competing for position in this market by 2019. The following companies were frequently cited in competitive analyses:

• Starship Technologies – Sidewalk delivery robots deployed on college campuses and suburban neighbourhoods.
• Nuro – A self-driving delivery vehicle startup focused on grocery and retail delivery partnerships.
• Marble Robot Inc. – Known for an early pilot with Yelp Eat24 on San Francisco pavements.
• Savioke – Focused on indoor delivery robots for the hospitality sector (hotels).
• Dispatch – Sidewalk robot delivery targeting neighbourhood-level logistics.
• Robby Technologies – Campus and community delivery robots.
• Aethon – Autonomous mobile robots aimed at healthcare and hospital logistics.
• Kiwi – Food delivery robots operating on university campuses.
• TeleRetail – European autonomous delivery platform.
• Eliport – Last-mile delivery robots with a European focus.

The Last-Mile Delivery Problem

In supply chain and logistics terminology, last-mile delivery refers to the movement of goods from a transportation hub — a depot, fulfilment centre, or locker — to the final destination, typically a private home or office. It is the most operationally complex segment of the delivery chain because:

• Stops are geographically dispersed, reducing vehicle utilisation.
• Recipient availability is unpredictable, driving redelivery attempts.
• Urban traffic, parking restrictions, and building access add friction.
• Consumer expectations for speed continue to rise.

ADRs address this by operating continuously at low marginal cost, navigating pavements autonomously, and enabling contactless handoff. For dense, walkable neighbourhoods and campuses — where stops are close together — the economics are particularly compelling.

Key Market Trends

Retail and Logistics: The Largest Demand Segment

Retail and logistics represented the largest end-user category for ADRs globally as of 2019. The growth of omnichannel retail — where customers expect consistent service whether they shop online, in-store, or via mobile — intensified pressure on last-mile infrastructure. Autonomous robots offered operators a way to scale delivery capacity without a proportional increase in headcount.

A notable example: in June 2018, JD.com — China’s second-largest e-commerce platform — began using autonomous robots for last-mile deliveries in Beijing. The company had previously run trials in controlled environments such as university campuses and industrial parks before expanding to public streets.

Hospitality: A Secondary but High-Visibility Segment

Hotels and resorts adopted indoor delivery robots — most notably from Savioke — to handle room service and amenity deliveries. Beyond the operational benefit of reducing staff time on routine runs, hospitality operators found that robot deployments generated significant social media attention, influencing booking decisions among tourists. Analysts noted that social media penetration in North America (cited at approximately 66% in 2017, compared to a global average of around 37%) amplified this effect in the region.

North America as the Leading Regional Market

North America held the largest market share for autonomous delivery robots in 2019. The region’s advantages included:

• A high concentration of ADR startups and venture capital investment.
• Relatively permissive early regulatory environments in some states (several US states passed legislation permitting pavement robots).
• Strong e-commerce adoption and consumer openness to technology-driven services.
• High demand from both the hospitality and retail logistics sectors.

Other regions with notable activity included Europe (particularly Estonia, home of Starship Technologies’ founding team) and East Asia (China and Japan).

Challenges and Barriers to Adoption

Regulatory Friction

Regulation emerged as one of the most significant near-term barriers. In December 2017, San Francisco restricted delivery robots from most of its pavements following complaints that the machines obstructed pedestrian movement and posed safety risks. The ban followed a proposal by Supervisor Norman Yee, who cited public safety concerns after Marble’s robots began operating on city streets in 2017 in partnership with Yelp Eat24. Companies were effectively forced to relocate testing to other, more permissive cities.

More broadly, municipalities worldwide were still developing frameworks for how to classify, license, and insure ADRs. The absence of consistent international regulation created uncertainty for companies planning cross-border deployments.

Environmental Conditions

Like autonomous vehicles more generally, delivery robots in 2019 performed best in predictable, fair-weather conditions. Rain, snow, ice, and debris all degraded sensor performance and navigation reliability. Testing in cities like Boston — where winter conditions are severe — highlighted these limitations. Reports from NuTonomy’s autonomous vehicle trials in Boston identified snow and unexpected obstacles (including, memorably, seagulls) as meaningful operational challenges.

Data Privacy and Remote Operation

Many ADRs in commercial use as of 2019 were not fully autonomous: they relied on remote human operators at a control centre to handle edge cases, navigate difficult intersections, or intervene when the robot’s perception system was uncertain. This model involves a continuous data link between the robot and the control centre, raising questions under data protection frameworks — particularly in jurisdictions subject to GDPR or similar regulations. Analysts noted that until the industry shifted entirely to fully onboard autonomous decision-making (as Starship Technologies was working toward), this data dependency would remain a compliance and commercial risk.

Urban Density and Pedestrian Conflict

The operating environment that would most benefit from efficient last-mile delivery — dense urban cores — is also the environment least suited to current ADR technology. High foot traffic, complex road crossings, construction, and inconsistent pavement conditions all increase the difficulty of autonomous navigation. The consensus in 2019 was that suburban neighbourhoods, campuses, and controlled logistics zones would see faster ADR adoption than city centres.

