Why Look Beyond Jenkins?

Jenkins is one of the most widely adopted open-source CI/CD tools in the Java ecosystem. Its massive plugin library, active community, and flexibility make it a default choice for many teams. However, Jenkins is not universally the right fit. Maintenance overhead, a dated UI, complex plugin management, and limited native support for modern cloud-native workflows lead many teams to evaluate alternatives.

The five tools below cover a broad range of team sizes, workflows, and infrastructure preferences. Each addresses a different pain point that Jenkins users commonly encounter.

1. Atlassian Bamboo — Best for Teams Already in the Atlassian Ecosystem

Atlassian offers a tightly integrated suite of DevOps tools: Jira for issue tracking, Bitbucket for source control, and Confluence for documentation. If your organization already relies on one or more of these products, Bamboo is a natural extension of that ecosystem rather than an isolated CI server.

• Native Jira and Bitbucket integration: Build results, deployment statuses, and test results surface directly in Jira tickets and Bitbucket pull requests without extra configuration.
• Agents and plans: Bamboo uses a plan-and-stage model to define build pipelines, which maps well to teams familiar with Jira’s project structure.
• Licensing model: Bamboo is a commercial product. Teams moving from free Jenkins should factor in licensing costs.

Best fit: Organizations that have standardized on Atlassian tooling and want a CI/CD server that shares the same vendor support, user management, and pricing tier.

2. GitLab CI/CD — Best for Teams Hosted on GitLab

GitLab is more than a Git hosting service. Its built-in CI/CD feature set makes it a full DevOps platform covering source control, issue tracking, container registry, security scanning, and deployment pipelines—all within a single interface.

• Pipeline-as-code: Pipelines are defined in a .gitlab-ci.yml file committed to the repository, keeping pipeline configuration version-controlled alongside application code.
• Runners: GitLab uses distributed runners (similar to Jenkins agents) that can be self-hosted or provided by GitLab’s SaaS offering.
• No separate CI server to maintain: For teams already on GitLab, there is no additional infrastructure to manage for basic CI/CD workflows.
• GitHub migration path: Organizations that moved from GitHub to GitLab—especially following Microsoft’s acquisition of GitHub—often find it practical to consolidate on GitLab’s CI tooling rather than maintaining a separate Jenkins instance.

Best fit: Teams that host code on GitLab and want to eliminate the overhead of managing a separate CI server.

3. Spinnaker — Best for Cloud-Native Continuous Delivery

Spinnaker was originally developed at Netflix and open-sourced to support complex, multi-cloud continuous delivery at scale. It is worth noting that Spinnaker is primarily a continuous delivery (CD) tool rather than a full CI solution—it is often paired with a separate CI system such as Jenkins or GitHub Actions to handle the build phase.

• Multi-cloud deployments: Spinnaker has first-class integrations with AWS, Google Cloud, Microsoft Azure, Kubernetes, and VMware, among others.
• Deployment strategies: It supports advanced deployment patterns such as blue/green, canary, and rolling deployments out of the box.
• Artifact management: Spinnaker tracks versioned artifacts (Docker images, Debian packages, etc.) through their entire delivery lifecycle.
• Operational complexity: Spinnaker has a steeper learning curve and higher operational overhead than Jenkins. It is better suited to platform engineering teams than to small development groups.

Best fit: Organizations deploying to multiple cloud providers or Kubernetes clusters that need sophisticated deployment strategies and artifact promotion pipelines.

4. TeamCity — Best for Developer-Centric Agile Teams

TeamCity is a CI/CD server developed by JetBrains, the company behind IntelliJ IDEA, the dominant Java IDE. That heritage is evident in TeamCity’s developer-focused feature set.

• IDE integration: TeamCity integrates with IntelliJ IDEA (and other JetBrains IDEs) so developers can inspect build results, run personal builds, and monitor pipelines without leaving their development environment.
• Build configuration DSL: Pipelines can be defined as Kotlin DSL scripts, which are version-controlled and reviewable like application code.
• Test intelligence: TeamCity tracks test history and can reorder test execution to surface failing tests faster—a concrete productivity improvement for Java teams with large test suites.
• Free tier available: JetBrains offers a free tier with a limited number of build configurations, making TeamCity accessible for smaller teams evaluating it before committing to a license.

Best fit: Java development teams that prioritize developer experience, already use JetBrains IDEs, or want Kotlin-based pipeline-as-code.

5. CloudBees CI — Best for Teams That Want Enterprise Jenkins

If the real issue with open-source Jenkins is not the tool itself but the lack of professional support, governance, and enterprise-grade reliability, CloudBees CI (formerly CloudBees Core) is worth evaluating. It is built on top of Jenkins and extends it rather than replacing it.

• Enterprise support: CloudBees provides SLAs, security patches, and certified plugin versions—addressing one of the most common complaints about managing open-source Jenkins at scale.
• Multi-team operations: CloudBees CI supports a managed controller model where individual teams can have isolated Jenkins instances managed from a central operations center, reducing the “shared Jenkins” problem.
• Deployment options: Available as an on-premises installation or as a cloud-hosted service, including Kubernetes-native deployments.
• Pipeline compatibility: Existing Jenkins pipelines (Jenkinsfiles) run on CloudBees CI without modification, lowering migration risk.

Best fit: Enterprises that have invested heavily in Jenkins pipelines and want professional support, better multi-team governance, and a supported upgrade path rather than a full tool migration.

Choosing the Right Jenkins Alternative

No single tool is the right answer for every team. Use the table below as a quick reference when evaluating your options:

The right choice depends on your existing toolchain, team size, cloud strategy, and budget. Start by identifying the biggest pain points with your current Jenkins setup—plugin management, lack of support, cloud-native deployment complexity, or poor developer experience—and let that drive the evaluation.

Why Physical DevOps Conferences Still Matter

With so much technical content available online, it is tempting to skip in-person events entirely. But for DevOps professionals, conferences offer something that asynchronous learning cannot fully replicate: concentrated time away from day-to-day work to focus on the bigger picture, ask questions of peers in real time, and build the kind of professional relationships that accelerate career growth.

The pace of software innovation means that practitioners must constantly absorb new skills, adapt to shifting job roles, and stay current with rapidly evolving toolchains. Conferences compress months of scattered learning into a few intense days. They are also one of the most effective ways to raise your profile in the DevOps community, whether by speaking, participating in workshops, or simply engaging with thought leaders you would otherwise only read about.

Key Themes Across 2020 DevOps Events

A scan of the 2020 DevOps conference calendar reveals several recurring themes that reflect where the industry is heading:

• Test automation maturity: Deciding which tests belong in the pipeline, applying AI and ML to test selection, and achieving quality visibility remain top challenges for continuous delivery.
• Shifting left and shifting right: DevSecOps and observability practices (sometimes called DevAPMOps) are expanding the traditional DevOps scope in both directions.
• Open source and commercial integration: Mixing open source frameworks with enterprise tooling is a central operational challenge.
• Leadership and culture: Agile and DevOps transformations increasingly require executive buy-in, and conference tracks aimed at managers reflect this.

10 DevOps Conferences to Consider in 2020

1. QA or the Highway — February 25, Columbus, OH

A software testing event designed to connect the dots between testing and DevOps. Sessions cover shift-left practices, agile testing approaches, and the specific challenges that hold back DevOps maturity. Well suited for test automation engineers and QA practitioners looking to understand how their work fits into the broader DevOps pipeline.

2. Triangle Software Quality Association (TSQA) — February 26–27, Durham, NC

TSQA focuses on software test automation challenges and how improved testing practices help development teams in mobile and web environments move faster without sacrificing quality. A practical event for practitioners who want actionable takeaways on automation strategy.

3. SCALE 18x — March 5–8, Pasadena, CA

The Southern California Linux Expo (SCALE) is one of the largest community-run open source and free software conferences in North America. The 2020 edition is expected to feature approximately 150 exhibitors and 130 sessions, tutorials, and special events. A dedicated DevOps track runs on Friday, March 5, making it a high-value option for practitioners who want open source exposure alongside DevOps content.

4. TechWell Events: Epic, STAREAST, and STARWEST — April through October

Dates: Epic (April 19–23, San Diego, CA), STAREAST (May 3–8, Orlando, FL), STARWEST (October 4–9, Anaheim, CA)

TechWell’s flagship testing conferences are among the most established software quality events in the United States. Topics span agile practices (BDD, TDD, continuous testing), test automation frameworks, unit testing, security, and application accessibility. These events serve both individual contributors and managers, with separate tracks and workshop formats designed for each audience.

5. DevOpsCon — April 21–24, London

DevOpsCon covers the full modern software delivery stack: continuous delivery, microservices, containers, cloud infrastructure, and lean business practices. Attendees can engage with DevOps thought leaders through keynotes, technical sessions, and in-depth workshops. Suitable for engineers who want to advance their skills in cloud-native delivery pipelines.

6. Agile + DevOps West — June 7–12, Las Vegas, NV

One of TechWell’s largest west coast events, Agile + DevOps West covers the full software delivery lifecycle, from development and agile process through testing and release engineering. A practical fit for software developers, release managers, test engineers, and test managers who want cross-functional perspective on DevOps delivery.

7. DevOps Enterprise Summit — June 23–25, London

Focused specifically on large, complex organizations undergoing DevOps transformation, this summit features practitioners and leaders sharing candid accounts of what works and what does not. The audience skews toward executives, directors, and senior managers who need strategic frameworks rather than hands-on tooling tutorials.

8. DevOps World — September 21–24, Las Vegas, NV

Organized by CloudBees, DevOps World is dedicated to CI/CD pipeline maturity and building effective DevOps strategies at scale. The conference draws DevOps specialists, release managers, and test managers who want exposure to leading tools and practices in automated deployments, cloud CI, and continuous delivery. Jenkins and related tooling feature prominently in the session lineup.