Autonomous Delivery Robots vs. Drone Delivery

Airborne delivery systems — most prominently Amazon Prime Air — received significant media attention during the same period. However, industry analysis suggested that ground-based robots offered practical advantages for many use cases:

• Payload capacity: Ground robots can carry larger and heavier loads than most commercial drones.
• Regulatory simplicity: Pavement robots do not require aviation authority approval, though they face their own local regulations.
• Cost per delivery: Ground robots can serve multiple sequential stops on a single charge, improving economics in dense areas.
• Weather resilience: While weather affects both, ground robots are generally less constrained by wind and visibility than drones.

Neither technology was considered ready for broad mass-market deployment in 2019; both were in active pilot and regulatory negotiation phases.

Summary

As of 2019, the autonomous delivery robot market was at an early but high-growth stage, driven primarily by e-commerce volume growth and the persistent cost challenge of last-mile logistics. North America led in deployment activity and investment, with meaningful activity also in China and Europe. Key players ranged from well-funded startups like Nuro and Starship Technologies to more niche operators serving hospitality and campus environments.

The path to mass-market adoption depended — and still depends — on resolving regulatory fragmentation, improving all-weather performance, and transitioning from human-assisted to fully autonomous operation. The market’s trajectory through 2024 and beyond will be shaped by how quickly companies and regulators can work through these constraints.

Original market data attributed to a report summarised by bostonlifenews.com (2019). Figures have not been independently verified.

The Partnership at a Glance

Trend Micro and Snyk announced a partnership aimed at making DevSecOps more actionable for enterprise engineering teams. The collaboration pairs Snyk’s open-source vulnerability scanning with Trend Micro’s virtual patching capability, giving DevSecOps teams a more complete workflow for discovering, prioritizing, and temporarily mitigating security flaws without grinding development to a halt.

What Snyk Brings to the Table

Snyk provides tooling that identifies and fixes vulnerabilities and license violations in open-source dependencies and container images. Because modern applications routinely pull in hundreds of third-party libraries, the attack surface hidden inside dependencies is often larger than the code a team writes itself. Snyk surfaces those risks early in the development cycle, ideally before vulnerable code ever reaches production.

How Trend Micro’s Virtual Patching Works

Virtual patching is a Trend Micro capability designed to protect applications from a known vulnerability without requiring an immediate code change. Instead of modifying the application itself, a virtual patch applies a security policy that restricts or controls access to the vulnerable code path. This buys development teams the time they need to properly remediate the issue during a planned sprint or release cycle.

Virtual patches work for both traditional monolithic applications and cloud-native microservices architectures. In a container-based environment, a virtual patch can limit exposure from a vulnerable container image until that image is rebuilt and redeployed with a fixed dependency.

Combining Detection With Mitigation

The practical value of this partnership lies in closing the gap between detection and remediation. Trend Micro’s COO Kevin Simzer explained that Trend Micro uses the alerts generated by Snyk to inform both developers and security professionals exactly where virtual patches need to be applied. The workflow looks roughly like this:

1. Snyk scans dependencies and container images for known vulnerabilities and license issues.
2. Alerts are surfaced to both developers and security teams, with context on severity.
3. High-severity findings are escalated for immediate developer remediation.
4. Lower-severity findings are covered by a Trend Micro virtual patch, protecting the application while developers address the issue in a future cycle.
5. Virtual patches are retired once the underlying code has been properly fixed.

This triage model is important because no development team can realistically fix every vulnerability the moment it is discovered. Virtual patching gives teams a structured way to manage risk without creating a permanent backlog of unaddressed security debt.

CI/CD Integration

Simzer noted that Trend Micro had already integrated its security offerings with CI/CD platforms such as Jenkins at the time of the announcement. Embedding security checks directly into the build pipeline means vulnerabilities can be caught and virtual patches can be applied automatically as part of the same pipeline that builds and deploys the application, rather than as a manual out-of-band process.

The Cultural Challenge: Shifting Security Left

The technical integration is only part of the problem. Simzer acknowledged that it will take time for security professionals to trust developers to own cybersecurity issues during the development process. The cultural gap between development and security teams is real and well-documented.

Developers are typically operating under significant deadline pressure, which historically has pushed security concerns to the end of the release cycle or into a separate security team’s queue. As responsibility for application security shifts left, developers are expected to address these issues earlier — but that shift requires tooling, training, and organizational support, not just policy mandates.

Virtual patching serves as a pragmatic bridge: security teams retain meaningful control over application exposure while developers are given realistic timelines to address root causes properly.

APIs vs. GUIs: Meeting Both Teams Where They Are

One under-discussed dimension of DevSecOps tooling is the interface gap between developer and security teams. Developers tend to interact with security tooling through APIs and CLI tools that fit naturally into automated pipelines. Security professionals are more likely to rely on graphical dashboards and GUIs for investigation and policy management.