9. QA Financial and QA Retail Events — Multiple Dates and Cities

These vertical-focused events bring together practitioners from banking and retail enterprises to share success stories and operational practices relevant to high-stakes, regulated delivery environments. Content tends to be more strategic and is most relevant to senior managers in testing and development functions within those industries.

10. All Day DevOps — November 12, Online

A 24-hour online event featuring approximately 150 practitioner speakers across five tracks: Cultural Transformation; DevSecOps; CI/CD Continuous Everything; Cloud Native & Infrastructure Monitoring; and Site Reliability Engineering. The fully virtual format removes travel barriers and makes it accessible to practitioners worldwide. Ideal for DevOps specialists who want peer-led learning without a conference budget.

Making the Most of Conference Attendance

Whether you attend as a delegate, workshop participant, or speaker, the return on investment from the right conference depends on how you engage. A few practical suggestions:

• Set a learning goal before you go. Identify one or two specific skills or questions you want to address. This helps you choose sessions deliberately rather than passively.
• Prioritize hallway conversations. Scheduled sessions are valuable, but unplanned conversations with peers often produce the most immediately actionable insights.
• Submit a talk proposal. Speaking at a conference is one of the fastest ways to establish credibility in the DevOps community and forces you to structure and validate your own thinking.
• Document and share takeaways. Writing up what you learned, even in an internal post or team meeting, reinforces your own learning and builds visibility within your organization.

In a field that evolves as rapidly as DevOps, making deliberate space for learning outside your normal work environment is not a luxury — it is part of the job.

If the answer is no, you may not need DevOps or containers at all—or you may need only selective pieces of what these ecosystems offer. This article helps you think through that decision honestly.

What DevOps Is—and Is Not

DevOps is a set of cultural practices, workflows, and tooling designed to tighten the feedback loop between software development teams and IT operations teams. The goal is to deploy software faster, more reliably, and with fewer manual handoffs. When an organization ships its own applications, DevOps can dramatically reduce release cycle times and improve system stability.

However, DevOps is also a broad umbrella. It encompasses CI/CD pipelines, infrastructure-as-code, configuration management, monitoring, and administrative automation. These individual capabilities have value outside of software development contexts. A healthcare organization that purchases and runs third-party applications, for example, may have no use for a CI/CD pipeline but could benefit significantly from automated configuration management and infrastructure provisioning.

The practical takeaway: if you do not write software, you can ignore the software-delivery portions of DevOps while still selectively adopting its automation tooling. Tools like Ansible, Puppet, Chef, and Terraform are frequently marketed as DevOps tools but solve general administrative automation problems that apply to any data center.

The Vendor Pressure Problem

Major infrastructure vendors increasingly orient their product roadmaps around DevOps and container-native workflows. This creates a real challenge for organizations that do not operate in that model. Features that matter to traditional sysadmin workflows can receive less investment over time as vendors chase the developer-experience market.

You cannot control a vendor’s product strategy, but you can respond to it deliberately:

• Vote with your budget. Make it explicit to your vendor account team which features you depend on and which DevOps-centric additions add no value for your use case.
• Evaluate the broader market. Large vendors are not the only option. Smaller or more specialized vendors sometimes maintain stronger support for traditional operational models.
• Track roadmap announcements carefully. When a core product begins wrapping essential functionality inside a container or Kubernetes abstraction, you need lead time to evaluate alternatives.

Containers: A Developer Technology First

Containers are lightweight, isolated runtime environments that share the host operating system kernel rather than emulating full hardware as a virtual machine does. Kubernetes is the dominant container orchestration system, managing scheduling, scaling, networking, and self-healing across clusters of container hosts.

Containers are fundamentally a software packaging and deployment technology. They exist to solve the problem of running custom application code consistently across development, staging, and production environments. If your data center runs purchased or licensed applications that ship as traditional installers or virtual appliance OVFs, standing up a Kubernetes cluster provides essentially no operational benefit—you would have nothing meaningful to run inside it.

Containers also introduce a distinct security surface compared with traditional VMs. Container escape vulnerabilities, image supply-chain risks, and the complexity of network policy in orchestrated clusters are real concerns that require dedicated expertise to manage. Adopting containers without a genuine use case means accepting those risks for no return.

Where Containers and Kubernetes Can Still Apply

Even organizations that do not develop software may encounter containerized workloads as vendors increasingly ship their products as container images or Helm charts. In that scenario, some familiarity with container operations becomes necessary—not because containers are inherently a better fit, but because the software you buy arrives that way.

If this trend affects your vendor portfolio, the appropriate response is building enough operational knowledge to run and maintain those workloads safely—not building a full DevOps practice around continuous delivery.

How to Make the Decision for Your Organization

Use these questions to guide an honest assessment:

1. Does your organization write, maintain, or release custom software? If yes, DevOps practices and potentially containers are directly relevant. If no, proceed to the next questions.
2. Are any of your purchased applications now delivered as container images? If yes, you need basic container operational skills even without a DevOps practice.
3. Do your current administrative workflows involve significant manual, repetitive work? If yes, DevOps-adjacent automation tooling can help regardless of whether you ship software.
4. Is your primary vendor tying critical features exclusively to container or Kubernetes infrastructure? If yes, evaluate whether an alternative vendor better serves your model, and factor migration cost into that decision.
5. Does your team have the capacity and interest to develop and maintain new skills in these areas? Adopting a technology your team cannot operate competently creates operational risk.

Remaining Flexible Without Chasing Every Trend

IT infrastructure evolves continuously. The cloud, software-defined networking, hyperconverged infrastructure, and now containers and DevOps have each arrived with significant industry momentum. Not every organization needed to adopt each of these shifts at the same time or to the same depth.

The organizations that manage these transitions well tend to share a common approach: they evaluate new technologies against specific, documented operational problems rather than against abstract trends. They build enough awareness to understand what is changing in their vendor ecosystem, and they maintain the flexibility to adjust when a forced transition genuinely becomes necessary.

Holding a well-reasoned, evidence-based position that containers are not appropriate for your data center today is not a failure to keep up. It is sound engineering judgment. The risk is only in holding that position so rigidly that you cannot adapt when your environment changes around you.

Summary

• DevOps is primarily valuable for organizations that develop and deploy their own software.
• The automation subset of DevOps tooling has broader applicability and can benefit any data center.
• Containers and Kubernetes are software-developer technologies; running them without custom application code to deploy inside them provides little operational value.
• Vendor roadmap shifts toward container-native delivery may force some operational familiarity even without a full DevOps adoption.
• Evaluate technologies against your specific operational needs, maintain awareness of vendor direction, and preserve the flexibility to adapt when circumstances genuinely require it.

The Capital Flood: Key Deals Driving the Conversation

The scale of recent investment in DevOps and observability vendors illustrates how seriously the market is taking software delivery as a business-critical function:

• GitLab raised $268 million in a funding round (announced September 2019), reflecting strong demand for integrated source-control and CI/CD platforms.
• Splunk acquired SignalFx — provider of a cloud-native application performance monitoring (APM) platform — for just over $1 billion, signaling that full-stack observability is now considered a strategic enterprise asset.
• New Relic raised $115 million after its 2014 IPO, an early indication of investor appetite for application monitoring at scale.
• Cisco acquired AppDynamics for approximately $3.7 billion in 2017, one of the largest APM acquisitions on record at the time.

These deals share a common thread: buyers and investors are betting that visibility into running software — not just the code itself — is where differentiated value lives.

Why Now? Organizations Are Finally Acting Like Software Companies

For years, DevOps adoption was uneven. Teams would adopt CI/CD pipelines in one department while the rest of the organization still relied on manual release processes. What has changed is the recognition — now reaching the boardroom — that every delay in software delivery has a direct revenue consequence.

Lew Cirne, CEO of New Relic, put it plainly: “If mission-critical software is not working, then the business is not working. You must watch it all; you can’t pick and choose.”

That framing — software uptime equals business uptime — is driving demand for continuous, comprehensive monitoring that spans the entire application lifecycle, not just production incidents.

The Technical Challenges Fueling Platform Investment

Delivering on that promise of full-lifecycle observability is technically hard, and getting harder. Arijit Mukherji, CTO of SignalFx at the time of its acquisition, summarized the core challenge: “The scale of the data has increased. Everything needs to happen in real-time.”

Several architectural trends are compounding the complexity:

Microservices and Distributed Systems

Breaking a monolithic application into microservices trades one set of problems for another. Each service boundary introduces a new failure point and a new dependency to track. Distributed tracing — propagating a trace ID across service calls so you can reconstruct a request’s full path — becomes essential, not optional. Matt Cleve, director and global lead for Business iQ at AppDynamics, noted that “real-time visibility into the entire application stack is going to be a lot more important” as microservice architectures proliferate.

AI and Machine Learning Integration

Modern observability platforms are incorporating machine learning to move beyond threshold-based alerting toward anomaly detection and predictive failure analysis. This requires platforms to ingest and analyze metric, log, and trace data at a scale that simple time-series databases were not designed to handle. The vendors raising and spending this capital are building the infrastructure to support ML-driven operations (AIOps) as a first-class feature.

The Real-Time Visibility Gap

A survey conducted by the Harvard Business Review on behalf of AppDynamics found that only 22% of IT organizations had access to any kind of real-time IT metrics at the time of publication. That gap represents both the problem and the market opportunity that these investments are targeting.

Platform Consolidation: What It Means for Engineering Teams

The acquisitions and funding rounds described above are not just financial events — they have practical implications for teams evaluating or currently using these tools.