For vendors like Trend Micro, this means building and maintaining both experiences simultaneously. A product that only exposes an API excludes many security practitioners; a product that only provides a GUI cannot be embedded in a modern CI/CD pipeline. Until the two disciplines converge more closely in terms of tooling preferences, security vendors serving DevSecOps teams will need to invest in both surfaces.

Enterprise Adoption Context

At the time of the announcement, Simzer stated that more than 16,000 enterprise IT organizations were already using Trend Micro security offerings to protect both monolithic and container-based cloud-native applications. That installed base gave Trend Micro a meaningful foothold from which to promote DevSecOps best practices at scale, particularly for organizations still running a mix of legacy monolithic workloads alongside newer microservices deployments.

Key Takeaways for DevSecOps Teams

• Not every vulnerability can be patched immediately. A risk-tiered approach — fix critical issues now, virtual-patch lower-severity issues temporarily — is more realistic than demanding instant remediation of everything.
• Detection and mitigation should be linked. Knowing about a vulnerability is only useful if there is a corresponding action. Integrating Snyk alerts with virtual patch deployment creates a closed loop.
• Pipeline integration matters. Security tooling that lives outside the CI/CD pipeline is tooling that gets ignored under deadline pressure. Embed controls where developers already work.
• Plan for two audiences. DevSecOps tooling must serve both API-driven developer workflows and GUI-driven security analyst workflows to achieve broad organizational adoption.

Two Unavoidable Tech Giants

Amazon and Alphabet (Google’s parent company) are two of the most consequential businesses in modern technology. Amazon dominates U.S. e-commerce and cloud infrastructure through Amazon Web Services (AWS), while Google’s search engine is so culturally embedded that its name became a verb. Both companies held market capitalizations close to $1 trillion as of late 2019 and were core components of the FANG group of high-growth tech stocks that drove much of the bull market’s gains through the 2010s.

Despite their similarities in scale, the two companies have very different financial profiles, business strategies, and risk characteristics — which means the “better buy” question is worth examining carefully.

Amazon: The Boundless Disruptor

Amazon’s defining characteristic as a business — and as a stock — is its willingness to enter entirely new markets and accept short-term losses in exchange for long-term dominance. The company has repeatedly demonstrated this pattern across e-commerce, digital books and tablets, video streaming, voice-activated assistants (Alexa), and cloud computing.

As recently as 2014, Amazon reported a net loss. By 2018, net income had reached approximately $10 billion, driven largely by the explosive profitability of AWS. That turnaround reflects founder Jeff Bezos’s long-standing philosophy of investing aggressively in competitive moats even at the cost of near-term earnings.

In 2019, Amazon announced it would shorten free delivery for Prime members from two days to one day — an expensive undertaking that pressured short-term profits, but one that investors largely welcomed as a move that would deepen customer loyalty and widen its e-commerce lead.

At the time of writing, Amazon traded at a price-to-earnings (P/E) ratio of approximately 72 — a premium valuation that reflects investor confidence in its continued reinvestment and growth trajectory. The company was also beginning to expand into healthcare, with the launch of Amazon Care as an internal pilot program for employees, with potential future expansion to the broader public.

Amazon’s Key Strengths (as of 2019)

• AWS: The market-leading cloud platform, generating the majority of Amazon’s operating profit.
• Prime ecosystem: Subscription-based loyalty spanning shopping, video, music, and logistics.
• Optionality: A demonstrated ability to enter and quickly scale new industries.
• Long-term investing culture: Management willing to sacrifice near-term margins for durable competitive advantages.

Alphabet (Google): The Advertising Gold Mine

Google was founded in 1998 — arriving relatively late to the dot-com era — but its search engine quickly became the dominant way people navigate the internet. That search dominance translated into an extraordinarily profitable advertising business. In 2018, Alphabet reported approximately $116 billion in advertising revenue, representing around 85% of its total revenue of $137 billion. Net income exceeded $30 billion that year.

Alphabet uses the cash flow from its core search and advertising business to fund a portfolio of ambitious longer-term projects it calls “Other Bets.” These include:

• Waymo: Alphabet’s autonomous vehicle unit, considered one of the technology leaders in self-driving systems.
• Google Fiber: A broadband internet and cable service.
• GV (formerly Google Ventures): Alphabet’s venture capital arm.

In 2018, the Other Bets segment generated approximately $595 million in revenue against an operating loss of around $3.4 billion — a deliberate investment in future platforms. Waymo alone was widely valued at over $100 billion by analysts at the time, though that figure was speculative and based on projected autonomous vehicle market potential.

Unlike Amazon, Alphabet’s valuation was far more conservative. It traded at a P/E ratio of approximately 25 as of late 2019 — close to the broader market average. The company also held roughly $121 billion in cash and marketable securities on its balance sheet, prompting some investor discussion about whether Alphabet should initiate a dividend.