• Vendor consolidation reduces tool sprawl. When an APM vendor is acquired by a network infrastructure company (as AppDynamics was by Cisco), the stated goal is to correlate application-layer telemetry with network and infrastructure data in a single pane of glass. Whether that integration delivers in practice depends heavily on execution.
• Larger balance sheets fund R&D. Standalone startups racing to build distributed tracing, ML-based alerting, and CI/CD integration simultaneously are constrained by capital. Acquisition by or investment from a larger company accelerates that roadmap — though it can also introduce product direction changes that don’t align with existing users’ needs.
• Lock-in risk increases. As platforms grow broader, switching costs rise. Teams should evaluate observability and CI/CD platforms against open standards (OpenTelemetry, for example) to preserve portability.
• Pricing models evolve. High-volume, real-time telemetry is expensive to store and query. Expect pricing models tied to data ingestion volume, host count, or active users to shift as vendors absorb the cost of operating at greater scale.

Beyond CI/CD: DevOps as an Organization-Wide Discipline

The investment wave reflects a maturation in how DevOps is understood. Early adopters focused on CI/CD pipelines — automating build, test, and deploy. That work is necessary but not sufficient. What the market is now pricing in is the operational half of the DevOps loop: continuous monitoring, feedback, and improvement across the full application lifecycle.

Organizations that have siloed DevOps in a single team or pipeline stage are under competitive pressure to extend those practices organization-wide. That means:

• Instrumenting applications from the first commit, not as an afterthought before production.
• Making telemetry data accessible to developers, not just operations teams.
• Treating software delivery metrics (lead time, deployment frequency, mean time to restore) as business KPIs, not just engineering vanity metrics.
• Building feedback loops between production behavior and backlog prioritization.

The Bottom Line

The capital flowing into DevOps tooling is not a speculative bubble — it reflects genuine enterprise demand for platforms that can operate at the scale and speed that modern software delivery requires. For engineering teams, the practical takeaway is straightforward: observability is no longer a nice-to-have feature appended to a monitoring checklist. It is a core engineering discipline, and the vendors competing for that budget are making billion-dollar bets on it.

The organizations that build robust, organization-wide DevOps practices — including continuous monitoring, real-time feedback, and AI-assisted operations — are positioning themselves to outpace competitors who are still treating software delivery as a cost center rather than a revenue driver.

Understanding “Olde World” IT: Why DevOps Exists

Understanding where DevOps came from is key to understanding what it actually is. To appreciate the movement, it helps to look at what IT operations looked like in the late 1990s and through most of the 2000s.

In large enterprises of that era, adding compute capacity meant placing multi-hundred-thousand-dollar hardware orders — servers, networking gear, cables, and software licenses — destined for on-site and off-site data centers. VMware was still convincing skeptical CFOs that virtual machines were cost-effective. Many companies ran applications directly on physical hardware. Entire teams were dedicated to data center operations: negotiating lease rates, managing cooling, and ensuring offshore night-shift staff could identify the right server rack without pulling the wrong cable during after-hours trading.

Software releases were similarly painful. The typical workflow looked something like this:

• Business analysts gathered requirements in meetings developers were not invited to attend.
• Developers wrote code to those requirements, optionally writing unit tests along the way.
• When code was marked “Ready for QA,” a quality assurance engineer picked it up and ran it in their own environment — which might bear little resemblance to production or even the developer’s local setup.
• Failures were sent back to developers days or weeks later, depending on business priorities.

Both sysadmins and developers shared one common frustration: change management. Most enterprises followed ITIL practices, which imposed heavily regulated processes governing when and how technical changes could happen. The intent was to minimize costly mistakes. In practice, this meant releases only every two weeks and all server changes — regardless of size or impact — batched into a Friday-evening-to-Monday-morning maintenance window. Ironically, this batching of work often led to even larger, more complex failures when things went wrong.

DevOps Is Not a Tiger Team

One of the most persistent and damaging misconceptions about DevOps is that it is a team, a department, or a Jira project. You may have encountered this in practice: a “DevOps team” that owns the CI/CD pipeline, a “configuration management group” that has to approve your deployment, or release planning meetings scheduled weeks after code was marked complete. If that sounds familiar, you are watching history repeat itself — the same silos, just with new labels.

Silos form naturally. People gravitate toward colleagues who share their context and vocabulary. In software companies, as codebases grow in complexity, developers working on related features cluster together. What starts as informal collaboration becomes official team structures. That is entirely human. The problem arises when those structures harden into walls, and the walls generate friction, blame, and waste.

In the pre-DevOps world, developers and sysadmins not only worked in separate silos — they actively competed. Developers blamed sysadmins for broken or overly locked-down environments. Sysadmins blamed developers for making arbitrary changes that destabilized shared systems and demanding more compute than they actually needed. Neither side understood the other’s constraints, and neither side was particularly motivated to learn. Developers threw environment problems over the wall; sysadmins prioritized them among hundreds of other requests; everyone waited angrily.

DevOps is a way of thinking, not an org chart entry. The movement’s core premise is that developers, sysadmins, and business stakeholders should function as a unified team. They do not need to know everything about each other’s domains, but they need to know enough to understand each other’s work, read each other’s backlogs, and speak a shared language. This is the philosophical foundation behind infrastructure as code, shared deployment pipelines, and accessible monitoring dashboards — practices that make the entire system legible to everyone involved.

As Chef co-founder Adam Jacob put it: “DevOps is the word we will use to describe the operational side of the transition to enterprises being software led.”

What Do You Actually Need to Know to Get Into DevOps?

The honest answer is: it depends on the company. The “DevOps engineer” role varies significantly by organization size, maturity, and existing team composition.

• Smaller companies with many developers but limited infrastructure expertise tend to hire people with strong systems administration backgrounds who can also write code.
• Larger or older organizations with established sysadmin teams often look for something closer to what Google calls a Site Reliability Engineer — essentially a software engineer who designs and improves operational functions.

Regardless of company size, the following skill areas appear consistently in DevOps roles:

Cloud Platforms

Knowing how to architect and administer secure, scalable cloud environments is table stakes. AWS is the most common platform to encounter, but Azure, Google Cloud Platform, and managed PaaS providers like DigitalOcean and Heroku are also widely used. Focus on understanding core networking concepts (VPCs, subnets, security groups), IAM and access controls, compute and storage services, and how to design for resilience.

CI/CD Pipelines

Continuous integration and continuous delivery are the mechanical heart of DevOps practice. Understanding how to build, optimize, and troubleshoot deployment pipelines is essential. Common tools include Jenkins, GoCD, Travis CI, CircleCI, and increasingly GitHub Actions and GitLab CI. Learn not just how to configure these tools but why specific pipeline strategies — blue/green deployments, canary releases, feature flags — exist and what problems they solve.

Monitoring, Logging, and Alerting

You cannot operate what you cannot observe. Familiarity with time-series metrics tools (Grafana, Prometheus), log aggregation platforms (the Elastic Stack including Kibana and Logstash, Splunk, Loggly), and alerting pipelines is expected. The goal is not just to collect data but to derive actionable signals from it.

Infrastructure as Code

Manually configured infrastructure is a liability: it is hard to reproduce, hard to audit, and hard to recover from. Configuration management tools like Ansible, Chef, and Puppet allow you to define system state in version-controlled code. Provisioning tools like Terraform and AWS CloudFormation let you declare entire infrastructure topologies the same way. This discipline is central to DevOps because it makes infrastructure legible, testable, and collaborative.

Containers and Orchestration

Containers have become a standard unit of deployment. Docker is the dominant container runtime, with alternatives like containerd now widely used under the hood. Kubernetes has emerged as the standard orchestration platform for running containers at scale, handling scheduling, self-healing, scaling, and service discovery. Understanding how to build, publish, and run container images, and how to write basic Kubernetes manifests, will serve you well in almost any DevOps role.

Skill Gaps: What Sysadmins and Developers Each Need to Learn

If You’re Coming from Systems Administration

You will need to learn to write code — not just shell scripts, but real programming. Python and Ruby are the most practical starting points for this path. Both are portable across operating systems, readable, and widely used in the DevOps ecosystem. Python underpins Ansible and is the dominant language for AWS, Azure, and GCP SDKs. Ruby is the foundation of Chef and Puppet. Either language will open doors; Python has somewhat broader adoption today.

Focus on writing clean, testable automation scripts before attempting to build complex tooling. The ability to write a reliable Python script that interacts with a cloud API or parses logs is immediately valuable in a DevOps role.

If You’re Coming from Software Development

Invest time in Unix fundamentals, basic Windows Server administration, and networking. Even though cloud platforms abstract away much of the complexity of raw infrastructure, debugging slow application performance, network latency, or mysterious crashes is dramatically easier when you understand what happens below the application layer. Learn how processes, file descriptors, TCP/IP, DNS, and HTTP actually work. This knowledge pays dividends every time something breaks in production — which it will.

Hands-On Projects to Build Your Skills

Reading about DevOps is a starting point; doing it is how you actually learn. Here are practical ways to build experience:

Deploy a Real Application Through a Full Pipeline

Take an open-source application from a public GitHub repository and build everything required to run it in production: a cloud environment provisioned with Terraform or CloudFormation, a CI/CD pipeline that runs tests and deploys on merge, and monitoring that alerts on errors and latency. Doing this end-to-end — even for a toy application — teaches you more than any course because you will encounter real failure modes.

Replicate a Manual Setup with Infrastructure as Code

Choose a common service — a web server, a database cluster, a message queue — and set it up manually on a cloud provider first. Document every step. Then tear it down and rebuild the entire thing using only Terraform (or CloudFormation) and Ansible. This two-pass approach forces you to understand what you are automating before you automate it, and the resulting code is something you can show to potential employers.

Run Containers Locally, Then in the Cloud

Start by containerizing an existing application using Docker. Run it locally with Docker Compose to simulate a multi-service environment. Then push the images to a container registry and deploy them to a managed Kubernetes service (Amazon EKS, Google GKE, or Azure AKS all offer free-tier or low-cost options for learning). Working through the networking, storage, and configuration challenges of a real Kubernetes deployment will sharpen your skills quickly.

Final Thoughts

Getting into DevOps is not about memorizing a tool list. It is about developing a mindset that treats infrastructure as software, values collaboration over hand-offs, and prioritizes feedback loops that make systems safer and faster to change. The tools matter, but they exist in service of that philosophy.

Start with the fundamentals — cloud basics, scripting, and a CI/CD pipeline — and build outward from there. Pick one real project, make it actually work in production, and you will have learned more in that process than in months of passive study.

This article draws on content originally published at opensource.com, adapted and expanded for a DevOps engineering audience.

The Core Problem: Legacy Networks in a Cloud-Native World

Cloud-native application development demands connectivity that is as flexible and fast as the development process itself. Traditional networks were not built with this in mind. Two legacy technologies in particular remain a persistent pain point for DevOps engineers:

• MPLS networks: Designed for predictable, hub-and-spoke enterprise traffic, MPLS is rigid and expensive to reconfigure. It was not built for hybrid cloud or multi-cloud architectures where workloads move dynamically between providers.
• VPN gateways and bastion hosts: VPN connections were originally designed for occasional remote access to static infrastructure. Repurposing them for continuous, high-throughput developer access to cloud environments introduces latency, operational complexity, and bottlenecks that directly slow down delivery pipelines.

Modern enterprises increasingly rely on multiple public cloud providers simultaneously, seeking both risk distribution and access to each provider’s unique services. Developers working in these multi-cloud environments need connectivity that natively understands that topology—not network infrastructure that treats the cloud as an afterthought bolted onto an on-premises model.

The Real Cost of Outdated Networking

Legacy connectivity approaches do not just cause technical friction—they create business risk. Misconfigured VPN tunnels, manual firewall rule changes, and slow provisioning cycles can delay the rollout of a new application feature by days or weeks. In competitive markets where time to market is a strategic differentiator, that kind of delay directly erodes a team’s ability to deliver value. Complexity in the network layer also increases the likelihood of human error, which in turn introduces security vulnerabilities.

Four Properties DevOps Teams Should Demand from a Network Solution

Whether evaluating a new SD-WAN vendor, a cloud networking product, or a software-defined connectivity platform, DevOps engineers and platform teams should insist on four fundamental capabilities:

1. Agility

Network provisioning should keep pace with application deployment. On-demand connectivity means developers can spin up or tear down network paths across cloud environments without raising a ticket with a network operations team and waiting days for a change window. Application-specific networking models allow development teams to define connectivity requirements as part of the application lifecycle itself—closer in spirit to infrastructure-as-code than to traditional network engineering.

2. Security

Perimeter-based security models break down in multi-cloud environments where there is no single, well-defined edge. Modern network solutions should implement a zero-trust security model, granting access based on verified identity and context rather than network location. Granular access controls—down to the individual service or API endpoint—reduce blast radius if credentials are compromised and make it easier to demonstrate compliance with regulatory requirements.

3. Simplicity

Multi-cloud environments are inherently complex, but that complexity should be abstracted away from application developers. A well-designed cloud-native networking solution presents a unified interface regardless of whether endpoints are in a public cloud, a private data center, or on a mobile device. Developers should be able to declare what connectivity they need—not configure how to achieve it at the protocol level.

4. Performance

Legacy MPLS and VPN solutions often introduce latency and throughput ceilings that are incompatible with the demands of real-time data pipelines, microservices architectures, and CI/CD feedback loops. Cloud-native connectivity solutions are designed to route traffic efficiently across modern infrastructure, reducing operational overhead compared to dedicated MPLS circuits while delivering consistent, measurable throughput improvements over traditional VPN tunnels.

What Application-First Networking Looks Like in Practice

The emerging category sometimes described as application-specific networking or cloud-native networking is a software-only approach that treats connectivity as a programmable resource rather than a physical constraint. Key characteristics include:

• API-driven provisioning: Network paths can be created, modified, and destroyed through the same automation pipelines that manage infrastructure and application deployments.
• Policy-as-code: Access controls and routing policies are defined in version-controlled configuration files, enabling peer review and audit trails.
• Separation of the data plane from the control plane: Changes to routing logic and access policy do not require hardware reconfiguration or maintenance windows.
• Native multi-cloud support: The solution understands the networking constructs of major cloud providers and can establish connectivity between them without requiring a physical intermediary.

Conclusion

The DevOps movement has successfully modernized software development, testing, and deployment. Networking is the last major domain where many teams are still operating with tools and mental models from a pre-cloud era. Legacy MPLS circuits and repurposed VPN gateways impose scalability limits, agility penalties, and security risks that are fundamentally at odds with cloud-native development practices.

Treating networking as a first-class concern in DevOps—subject to the same expectations of automation, observability, and rapid iteration as any other part of the stack—is not a nice-to-have. It is a prerequisite for organizations that want their platform teams to remain competitive.

The Gym Membership Problem

Many organizations treat DevOps the way most people treat a gym membership in January: they talk big, sign up enthusiastically, and then never show up. The rationalization is familiar — “We’re a bank, not a streaming video service” — as if the industry you operate in exempts you from the pressure to ship better software, faster.

But this reasoning misses the point entirely. The question isn’t whether you can match Google or Netflix in DevOps maturity. The question is whether you can outpace the other players in your market. Think of it like two hunters who encounter a bear in the woods: you don’t have to outrun the bear — you just have to outrun the other hunter. In a competitive market, DevOps is your running shoes.

DevOps Is a Survival Imperative, Not a Trend

One reason organizations avoid committing to DevOps is the perception that it’s just another passing fad — the “juice cleanse of software development.” Slogans like “DevOps is about culture, not tools” can feel abstract and unactionable, making it easy to defer the work indefinitely.

But DevOps has a measurable financial track record. The DevOps Research and Assessment (DORA) group has published annual State of DevOps reports that document the business impact of DevOps adoption. According to their research, organizations that adopted DevOps outperformed the S&P 500 over a three-year period, and high-performing IT teams showed significantly higher market capitalization growth compared to low-performing IT organizations over the same timeframe.

These are not soft, qualitative benefits. They are quantifiable outcomes that should matter to any executive team or board. If your organization wants to outperform the market, DevOps is one of the most reliable paths to get there — short of being Warren Buffett.

Identifying and Addressing Internal Resistance

Even when leadership is convinced, DevOps adoption often stalls because of resistance from within. Some individuals in your organization will resist the DevOps model — not because they’ve assessed it and found it lacking, but because change feels threatening to their current role, status, or comfort zone.

These individuals are prioritizing their personal interests over the organization’s long-term competitiveness. Left unaddressed, that resistance can be the difference between an organization that thrives and one that stagnates — or worse, goes the way of companies that failed to adapt to changing market conditions.

The instinct to simply remove these people and replace them is understandable but counterproductive. In a tight labor market, that approach is expensive, slow, and wasteful. More importantly, those individuals often carry years of institutional knowledge — about systems, processes, customers, and history — that is genuinely hard to replace.

Invest in Your People, Not Just Your Tools

The more effective path is investment in retraining and upskilling. DevOps skills are among the most in-demand in the technology labor market. By offering targeted training to your operations teams — including database administrators, network engineers, and security professionals — you accomplish several things at once:

• You retain experienced staff who already understand your systems and business context.
• You signal to your team that the organization invests in people, which improves morale and retention.
• You build internal DevOps capability that is grounded in real-world knowledge of your environment, not just theoretical best practices.
• You improve software delivery outcomes over time as those retrained individuals apply DevOps principles to daily work.

None of this happens automatically. It requires visible commitment from senior leadership, dedicated budget, and the organizational patience to see the transformation through.

Culture Change Takes Time — Start Anyway

One of the most honest acknowledgments about DevOps is that cultural transformation does not happen overnight. Many organizations have spent decades optimizing for failure avoidance — treating any outage, bug, or incident as something to be punished rather than learned from. Shifting to a culture that embraces failure as a feedback mechanism and a learning opportunity is a significant psychological and organizational challenge.

But the difficulty of change is not a reason to avoid it. It is a reason to start earlier. Every month spent waiting is a month your competitors are using to ship faster, learn faster, and pull further ahead. The organizations that begin the journey today — even imperfectly — will be meaningfully ahead of those that wait for conditions to be ideal before starting.

DevOps is not a juice cleanse. It is not a January gym membership. It is an operational model with demonstrated results, and the organizations treating it as optional are making a strategic bet that their competitors are equally slow. That bet is getting riskier every year.

Key Takeaways

• Competitive pressure is real: You don’t need to match Google or Netflix — you need to outpace others in your specific market.
• The financial case exists: DORA research links DevOps adoption to measurable improvements in stock performance and market capitalization growth.
• Resistance is human, not inevitable: Identify internal doubters and address their concerns through training and involvement, not replacement.
• Invest in people: Retraining existing staff preserves institutional knowledge while building modern capability.
• Culture change is slow — start now: The longer you wait, the harder it becomes to catch up with competitors who started earlier.

With Kubernetes now established in many organisations as the container orchestration platform of the future, are cracks already starting to show? Well, not exactly – but if I could pull out one common theme from what’s hot in the world of cloud-native, it’s the dawning light of Day Two.

That’s a shorthand term used for when this stuff goes live – the Ops end of DevOps, if you like. Going live was only to be expected, yet as so often with these things, it seems to have caught some early-adopters on the hop. The risks? Either you deploy containers into production without all the underpinnings needed to keep them fit and safe out in the wild, and they get exploited, or your high-profile project is embarrassingly terminated before launch due to its security failings.