Alphabet’s Key Strengths (as of 2019)

• Search monopoly: Google processes the vast majority of internet search queries globally, giving it unmatched advertising pricing power.
• Diversified properties: YouTube, Gmail, Chrome, Android, and Google Maps provide multiple high-traffic platforms.
• Cash reserves: Over $120 billion in liquid assets provides a significant financial cushion and acquisition firepower.
• Reasonable valuation: A lower P/E ratio relative to Amazon makes it more attractive to value-oriented investors.
• Google Cloud: A growing enterprise cloud platform competing with AWS and Microsoft Azure.

Head-to-Head Comparison

Which Is the Better Buy?

The answer depends heavily on your investing philosophy and time horizon.

Amazon commands a significant valuation premium, but that premium reflects a track record of entering new markets and building dominant positions. AWS, in particular, is the clearest example: Amazon turned cloud infrastructure into its single most profitable business segment, now a critical piece of the internet’s plumbing. If you believe Amazon will replicate that pattern in healthcare, logistics, or other sectors, the higher P/E may be justified.

Alphabet, by contrast, offers a more stable, cash-rich balance sheet and a lower valuation entry point. Its advertising business has proven remarkably durable, and Google Cloud, while trailing AWS, was growing rapidly. For investors who want exposure to big tech at a more defensible price, Alphabet has historically been the more conservative choice.

The original Motley Fool analysis (September 2019) favored Amazon as the better long-term pick, citing its superior optionality and history of disruption — while acknowledging that both companies are likely to outperform the broader market over the long term.

Important Caveats for Today’s Readers

This analysis was written in September 2019. The technology landscape, both companies’ financial results, and their respective valuations have changed substantially since then. Amazon has continued to grow AWS and its advertising business significantly. Alphabet has expanded Google Cloud and faced regulatory scrutiny in multiple jurisdictions. Neither stock price, P/E ratio, nor cash balance cited above should be treated as current data.

Before making any investment decision, consult up-to-date financial filings, analyst reports, and — where appropriate — a qualified financial advisor.

Original source: The Motley Fool (fool.com). Attribution preserved per original post.

Public sector IT is suffering from a major crisis in confidence when it comes to the pace of change, new figures have claimed.

Despite the need to implement digital transformation ideas and updates, many organisations are facing several major hurdles, according to the 2017 Public Sector IT Operations Survey from Splunk.

The study found that public sector organisations undergoing digital transformation are losing confidence in the ability of their IT teams to keep tabs on the increasing level of new technologies alongside growing external expectations.

Overall, 78 per cent of the organisations surveyed said that a lack of end-to-end visibility was the biggest hurdle to delivering services effectively, while 68 per cent and 64 per cent reported that skills and resources respectively were major issues.

The survey, which quizzed 1227 public sector IT professionals, also found that on average, it takes 41 hours and 11 staff members to restore IT systems to operational status following an outage, costing an average of £21,846.

Almost one third (31 per cent) believe better network visibility would be the most effective way to strengthen IT operations

“There’s no question that a lack of visibility is a major factor shaking the confidence of IT operations staff and management,” said Kevin Davis, vice president of public sector, Splunk.

“A majority of IT decision makers do not think or are unsure if challenges such as IT troubleshooting, service monitoring, security and business and mission analytics can be addressed using a single set of data. Splunk has helped many of our customers realise this and ultimately become more analytics driven. By giving our customers the ability to ingest data once and use it across their IT infrastructure, public sector organisations can get ahead of IT modernisation programs, ultimately helping them embrace digital transformation.”

However help may be at hand in the form of DevOps strategies, which are helping many businesses achieve success despite the rising complexity of IT challenges facing public sector bodies today. The study found that 54 per cent of organisations expect spending on DevOps to increase next year, while 53 per cent forecast increased investment in cloud operations over the same period

“The confidence gap we are seeing maps to other industry and government technology trends including growing public scrutiny, ever-present resource limitations and rapidly increasing expectations of technology by end-users. There’s never been a more important time for public sector organisations to embrace analytics to help them face and overcome these challenges with data,” said Larry Ponemon, chairman and founder of the Ponemon Institute.

“It’s a challenging time to work in Government IT, but there are plenty of reasons to be hopeful for the future. It’s not surprising public sector IT leaders are looking to analytics, cloud and DevOps to help accelerate IT performance and management.”

The pitfalls fall into several distinct categories: leadership failures, technology missteps, misaligned business expectations, development culture problems, quality assurance gaps, operational resistance, vendor mismanagement, and transformation planning errors. Understanding each category is the first step toward avoiding them.