Building for Day Two
However, Kubernetes – and cloud-native more broadly – is one of the fastest-growing areas of the whole IT industry. The result is a frenzy of activity as open-source, freemium and commercial projects jump in to build that necessary Day Two infrastructure. Here’s some of the key areas of development to watch, and a few of the vendors and projects we’ve looked at recently:

Security: The ‘shift left’ that builds container security into the dev pipeline is essential – restricting container privileges and API access to the minimum needed, for example, using only trusted open-source components, keeping containers simple, and so on – but there’s more. So among other things, you should explore vulnerability and misconfiguration scanners (e.g. Aqua Security), container hardening and compliance (e.g. Twistlock, now being acquired by Palo Alto Networks), and tools to help deliver a secure Kubernetes service (e.g. Rancher Labs).

Storage: Containers are inherently stateless, and it’s safer if you make them immutable too. That means some careful thinking is needed when you containerise a stateful application and give it persistent storage to consume (we’ve a paper on this topic here). Once you’ve abstracted the physical storage as software-defined storage (SDS) using the likes of Ceph or Robin, which can of course themselves be deployed as stateful apps on Kubernetes, you then need to orchestrate and automate it, which is where the popular Rook project comes in.

Data management: As mentioned, the container may be stateless but if the running application is stateful then you still have data to manage, protect and migrate. Most traditional data management tools aren’t well suited to protecting containers, although their suppliers are working to catch up. In the meantime, new ideas and tools are emerging, such as Kasten for stateful app backup and migration, and the highly-resilient distributed database CockroachDB.

Governance and cost management: As the use of Kubernetes grows, so does the risk of container sprawl. So we see projects such as Replex.io, aimed at keeping an inventory, rightsizing containers and managing costs, and Razee from IBM, which can enforce profiles and rules on containers, clusters and clouds – you could call it continuous delivery and auto-updating for Kubernetes clusters.

Runtimes and meshes
There’s lots more going on though, including new container runtime engines such as CRI-O and Containerd. Their aim, as Red Hat’s Urvashi Mohnani, speaking at Kubecon Barcelona, said of CRI-O, is “to make running containers in production secure & boring.”

And then there’s service mesh architecture, where the emerging duopoly between Istio and Linkerd has recently been disrupted by the arrival of the Service Mesh Interface, an interoperability project led by Microsoft but also including service mesh developers such as Buoyant, Solo.io and VMware.

What do you think – are your Kubernetes projects ready for Day Two? What’s missing, if anything, or what more might be needed? Tell us in the comments below…

A security champion is a developer, tester, or engineer embedded within a product team who takes on a formal secondary responsibility for security awareness, guidance, and advocacy. They are not a replacement for dedicated security engineers or penetration testers, but they act as a distributed security presence across every team in the organisation.

Why DevOps Teams Need Security Champions

In traditional software delivery models, security was a gate at the end of the development lifecycle. A separate security team would review the finished application, find issues, and send it back for remediation. This model is incompatible with the short sprint cycles and continuous deployment pipelines that define modern DevOps practice.

Security champions solve this mismatch by pushing security knowledge and decision-making into the teams that write and ship code. Rather than relying on a central security team to catch every issue after the fact, champions ensure that security considerations are part of daily development conversations — during design, during code review, and during testing.

This approach also relieves pressure on team leads. Instead of a single person needing to track security posture across an entire project, champions distribute that responsibility across the team structure, each acting as an informed first line of defence.

Step 1: Define Your Teams and Map Responsibilities

Before assigning anyone to a champion role, you need a clear picture of how your development organisation is structured. This means identifying:

• How many discrete product or feature teams exist
• How many engineers are in each team
• Which programming languages, frameworks, and platforms each team works with
• The current maturity level of security practices within each team
• Who the technical leads, product owners, and decision-makers are

Document this mapping. The goal is to distribute security responsibility proportionally across teams, and you cannot do that without first knowing how those teams are organised. Speak with technical managers and product owners early in the process to get accurate answers. Teams using different languages or deployment targets will face different threat landscapes and will need champions with relevant knowledge.

Step 2: Define the Security Champion Role Clearly

Ambiguity kills champion programmes. If the people taking on this role are not certain what they are responsible for, they will default to their primary engineering work and the champion function will quietly fade. Every security champion should have a written set of objectives covering:

• Security review cadence: Champions should conduct regular security reviews aligned with sprint milestones or release gates, not just at project completion.
• Threat modelling participation: Every significant new feature should be evaluated from a threat perspective. Champions facilitate this conversation within the team.
• Snag list ownership: Security issues discovered during development, code review, or automated scanning should be logged and tracked. Champions are responsible for ensuring these items are not dropped.
• Framework alignment: The security framework or standard the organisation follows — whether that is OWASP, NIST, an internal standard, or something else — should have been selected before development begins. Champions ensure their team’s work stays consistent with that framework.
• Automated scanning integration: Champions should advocate for and help integrate static analysis (SAST), dependency scanning, and where appropriate, dynamic testing (DAST) into the team’s CI/CD pipeline. Teams should not wait for an external penetration tester to find what automated tooling can surface continuously.

It is important to emphasise that security is everyone’s responsibility, not solely the champion’s. The champion role is about leadership and coordination, not about being the single person who catches all security issues. Developers on the team are expected to raise potential vulnerabilities when they encounter them, and to apply secure coding practices as a matter of course.

Step 3: Choose the Right People

Selecting the right individuals is one of the most consequential decisions in building a champion programme. The selection is typically driven by project managers and product owners, though input from the team itself often results in better acceptance and a smoother working relationship.

Good candidates for the security champion role typically share a few characteristics:

• Genuine curiosity about how software can be attacked or misused
• Strong communication skills — they will be the bridge between their team and the central security function
• Enough technical credibility within the team that their guidance will be taken seriously
• A collaborative rather than adversarial working style

Be realistic about motivation. Most engineers already have demanding workloads, and asking someone to take on additional security responsibilities without acknowledgement or incentive is unlikely to produce a committed champion. Make the value proposition clear. Security champion experience is a genuinely marketable credential. It provides direct exposure to product owners and senior leadership, opens doors to security engineering career paths, and typically includes access to training and conferences that would otherwise be outside a developer’s normal scope. Frame the role as a career opportunity, not an administrative burden.

Once champions are in place, define a minimum amount of time per sprint or per week that should be allocated to security-related work. Without this, other priorities will always crowd it out.

Step 4: Keep Security Information Flowing

A security champion programme only works if information moves freely — between champions and their teams, between champions and the central security function, and between champion peers across different teams.

Practically, this means establishing:

• A dedicated communication channel for champions. This could be a Slack channel, a Teams channel, a mailing list, or a recurring call — whatever fits your organisation’s working style. The point is that champions should be able to share what they are seeing and ask questions without routing everything through formal processes.
• A shared knowledge base where security guidelines, past findings, remediation patterns, and framework documentation are stored and accessible to all teams. Centralising this information reduces duplicated effort and prevents teams from solving the same security problems in isolation.
• Regular champion syncs with the central security team or a security lead. These meetings keep champions current on emerging threats, changes to tooling, and organisational security priorities.

The communication tooling itself matters less than the discipline to use it consistently. Champions who are well-connected and well-informed are far more effective than those who receive sporadic guidance and are expected to operate independently.

What a Mature Security Champion Programme Looks Like

When the security champion model is working well, a few things become observable across the organisation:

• Security issues are raised and discussed during sprint planning and design reviews, not discovered weeks later during a penetration test.
• Automated security tooling is embedded in CI/CD pipelines and treated as a first-class part of the build process.
• Threat modelling happens early, and findings are documented and tracked.
• Team leads spend less time on security audit and remediation because issues are being caught and addressed within teams in real time.
• Champions begin proposing their own security initiatives — new tooling, process improvements, training sessions — rather than waiting to be directed.

This last point matters. A programme that reaches this stage has genuinely changed the security culture of the organisation. Champions become advocates and initiators, not just compliance enforcers. That shift — from reactive to proactive security culture — is the real end goal of the security champion model in a DevOps environment.

Summary

Security champions are a practical mechanism for scaling security expertise across engineering teams without requiring every team to have a dedicated security engineer. By mapping your team structure first, defining the role with clear objectives, selecting motivated individuals, and maintaining strong information flow, you can build a programme that makes security a genuine part of your DevOps culture rather than an afterthought bolted on at the end of the pipeline.

The answer is not to slow developers down, nor to bolt security on at the end of the pipeline. It is to integrate security into every stage of the software development lifecycle—an approach known as DevSecOps. Getting there requires deliberate cultural and organisational change. Three practical strategies can help.

Why the Developer–Security Divide Persists

Different cultures, priorities, and toolchains keep development and security teams apart. Developers are typically rewarded for shipping features quickly; security teams are rewarded for preventing incidents. These incentives are not naturally aligned, and without deliberate effort to reconcile them, the gap widens over time.

Closing that gap requires action on three fronts: trust, accountability, and cooperation.

1. Develop Trust

Trust is the foundation on which any DevSecOps programme must be built. Security professionals who approach development teams as a compliance police force will meet resistance. A more effective starting point is empathy: understanding what motivates developers and finding ways to align security with those motivations.

Most developers are energised by learning new technologies and solving technically interesting problems. Security is rarely perceived as cutting-edge, and that perception is a barrier worth addressing directly. One effective approach is a Security Champions programme: identify a small cohort of respected senior engineers who are willing to engage with security topics, involve them in co-developing security best practices, and then recognise and reward them publicly when those practices are followed.

The mechanics of this approach typically follow three steps:

1. Identify motivated senior architects or engineers with credibility among the wider development team.
2. Engage that group to co-author security guidelines, so the guidelines reflect real-world development constraints.
3. Leverage those respected peers to promote and model secure practices across the organisation.

Shifting the security team’s posture from enforcement to enablement is what earns the development team’s trust and respect over time.

2. Create Accountability

In traditional software delivery, security responsibility was siloed: the security team owned security, full stop. In a DevSecOps model, developers share that ownership—just as they have come to own code quality, test coverage, and deployment reliability.