Top IT Leadership Mistakes

DevOps transformations live or die at the leadership level. Technical tooling can always be changed, but a lack of executive clarity or commitment poisons the entire effort from the top down. Common leadership failures include:

• No clear vision — or a vision that isn’t communicated. Teams can’t align around a goal they’ve never heard articulated clearly.
• Assuming DevOps can be bought “in a box.” No single platform, toolchain, or consulting engagement delivers DevOps. It is a continuous cultural and operational shift.
• Failing to break down silos — or simply moving them. Reorganising teams without changing how they interact just relocates the problem. In the worst cases, new silos are created between the “DevOps team” and everyone else.
• Not providing room for change. Expecting transformation while holding teams to legacy processes and deadlines guarantees failure.
• Delegating without engaging. Leaders who hand DevOps off to a single champion and disengage signal that the effort isn’t truly a priority.
• Treating DevOps as an end goal. There is no finish line. Delivery capability, automation, and collaboration must be continuously improved.
• Starting where there is “less need.” Piloting DevOps on low-risk, low-visibility systems generates little learning and even less organisational momentum.
• Trying to move the whole organisation at once. Big-bang transformations are high-risk. Incremental, team-by-team adoption is more sustainable.
• Failing to spread adoption through champions and community. Internal evangelists and communities of practice are how DevOps culture propagates organically.
• Not allowing room for new processes or honest mistakes. Psychological safety is a prerequisite for the experimentation that DevOps requires.

Technology Pitfalls

Technology is an enabler of DevOps, not a definition of it. That said, the wrong technology choices — or the absence of key capabilities — will block progress regardless of how good the culture is.

• No coherent toolchain strategy. Adopting tools reactively results in fragmented pipelines that are harder to maintain than the manual processes they replaced.
• No dynamically provisioned environments. The ability to spin up consistent, on-demand environments (typically via cloud infrastructure) is foundational to continuous integration and delivery.
• Ignoring monitoring and metrics. You cannot improve what you cannot measure. Observability — covering logs, metrics, and traces — must be built in from the start, not retrofitted.
• Failing to rationalise the toolset. Tool sprawl increases cognitive load, support burden, and integration complexity. Periodically audit and consolidate.
• Underestimating the importance of version control. Everything — application code, infrastructure definitions, configuration, pipeline scripts — should be version-controlled.
• A codebase that isn’t ready for frequent releases. Tight coupling, long build times, and absent automated test coverage make frequent deployments dangerous rather than beneficial.

Business Alignment Pitfalls

DevOps initiatives that operate in isolation from business stakeholders tend to optimise for the wrong outcomes. Velocity is only valuable when it delivers value the business actually needs.

• Lack of a shared vision between IT and the business. Technology teams and business units must agree on what success looks like before any tooling decisions are made.
• Not managing agile expectations. Agile and DevOps surface requirements and defects faster. Stakeholders who aren’t prepared for this can interpret increased visibility as increased instability.
• Excluding cross-functional teams from decision-making. Product managers, compliance officers, and security teams must have a seat at the table, or their concerns become blockers later.
• Failing to demonstrate ROI. Without measurable improvements — deployment frequency, mean time to recovery, change failure rate — leadership buy-in erodes.
• Deploying changes before the business is ready. Technical delivery capability must be paired with business readiness: training, support processes, and communication plans.

Development Team Pitfalls

Development teams are often the initiators of DevOps conversations, but they are equally capable of undermining the effort through cultural resistance or misguided hiring decisions.

• Hiring a “DevOps Engineer” and calling it done. DevOps is a set of practices shared across a team, not a job title. A single hire cannot substitute for organisational change.
• Being “too busy” to participate. Teams that claim they can’t invest time in automation and process improvement because of delivery pressure are in a self-reinforcing cycle of technical debt.
• Failing to address culture. Shared responsibility for reliability, proactive collaboration with operations, and comfort with rapid iteration are cultural traits that must be deliberately cultivated.
• Not updating KPIs and incentives. Developers measured only on features shipped have no structural incentive to care about deployment stability, operational cost, or post-release reliability.

Quality Assurance Pitfalls

QA is frequently an afterthought in DevOps transformations, yet it is one of the functions most directly affected — and one of the most powerful accelerators when done well.

• Assuming QA can’t lead DevOps adoption. QA teams have a strong incentive to automate and a clear view into both development and operational quality. They are natural drivers of pipeline improvement.
• Clinging to processes built around static test environments. Static environments are a bottleneck in a continuous delivery pipeline. QA must adapt to ephemeral, dynamically provisioned environments.
• Failing to optimise the testing pyramid. Over-reliance on slow, expensive end-to-end tests at the expense of fast unit and integration tests slows pipelines and delays feedback.
• Not understanding what DevOps QA looks like in practice. In a mature DevOps organisation, QA shifts left — writing automated tests alongside developers, defining quality gates in the pipeline, and contributing to production observability.

Operations Team Pitfalls

Operations teams often bear the consequences of poor development practices and can develop a defensive posture that, while understandable, actively resists the collaboration DevOps requires.

• Accepting DevOps in principle while refusing to change processes. Embracing the vocabulary of DevOps without changing change management, incident response, or release processes produces no real improvement.
• Treating development as the root of all problems. Blame culture between Dev and Ops is the primary anti-pattern DevOps exists to eliminate. Shared ownership of system reliability is the goal.
• Refusing to adopt new platforms in production. Containerisation, infrastructure-as-code, and cloud-native platforms require operations teams to develop new skills. Resistance creates a capability gap.
• Monitoring infrastructure but not business outcomes. CPU and memory metrics matter, but so do order completion rates, error rates visible to customers, and latency at the application layer.
• Not giving development and QA visibility into production. Shared dashboards, shared on-call rotations, and shared post-mortems are practical mechanisms for building shared responsibility.

Vendor Management Pitfalls

Third-party vendors — whether software suppliers, managed service providers, or offshore delivery partners — are frequently excluded from DevOps thinking, which creates gaps in the delivery pipeline at organisational boundaries.

• Excluding vendors from your DevOps model. If a vendor controls part of your pipeline or production environment, their processes directly affect your delivery capability. They need to be inside the model, not outside it.
• Not changing how you interact with vendors. Legacy procurement and engagement models based on annual contracts and waterfall deliverables are incompatible with iterative DevOps delivery.
• Failing to reassess globalisation strategy. Offshore or nearshore arrangements that introduce long feedback loops and timezone barriers can undermine the rapid iteration DevOps depends on.
• Failing to align vendor incentives with DevOps outcomes. Vendors paid for hours worked have no incentive to automate. Vendors paid for uptime have no incentive to release frequently. Incentive structures must match desired behaviours.
• Inhibiting vendor competition. Single-vendor lock-in reduces negotiating leverage and limits the ability to adopt better tools or practices as the market evolves.

Transformation Planning Pitfalls

Even organisations that understand the cultural and technical dimensions of DevOps can fail through poor programme design. Transformation pitfalls are often the most preventable.

• Being too focused on tools. Tools implement practices. Without the underlying practices — continuous integration, infrastructure as code, blameless post-mortems — tools produce only the illusion of DevOps.
• Thinking DevOps is anything other than sustained process excellence. The goal is a permanent improvement in how software is built, delivered, and operated — not a one-time project.
• Trying to eat the elephant in one bite. Attempting a full organisational transformation simultaneously is a reliable way to overwhelm teams and produce nothing. Start with a pilot team, prove value, and expand.
• Creating a DevOps team disconnected from existing operations. A centralised DevOps team that doesn’t understand the organisation’s current systems, constraints, and culture will prescribe solutions that don’t fit.
• Treating DevOps as a short-term project. DevOps capability compounds over time. Organisations that declare success after six months and disband the effort lose momentum and regress.
• Missing a business case. Without a documented, measurable business case — tied to outcomes like deployment frequency, lead time, or customer satisfaction — DevOps investment cannot be justified or sustained.
• Not knowing the current state. Transformation requires a clear baseline. Without understanding existing process maturity, tooling, and team capability, there is no way to measure progress or identify the right starting points.
• Having no plan. Enthusiasm for DevOps without a structured roadmap, defined milestones, and accountable owners produces activity without progress.

Conclusion

DevOps is not a product, a tool, or a project. It is a sustained organisational capability built through deliberate cultural, process, and technology changes across every function that touches software delivery. The pitfalls described above are not theoretical — they are the patterns that consistently produce slower delivery, more outages, and higher costs when DevOps is adopted superficially rather than substantively.

Avoiding them requires honest self-assessment, strong leadership commitment, cross-functional collaboration, incremental adoption, and relentless measurement. Organisations that treat these as prerequisites rather than afterthoughts are the ones that realise the genuine benefits DevOps promises.

This article draws on perspectives originally published at capgemini.com.

Why DevOps Hiring Is So Difficult

DevOps sits at the intersection of software development and IT operations. A competent DevOps engineer must understand application development, infrastructure, networking, security, and automation — often simultaneously. That breadth of required knowledge makes the role genuinely hard to fill. On top of technical depth, DevOps demands strong interpersonal skills: the ability to break down organizational silos and foster continuous collaboration between teams that have historically worked in isolation.

The practical results of successful DevOps adoption speak for themselves. Organizations that have committed to DevOps methodologies report meaningful reductions in system downtime, faster application deployment cycles, and lower overall software delivery costs. Those outcomes drive executive demand — which in turn drives fierce competition for the people who can deliver them.

1. Articulate Your Own DevOps Vision First

Before you write a single job description, you need clarity on what DevOps means inside your organization. DevOps is not a single, universal implementation — it means different things in different contexts:

• Cultural transformation: Breaking silos so that developers, operations staff, DBAs, security engineers, and network administrators collaborate throughout the application lifecycle.
• Process automation: Using toolchains (CI/CD pipelines, infrastructure-as-code, configuration management) to reduce manual work and accelerate delivery.
• Both simultaneously: Most mature DevOps implementations combine cultural change with tooling.

Experienced DevOps engineers have career goals and preferences of their own. A candidate who thrives on greenfield automation work may leave quickly if your primary need is cultural change management. Misalignment between the role as sold and the role as lived is one of the most avoidable causes of early attrition. Define your DevOps vision internally before you hire externally.