Building that sense of ownership rarely happens through policy alone. Practical demonstrations are more effective. Consider running joint sessions where both development and security engineers examine simple, real-world exploits against internal or staging applications—for example, demonstrating how an unvalidated input field could be abused, or how a misconfigured access control rule could expose data. These sessions do not need to be adversarial; framed as shared learning, they open both teams’ eyes to the real-world consequences of security oversights.

Key outcomes of this approach include:

• Developers gain concrete understanding of why security controls exist, not just what they are.
• Security engineers gain insight into the constraints and trade-offs developers face.
• Both teams develop a shared vocabulary and a shared stake in the outcome.

3. Promote Cooperation Through Embedding

Application security teams are typically small relative to the number of development projects they must support. The common model—assigning a security reviewer to audit work at specific checkpoints—creates a bottleneck and breeds resentment on both sides. Security engineers feel ignored; developers feel ambushed by late-stage feedback they cannot easily act on.

An alternative is to embed security engineers directly into development teams, at least for the organisation’s highest-priority projects. Rather than acting as external auditors, embedded security engineers participate in sprint planning, architecture reviews, and pull-request discussions. The goal is to build a replicable DevSecOps blueprint within a controlled set of projects that can later be scaled across the portfolio.

When security professionals are embedded this way, they are able to:

• Participate in daily development processes and ceremonies rather than reviewing output after the fact.
• Raise security concerns at the point in the development cycle when they are cheapest to fix.
• Build genuine understanding of the time, technology, and business constraints the development team operates under.
• Communicate security requirements with greater credibility because they have earned it through collaboration.

For the model to work at scale, the practices established in embedded pilots must be documented clearly enough that other teams can adopt them without requiring a dedicated embedded security resource on every project.

Putting It All Together

None of these three strategies delivers results overnight. Trust takes time to build. Accountability requires consistent reinforcement. Cooperation depends on structural changes that may be politically difficult in larger organisations. Positive recognition of progress—from leadership as well as peers—matters throughout the journey.

What organisations gain from the effort is substantial: a development pipeline where security is a first-class concern rather than a last-minute check, and a culture where developers and security engineers see themselves as partners working toward the same goal—software that ships fast and ships securely.

Building a DevSecOps culture is ultimately about changing how people work together, not just which tools they use. Get the people side right, and the tooling decisions become significantly easier.

The Hidden Cost of High-Velocity Releases

Every time your team ships a change, your customers bear a cost. Production software doesn’t exist in a vacuum. Enterprises that depend on your application typically have their own change-management processes: internal regression testing, compliance reviews, staged rollouts, and sign-off procedures. A change that takes your team an afternoon to ship may require days of validation effort on the customer’s side.

If your team releases updates monthly or even weekly, that overhead compounds quickly. Customers may struggle to keep up, skip validations to save time, or grow frustrated with the constant churn. None of those outcomes serve the business goals DevOps is supposed to advance.

The key question every DevOps team should ask before shipping is not can we release this? but should we release this now, and are our customers ready for it?

Orient Your DevOps Mindset Around the User

High-velocity CI/CD pipelines are engineered to reduce friction for the development and operations teams. That’s genuinely valuable. But those pipelines accelerate the release process, not the adoption process. Automation helps your team get code out the door faster; it doesn’t automatically help your customers absorb, test, and deploy that code in their own environments.

Change for the sake of change imposes a real tax on customers. A stream of small, low-impact updates may keep developers busy and dashboards green, but if those updates don’t meaningfully improve the user experience, the net effect is churn without value. Frequent, minor changes accumulate into upgrade fatigue.

This is not an argument for returning to monolithic, infrequent releases. Waterfall-style delivery cycles have well-documented drawbacks: large batches increase risk, delays reduce responsiveness, and big-bang deployments are harder to roll back. The goal is deliberate cadence, not slowness.

Define and Publish a Predictable Release Cadence

One of the clearest signals that a DevOps organization has lost its customer focus is the absence of a defined release schedule. Shipping whenever a feature is ready — or whenever the pipeline goes green — makes it impossible for customers to plan. If your release cadence is unpredictable or undocumented, that is a serious process gap.

A well-managed release cadence should be:

• Predictable: Customers should know in advance when to expect updates so they can schedule their own validation work.
• Documented: Release notes, changelogs, and migration guides give customers the information they need to assess impact.
• Risk-categorized: Distinguish between patch-level bug fixes, minor feature additions, and breaking changes. Not every update warrants the same customer response.
• Communicated early: Advance notice — especially for changes that affect APIs, data schemas, or integrations — lets customers prepare rather than react.

Communicate Software Changes Clearly

Production systems fail in unexpected ways. The assumption that a minor change is safe has preceded many significant outages in the software industry. A small configuration tweak, a dependency version bump, or a subtle behavior change in a shared library can cascade into a major incident in a complex production environment. This doesn’t mean teams should be paralyzed by caution, but it does mean every change deserves deliberate communication.

Clear communication requires effort and time. It can’t happen effectively when changes are arriving faster than your team can document and explain them. If your release velocity prevents you from writing meaningful release notes or notifying customers of upcoming changes, that’s a signal to slow down and recalibrate.

Practical communication practices for DevOps teams include:

• Maintaining a public or customer-facing changelog with each release.
• Sending advance notifications for changes that affect integrations or workflows.
• Providing a deprecation timeline for features or APIs being retired.
• Offering a way for customers to opt into early releases or preview environments.
• Keeping a known-issues register that is updated alongside each release.

Finding the Right Balance

DevOps is ultimately about delivering value to users reliably and sustainably. Speed is an enabler of that goal, not the goal itself. A team that releases continuously but leaves customers overwhelmed, under-informed, or perpetually fire-fighting has optimized the wrong metric.

The right balance depends on your product, your customers, and the nature of the changes you’re shipping. Some SaaS products can sustain daily deployments because they control the end-user environment entirely. Others — particularly those integrated deeply into enterprise workflows — may need a more measured cadence with stronger communication support.

The discipline is in asking the question honestly: are we releasing at this pace because it genuinely serves our users, or because it’s what our pipeline makes easy? A mature DevOps mindset holds both technical efficiency and customer impact in view at the same time.

Slow down when the situation calls for it. Communicate proactively. Measure success by outcomes for users, not just throughput from the pipeline. That’s what a customer-centered DevOps practice looks like.

Moving applications and operations to the cloud can be scary, even when you know the payoff can be big. Indeed, cloud technology can empower your organization and provide the on-demand resources your developers need.

But when you start to build the cloud for DevOps, you will face at least two challenges. First, the staff will likely have a steep learning curve. Also, the staff must be kept happy as it learns to perform DevOps correctly, and avoids unforced errors and embarrassing security missteps.

The way to marry DevOps and the cloud is to use a cloud toolchain. Instead of choosing tools based on personal preference, consider a simple rubric to evaluate how potential tools can fit into your DevOps process. This not only provides for the best possible outcome but also keeps any bias out of play

System Independence vs. Collaboration

Event-driven architectures encourage process independence by design. Each process acts on a message within the boundaries of its own area of concern, without needing to know what happens upstream or downstream. Many architects translate this into process isolation — the idea that developers, testers, and support engineers only need to understand the internals of their own component. While that purist approach has merit during early development phases, it breaks down once all the parts of a live system begin interacting.

Independence does not eliminate the need for cooperation. In fact, the further along the software development lifecycle (SDLC) a team progresses — toward integration, staging, and production — the more cross-team cooperation matters. This is especially true in a DevOps 2.0 context, where development, operations, security, and release engineering are expected to work as a single continuous feedback loop.

Consider a simple supply chain example: purchasing a box of paper clips. A single purchase event triggers messages to management, purchasing, finance, fulfillment, receiving, and internal distribution — potentially across different systems and geographic locations. Each system can be built independently, but they must interoperate correctly to get goods into the hands of the people who need them. When they don’t interoperate, things go wrong in ways that are difficult to trace.

Set Cross-Functional Messaging Standards

One of the most common failure modes in inter-process messaging is inconsistent message formats. When a system emits similar information in multiple formats — XML in one service, JSON in another, CSV from a legacy component — every downstream consumer must support multiple rule sets to parse and act on those messages. This multiplies development effort, increases test surface area, and raises the risk of a process receiving a message it simply cannot parse.

The practical remedy is to establish a cross-functional message specification before significant development begins. This does not need to be an elaborate governance document. Something as lightweight as a team-wide agreement to use JSON with a defined set of required fields in a shared schema is enough to dramatically reduce integration friction. The goal is a format that is predictable and easy to consume by any team or service in the ecosystem.

Key principles for a useful message standard in an EDA context:

• Choose one canonical format (e.g., JSON or Avro) and enforce it across all services.
• Define required fields in a shared schema registry so consumers have a reliable contract.
• Version the schema to allow incremental evolution without breaking existing consumers.
• Automate validation so non-conforming messages are rejected at the broker or gateway before they cause silent downstream failures.

When a common message format is in place, onboarding new services, testing integrations, and diagnosing failures all become substantially easier.

Take a Cooperative Approach to Performance Testing

Performance testing a sequential, synchronous system is relatively straightforward: start the transaction, measure elapsed time, stop, analyze. An asynchronous event-driven system does not work that way. Events fire at unpredictable times, components process them independently, and a single business transaction may span many services that have no direct knowledge of each other.

Consider ordering a pizza. If you only measure total elapsed time from order placement to delivery, you cannot identify where a slowdown occurred. The credit card processor might be the bottleneck, but that component is internal to the pizzeria — you have no direct visibility into it. The only way to surface that information is through cooperation: asking the pizzeria to emit diagnostic messages (keyed by order number) to a shared test message broker. With a shared key and a shared channel, both parties can correlate events and pinpoint the bottleneck without exposing sensitive internal data beyond the agreed boundary.

This analogy translates directly to distributed systems. Most payment processors, for example, already provide a test or sandbox mode that developers use to simulate live transaction flows. The same concept — test mode instrumentation — can be applied to any component in an EDA system. The pattern works as follows:

1. Each component publishes predefined performance metrics to a dedicated test message broker or structured log sink on demand.
2. Metrics are agreed upon cross-functionally: CPU utilization, memory consumption, disk I/O, error rates, message processing latency, and queue depth are common candidates.
3. Any team — development, QA, SRE — can subscribe to that broker and correlate metrics across components using a shared transaction or correlation ID.
4. Findings are analyzed after the test window rather than requiring all teams to be present simultaneously.

The key cultural shift here is that performance testing in an EDA system is not a single team’s responsibility. It requires agreement on what to measure, how to publish measurements, and how to correlate them — before a single load test is run. Teams that treat performance testing as cooperative and pre-planned will find it far easier to isolate regressions when they appear in production.

Documentation Is a Map, Not an Afterthought

In a distributed, asynchronous system where no single person can hold the entire topology in their head, good documentation is not optional — it is a critical operational dependency. For DevOps 2.0, where responsibility for reliability is shared across development, operations, and release teams, everyone needs access to an accurate, current reference for how the system behaves.

The most effective documentation strategies for EDA systems share several characteristics:

• Component ownership: Each team responsible for a service is also responsible for documenting it. Domain experts write the code; they should also write the docs.
• Common format: Just as message schemas should be standardized, documentation structure should follow a shared template so that any engineer can navigate any component’s docs without orientation time.
• Living documents: Documentation is version-controlled alongside code and updated as part of the definition of done for any change that affects message contracts or system behavior.
• Message exchange focus: The most critical documentation in an EDA system describes what events a component produces, what events it consumes, and under what conditions — not just internal implementation details.

A master outline, collaboratively maintained across all teams, ensures that any engineer can quickly locate the reference they need, understand the scope of a change, and assess the downstream impact before merging.

Putting It All Together

A distributed event-driven system is only as reliable as its weakest message handler. A single malformed message, an undocumented schema change, or an untested performance edge case in one component can propagate failures across many others — often in ways that are difficult to trace after the fact.

The four practices outlined above — embracing cooperation alongside independence, standardizing message formats, instrumenting components for cooperative performance testing, and maintaining living documentation — are mutually reinforcing. None of them works in isolation, and that is exactly the point: DevOps 2.0 in an event-driven architecture is not about perfecting individual components in isolation. It is about designing the entire system — its people, processes, and tooling — to collaborate effectively even when the messages themselves flow unpredictably.

Teams that internalize this shift will find that their EDA systems are easier to test, faster to debug, and more resilient at scale — which is the promise that DevOps 2.0 is built on.

The Core Argument: PaaS as a Bridge, Not a Barrier

Red Hat has staked a strategic position on the idea that platform-as-a-service (PaaS) environments will become the primary mechanism through which IT operations teams expose a curated catalog of distributed services to developers—without losing governance or control.

Paul Cormier, then President of Products and Technologies at Red Hat, articulated the thesis: when developers lack access to sanctioned services, they route around IT operations entirely—downloading libraries and dependencies on their own. The result is a sprawling application estate of uncertain provenance, duplicated functionality, and hidden operational risk. A mature PaaS changes that dynamic by making approved services available through well-defined APIs, giving developers speed while giving operations teams visibility.

Why CIOs Are Paying Attention

According to Cormier, Red Hat was increasingly engaging CIOs and senior IT leaders—not just architects or platform engineers—in conversations about OpenShift. That shift in audience is significant. It signals that PaaS adoption is moving from a tactical, team-level decision to a strategic initiative aimed at resolving the historic tension between development velocity and operational stability.

The conversation is framed around a familiar DevOps pain point: developers throwing code “over the wall” to operations, followed by fire-fighting, blame, and slow delivery cycles. PaaS, in this framing, replaces the wall with an API.

OpenShift, Kubernetes, and the Blurring of PaaS vs. CaaS

One of the more consequential technical points Cormier made is that OpenShift’s adoption of Kubernetes as its orchestration layer effectively erases the traditional boundary between PaaS and container-as-a-service (CaaS). In practice, this means:

• Traditional 12-factor applications can run alongside newer, microservices-based workloads on the same platform.
• Deployment targets can span on-premises infrastructure and public cloud environments, with the choice driven by cost, data gravity, or regulatory requirements rather than platform limitations.
• Serverless computing frameworks can sit on top of this substrate, enabling event-driven workloads and cloud-bursting scenarios in heterogeneous hybrid cloud environments.

This convergence matters for teams evaluating platform strategy: rather than maintaining separate toolchains for PaaS and container workloads, a Kubernetes-native PaaS like OpenShift aims to consolidate them.

Mixing Internal and External Services in a Hybrid World

Cormier also pointed to a pattern already emerging in larger enterprises: selectively mixing internal platform capabilities with external cloud services based on performance characteristics. His example was AI/ML workloads—an organization might route a specific inference workload to a particular cloud provider because that provider’s AI service outperforms alternatives for that use case, while keeping other workloads on-premises.

This “best tool for the workload” posture requires a platform layer capable of brokering access to both internal and external services consistently—exactly the role Red Hat was positioning OpenShift to play.

The Goal Is Developer Focus, Not Operations Elimination

A common misreading of DevOps is that it aims to replace IT operations with developer self-service. Cormier pushed back on that interpretation. The goal, he argued, is to maximize the time developers spend building and testing applications—not to eliminate operations teams. PaaS enables this by abstracting infrastructure complexity, so developers are not blocked waiting for environments, and operations teams are not overwhelmed fielding ad-hoc infrastructure requests.

This distinction is important for organizations evaluating whether to invest in a PaaS: the platform does not make operations teams redundant; it reshapes their role from reactive ticket-handlers to curators of platform capabilities.

The Adoption Gap: Why PaaS Uptake Remained Uneven

Despite the strategic argument, Cormier acknowledged that most IT organizations in 2018 had only pockets of PaaS adoption. The majority of applications were still being built and deployed without any PaaS involvement. The most frequently cited reason: early PaaS platforms were perceived as too prescriptive—they imposed opinions on application structure, deployment workflow, and runtime choices that many teams were unwilling to accept.

Red Hat’s bet was that the rise of container-based, microservices architectures would change the calculation. Containers already impose a degree of standardization on how software is packaged and run. Given that foundation, organizations may find it easier to accept the additional conventions of a PaaS in exchange for significantly reduced infrastructure complexity.

Key Takeaways for DevOps Teams

• PaaS as governance layer: A mature PaaS can enforce approved service consumption without becoming a bottleneck, replacing informal workarounds with sanctioned APIs.
• Kubernetes convergence: The PaaS/CaaS distinction is increasingly academic on Kubernetes-native platforms. Evaluate platforms on workload breadth, not category label.
• Hybrid cloud brokering: Platform strategy should account for selectively routing workloads to internal or external services based on performance, cost, and compliance—not just lift-and-shift.
• Serverless integration: Event-driven serverless frameworks can extend a PaaS to cover burst and integration workloads without requiring separate infrastructure.
• Operations reshaping, not elimination: PaaS adoption changes the operations team’s mandate from infrastructure provisioning to platform product ownership.

The 2018 AI Land Grab for Developers

Developer conference season in May 2018 made one trend impossible to ignore: every major technology platform was racing to put artificial intelligence tools directly into developers’ hands. Facebook’s F8, Microsoft Build, Google I/O, and the AWS London Summit all centred their messaging on democratising machine learning — lowering the barrier to entry so that developers without deep data-science expertise could still build AI-powered applications.

The business logic is straightforward. By wrapping complex capabilities — computer vision, natural language understanding, deep learning model training — into consumable APIs and managed services, cloud vendors create stickiness. Developers who build on a platform’s AI services consume that platform’s compute and storage, deepening the relationship over time.

Below is a breakdown of what each company announced and the strategic position each was staking out.

AWS: Machine Learning for Every Developer

Speaking at the AWS London Summit, Amazon CTO Werner Vogels summarised the company’s ambition plainly: “Our mission is to make machine learning available and put it into the hands of every developer.”

AWS had already assembled a broad AI portfolio spanning three layers:

• Infrastructure layer: Raw GPU and CPU compute for training custom models at scale.
• Platform layer: Amazon SageMaker, announced at re:Invent 2017, which abstracts away infrastructure provisioning, model tuning, and deployment so teams can focus on the model itself rather than the operational plumbing.
• Service layer: Pre-built, API-driven capabilities including Rekognition (image and video analysis), Polly (text-to-speech), and Lex (conversational interfaces and chatbots — the same technology underlying Alexa).

SageMaker was the centrepiece of AWS’s developer story at the time. Rather than requiring data engineers to manage training clusters manually, SageMaker provided a managed Jupyter notebook environment, built-in algorithm support, and one-click model deployment to scalable endpoints. This significantly reduced the time from experimentation to production.

AWS’s strategic position was to offer the widest surface area of AI services while keeping the underlying infrastructure invisible. The trade-off is that deeper AWS integration makes it harder to move workloads elsewhere — a point that rivals were quick to criticise.

Google: AI as Core Mission, Not Just a Feature

Google entered 2018 with arguably the strongest AI research credentials of any public company, having open-sourced the TensorFlow framework and recently rebranded its research division as Google AI. At Google I/O in Mountain View, CEO Sundar Pichai positioned AI not as a product category but as the foundation of Google’s entire mission: “To make information more useful, accessible and beneficial to society.”

Two demonstrations attracted widespread attention:

• Smart Compose in Gmail: An AI feature capable of completing full sentences — and eventually entire email drafts — based on context.
• Google Duplex: A capability allowing Google Assistant to conduct natural-sounding phone conversations on a user’s behalf, such as making a restaurant reservation. The demo was both impressive and immediately controversial, raising questions about disclosure and consent.