2. Look for the Right Technical Skills

The skill set required of a strong DevOps engineer is genuinely broad. When evaluating candidates, look for demonstrated competence in as many of the following areas as possible:

• System build, configuration, and administration
• Virtualization and container technologies (e.g., VMware, Docker, Kubernetes)
• Infrastructure-as-code tools (e.g., Terraform, Ansible, Puppet, Chef)
• CI/CD pipeline design and management (e.g., Jenkins, GitLab CI, GitHub Actions)
• Scripting and automation (e.g., Python, Bash, Go)
• Cloud platform experience (AWS, Azure, GCP)
• Monitoring, observability, and incident response
• Security practices relevant to software delivery (DevSecOps)

It is rare to find all of these skills at an expert level in a single person. Identify which competencies are critical for your current needs and which can be developed on the job. Prioritizing a short, honest list of must-haves leads to better hiring decisions than chasing an impossible generalist unicorn.

3. Evaluate Attitude and Cultural Fit

Technical skills can be taught; attitude is much harder to change. DevOps requires people to collaborate across traditional IT boundaries, share ownership of outcomes, and embrace continuous learning. When interviewing, look for candidates who:

• Show genuine curiosity about systems and processes outside their primary specialty
• Can articulate how they have improved collaboration between development and operations teams
• Are comfortable with ambiguity and iterative improvement
• Take a service-oriented approach — they care about the outcome for end users, not just completing a ticket

It is also worth assessing your existing team’s readiness for change. Introducing a DevOps engineer into an organization that is resistant to the cultural shift can isolate that individual and undermine the entire initiative. Internal change management is as important as external hiring.

4. Find the DevOps Community Watering Holes

Passive job postings are unlikely to surface the best DevOps candidates. Experienced engineers in this field are typically employed and are not actively searching. To reach them, you need to be present where they gather:

• Professional networks: LinkedIn groups focused on DevOps, SRE, and platform engineering
• Conferences: DevOpsDays events (held in cities worldwide), KubeCon, HashiConf, and similar practitioner-focused conferences
• Online communities: Slack workspaces, Discord servers, and forums dedicated to specific tools (Kubernetes, Terraform, etc.)
• Local meetups: Many cities have active DevOps, SRE, and cloud-native user groups
• Open-source projects: Contributors to relevant projects are demonstrably skilled and motivated

Your own engineering staff are often the best source of referrals. They know the community, and candidates who come through employee referrals tend to be better matches and retain longer.

5. Grow Your Own Engineers

External hiring alone is not a sustainable DevOps talent strategy, particularly in a competitive market. Consider two internal pipelines:

• University partnerships: Engage computer science and information systems programs at nearby colleges. Early internship relationships with strong students allow you to evaluate potential hires before committing to a full-time offer. Some universities now offer DevOps-specific coursework or certificates.
• Internal upskilling: Audit your existing IT staff. Systems administrators, network engineers, and application developers who show initiative and curiosity about adjacent disciplines are strong candidates for DevOps cross-training. Providing formal training, mentorship, and stretch assignments is often more cost-effective than recruiting experienced engineers from outside.

Internal candidates already understand your systems, culture, and business domain — advantages that take months to develop in an external hire.

6. Use Staffing Agencies Selectively — Not Exclusively

Specialized IT staffing firms can accelerate your search, particularly when you need to fill a role quickly or lack internal recruiting capacity. However, there are important caveats:

• Avoid exclusive agreements. As your hiring effort progresses, you will develop your own candidate channels — referrals, community relationships, university pipelines — and you do not want a contractual obligation that prevents you from using them.
• Vet agencies for genuine DevOps expertise. Some generalist agencies lack the technical depth to screen DevOps candidates effectively. Look for firms whose recruiters can have a credible conversation about CI/CD pipelines or infrastructure-as-code.
• Use agencies as one channel among several, not as a replacement for a broader talent strategy.

7. Do Not Limit Yourself Geographically

The shortage of experienced DevOps engineers means that insisting on local candidates significantly reduces your addressable talent pool. Consider the following adjustments:

• Budget for relocation assistance when a candidate is an excellent match but lives outside commuting distance. For a senior-level DevOps role, relocation costs are modest relative to the cost of a prolonged vacancy or a bad hire.
• Embrace remote or hybrid work arrangements where your infrastructure and security posture allow it. Remote-friendly positions dramatically expand the candidate pool and are increasingly expected by experienced engineers.
• Consider international candidates if your organization has the capacity to navigate visa and employment requirements.

8. Convert Independent Consultants Into Permanent Hires

Many skilled DevOps professionals work as independent consultants or contractors. While daily rates for experienced independents are high, not every consultant prefers the instability of contract work indefinitely. Some actively look for the right permanent opportunity — one that offers technical challenge, organizational support, and long-term career development.

If you engage a DevOps consultant for a project and they perform well, it is worth having a frank conversation about whether a staff role would interest them. Offering a senior title with appropriate compensation and genuine technical ownership can be compelling to the right person. You already have direct evidence of their skills and working style — which removes much of the risk inherent in a traditional hire.

Putting It All Together

No single approach reliably fills a DevOps role in a competitive market. The most effective hiring strategies combine several of these practices simultaneously: maintaining active community presence, upskilling internal staff, keeping geographic constraints minimal, using agencies as one tool among many, and staying alert for consultants who may be ready for a permanent role.

Accept that finding the right DevOps engineer may take several months, and plan accordingly. A rushed hire that results in a poor cultural or technical fit is more disruptive and costly than a patient, disciplined search. Define what success looks like in your specific context, communicate that vision clearly to candidates, and invest in developing talent at every level — external hires, internal transfers, and early-career engineers alike.

1. Centralised Employee Management

Cloud payroll platforms consolidate employee data into a single source of truth. HR teams can track and manage the full employee lifecycle from one dashboard — onboarding, benefits enrolment, performance reviews, pay-rate changes, leave balances, certifications, and offboarding. Many platforms also allow you to log company assets assigned to employees, such as laptops, vehicles, and specialist tools, eliminating the need for separate asset registers.

2. Employee Self-Service

One of the most immediate productivity gains from cloud payroll is employee self-service. Staff get 24/7 online and mobile access to their payslips, tax documents, leave balances, benefits information, and personal details. They can update their own records without routing a request through HR. The knock-on effect is a measurable reduction in routine HR queries — fewer calls, fewer emails, and more time for HR teams to focus on strategic work. It also improves employee experience, because people are not waiting days for a payslip reprint.

3. Manager Self-Service and Mobile Access

Cloud platforms extend management capability beyond the physical office. Using a smartphone or tablet, a manager can approve leave requests, review and sign off timecards, correct missed clock-in punches, and view team schedules and employee records — without being tethered to a desktop or relying on HR as an intermediary. This is particularly valuable for organisations with distributed teams, field workers, or multiple sites.

4. Reporting and HR Analytics

Generating meaningful HR and payroll reports from on-premises systems often means exporting raw data, manipulating it in spreadsheets, and hoping nothing has changed by the time a decision is made. Cloud payroll systems store all data centrally and provide built-in report builders and dashboards. Common reports — headcount by department, leave liability, salary cost by cost centre, turnover rates — can be run on demand. Custom reports can be created without IT involvement, giving HR and finance leaders a real-time view of workforce costs and trends.

5. Statutory Compliance, Automatically Maintained

Tax codes, provident fund rules, labour laws, and payroll compliance requirements change regularly. Keeping an on-premises payroll system up to date with every legislative change requires dedicated effort — and missing an update can result in penalties. Cloud payroll vendors push compliance updates to all customers as part of the subscription, so your system always reflects current rules. This typically covers statutory filings, tax deduction calculations, and jurisdiction-specific reporting requirements. The compliance burden shifts largely from your team to your vendor.

6. Lower Total Cost of Ownership

On-premises payroll systems carry significant hidden costs: server hardware, operating system licences, database licences, backup infrastructure, security patching, and the internal IT headcount needed to maintain all of the above. Cloud payroll replaces that capital expenditure model with a predictable operating expenditure — typically a per-employee, per-month subscription. There is no physical infrastructure to procure, maintain, or eventually decommission. For most small and mid-sized organisations, this represents a significant reduction in both upfront investment and ongoing running costs.

7. Reduced Environmental Impact

Running a dedicated on-premises server room consumes electricity continuously — for compute, cooling, and physical security. Eliminating a local server in favour of a shared cloud data centre reduces an organisation’s direct energy consumption. Major cloud providers operate at a scale that allows them to optimise energy efficiency far beyond what an individual company can achieve in its own server room, and many have commitments to renewable energy usage. For organisations with sustainability targets, moving payroll to the cloud is a straightforward way to reduce the carbon footprint of their IT estate.

Is Cloud Payroll Right for Your Organisation?

Cloud payroll software is not a single product — it ranges from lightweight tools aimed at small businesses to enterprise platforms that integrate with ERP, time-and-attendance, and benefits administration systems. When evaluating vendors, prioritise:

• Data security and encryption: Confirm that data is encrypted in transit and at rest, and understand where your data is physically stored.
• Compliance coverage: Verify that the platform covers the specific tax jurisdictions and statutory requirements relevant to your workforce locations.
• Integration capability: Check whether the platform integrates with your existing HR, accounting, or ERP systems via API or pre-built connectors.
• SLA and uptime guarantees: Payroll cannot miss a run. Review the vendor’s published uptime commitments and disaster recovery policies.
• Support model: Understand the support tier included in your subscription and what escalation paths exist at year-end or during complex payroll periods.

A well-chosen cloud payroll platform reduces administrative overhead, keeps compliance current, and gives both employees and managers the self-service access they expect from modern business software. For most organisations, the operational and financial case for switching is straightforward — the main work is in selecting the right vendor and planning the migration carefully.