On the infrastructure side, Google announced its Cloud TPU (Tensor Processing Unit) offering, making the custom silicon it uses internally to accelerate TensorFlow workloads available to external developers via Google Cloud. TPUs offer significantly higher throughput for matrix operations common in deep learning compared to general-purpose GPUs, though the economics depend heavily on workload type.

Google’s differentiation was depth of research pedigree and control of the full stack from custom silicon through to developer-facing APIs — a position no other vendor could fully replicate in 2018.

Microsoft: Intelligent Cloud, Intelligent Edge

Microsoft CEO Satya Nadella arrived at Build 2018 with a clear strategic framing: intelligent cloud, intelligent edge. His goal was to “commoditise AI” — making it available not just in the cloud but at the network edge, embedded in devices and IoT hardware.

Nadella articulated the developer promise directly: “What matters is can we translate these breakthroughs into frameworks, tools and services and put them into your hands as developers, so you can take AI and make an impact on every industry and application.”

Microsoft’s AI stack mirrored AWS in structure but differed in emphasis:

• Azure Machine Learning Studio: A visual, drag-and-drop modelling environment for building and testing ML pipelines.
• Cognitive Services: A broad catalogue of pre-trained APIs covering computer vision, speech recognition, language understanding, and decision-making — directly comparable to AWS’s service-layer offerings.
• Project Brainwave: A real-time AI inference system using FPGAs (field-programmable gate arrays) to run models with lower latency than CPU or GPU alternatives. Microsoft claimed this made Azure the fastest cloud for real-time AI inference at the time of the announcement.

The edge computing angle was a genuine differentiator. Microsoft announced partnerships with Qualcomm to produce a Vision AI Developer Kit — combining hardware and software for camera-based IoT applications — and with drone manufacturer DJI to bring complex compute directly to the device level.

Nadella also explicitly rejected lock-in as a strategy, calling for openness across frameworks and infrastructure. This was a pointed contrast to the AWS model and reflected Microsoft’s increasing investment in open-source tooling under his tenure.

Facebook: Open-Source AI from a Non-Cloud Perspective

Facebook occupies a different position in this landscape. It is not a cloud infrastructure provider, so it cannot monetise AI services the same way AWS, Google, or Microsoft can. Its influence on the developer AI ecosystem instead flows through open-source contributions and its large developer community.

At F8 2018, Facebook described AI as a “foundational technology” and highlighted its ongoing investment in research: “We’ve made deep investments in advancing the state of the art through scientist-directed research.”

The most significant developer announcement was the forthcoming release of PyTorch 1.0, the next major version of Facebook’s open-source deep learning framework. PyTorch had gained substantial traction in the research community for its dynamic computation graph, which makes debugging and experimentation more intuitive than static-graph frameworks. Version 1.0 was positioned to bridge the gap between research flexibility and production-grade deployment — a tension that had previously led many teams to use PyTorch for prototyping but switch to TensorFlow for production.

The Ethics Layer: Genuine Commitment or Conference Theatre?

Every keynote in the spring 2018 conference season included a section on AI ethics — not coincidentally, given that Facebook’s Cambridge Analytica data scandal was dominating headlines at the time. All three CEOs used strikingly similar language.

Mark Zuckerberg stated: “We need to take a broader view of our responsibility. It’s not enough to just hold powerful tools, we need to ensure they are used for good.”

Sundar Pichai acknowledged the tension inherent in Google’s position: “We can’t just be wide-eyed about the innovations technology creates. There are very real and important questions being raised about the impact of these advances. We know the path ahead needs to be navigated carefully and deliberately, and we feel a deep sense of responsibility to get this right.”

Satya Nadella went furthest in announcing structural commitments, stating that Microsoft had formed an internal ethics board to govern AI products and projects. He summarised the challenge as: “We need to ask ourselves not only what computers can do but what computers should do.” Microsoft also pledged $25 million to an “AI for Accessibility” initiative aimed at applying AI to challenges faced by people with disabilities.

Whether these ethics commitments translated into durable policy changes is a question that only subsequent years could answer. At the time, the consistency of the messaging across competing platforms was itself notable — suggesting that developers and enterprise customers were beginning to treat responsible AI practices as a procurement criterion, not merely a nice-to-have.

Key Takeaways for DevOps and Engineering Teams

Looking across all four platforms, several patterns emerge that remain relevant for teams evaluating AI infrastructure today:

• Managed services reduce operational burden but increase vendor dependency. SageMaker, Azure ML Studio, and Google Cloud AI Platform all abstract away infrastructure management — useful for velocity, but worth auditing for portability before committing at scale.
• Open-source frameworks (TensorFlow, PyTorch) are the lingua franca. All platforms support both, meaning framework choice does not determine platform choice. Teams can retain flexibility at the model layer even when using managed cloud services for training and inference.
• Edge inference is a distinct problem from cloud training. Microsoft’s early focus on edge hardware partnerships pointed toward a pattern that has become much more prominent since — the need to run lightweight models on constrained hardware without a round-trip to the cloud.
• Custom silicon matters at scale. Google’s TPUs and Microsoft’s Brainwave FPGAs signalled that general-purpose compute is not always the right choice for high-throughput inference workloads. Teams running AI at scale should evaluate specialised hardware options.
• Ethics governance is increasingly a first-class engineering concern. The formation of ethics review boards and accessibility commitments in 2018 was an early signal of what has since become a broader movement toward responsible AI frameworks inside engineering organisations.

Conclusion

The spring 2018 conference season captured a specific inflection point: the moment when AI shifted from a specialised research discipline to a mainstream developer tool. All four platforms converged on the same insight — that the winner of the AI era would not be determined solely by who had the best models, but by who made those models most accessible to the widest range of developers.

The competitive dynamics have continued to evolve since, with each platform adding significant capabilities. But the foundational bets each company made in 2018 — AWS on breadth of services, Google on research depth and custom silicon, Microsoft on the cloud-to-edge continuum, and Facebook on open-source frameworks — have remained defining characteristics of their respective AI strategies.

Overview: Agile Leads in Adoption, DevOps Leads Among High Performers

Agile remains the most popular software development methodology by a wide margin, but high-performing development teams are considerably more likely to embrace a DevOps culture. That is one of the headline findings from the GitLab 2018 Global Developer Report, which surveyed approximately 5,300 software professionals from around the world towards the end of 2017.

Respondents were geographically distributed, with the majority coming from Europe (46%) and North America (36%), and roughly 1.5% from Africa.

Methodology Adoption Rates

When asked which software development methodology their team primarily used, respondents reported the following breakdown:

• Agile – 60%
• DevOps – 23%
• Waterfall – 16%
• Conversational Development – 12%
• Rapid Application Development (RAD) – 9%
• Other – 9%

Notably, among those who selected “Other” to describe their development process, the most common adjective used was “chaos” — a candid signal that a significant portion of teams operate without a clearly defined methodology.

DevOps and Agile: Complementary, Not Competing

Rather than positioning DevOps and Agile as mutually exclusive, the GitLab report framed them as complementary. Agile focuses on iterative delivery and responsiveness to change within development teams, while DevOps extends that collaboration outward — bridging the gap between software development and IT operations. The goal of DevOps is to improve communication, collaboration, and integration between these two business functions, reducing friction across the delivery pipeline.

In practice, many organisations adopt Agile practices within their development teams while simultaneously pursuing a broader DevOps culture that spans development, QA, security, and operations.

High Performers vs Low Performers

The report segmented respondents into high-performers and low-performers based on deployment frequency and time allocation:

• High-performers: Deployed code on demand or multiple times per day, and estimated spending 50% or more of their time on new work.
• Low-performers: Deployed code between once per day and once per month, or as infrequently as once every six months.

The difference in DevOps adoption between these two groups was striking:

• 45% of high-performing teams reported having a strong DevOps culture.
• Only 21% of low-performing teams said the same.

High-performing teams also reported better visibility into their pipelines and stronger cross-functional collaboration — outcomes the report attributes, at least in part, to their DevOps practices.

Automation Is a Top Priority for DevOps Practitioners

Automation of the software development lifecycle emerged as a defining priority for high-performing teams. The data showed a clear gap between DevOps and Agile practitioners on this front:

• 71% of DevOps practitioners rated automating more of the software development lifecycle as a high priority.
• 60% of Agile practitioners said the same.

Organisations that had adopted DevOps were also more likely to deploy on demand — reinforcing the link between automation investment and deployment frequency.

Perception Gap: Managers vs Developers on DevOps ROI

Both developers and managers agreed on the value of improved collaboration and communication that DevOps enables. However, a notable perception gap emerged when it came to time savings:

• 81% of managers agreed that DevOps saves time in the development process.
• Only 65% of developers agreed with the same statement.

This gap may reflect the difference between strategic-level visibility (where managers see throughput gains across the organisation) and day-to-day developer experience (where tooling friction, cultural resistance, or incomplete automation may offset some of the benefits).

Remote Teams and DevOps Culture

The survey also surfaced an interesting finding about remote work and DevOps adoption:

• 41% of remote teams reported having a well-established DevOps culture.
• Only 34% of in-office teams said the same.

This may reflect the fact that distributed teams are more dependent on robust tooling, automated pipelines, and clear communication processes — the very foundations of a DevOps culture — in order to function effectively across time zones and locations.

DevOps as a Technology Investment Priority in 2018

Among the most important technology investments for 2018, the survey ranked DevOps third — behind continuous integration, deployment and delivery (CI/CD) and automation and containers. Agile, by contrast, did not rank as highly as a priority investment area, suggesting that while Agile is broadly adopted, organisations were directing new spending toward DevOps tooling and practices.

A Note on Survey Bias

It is worth acknowledging a significant caveat: the survey was conducted by GitLab, and respondents were drawn largely from the GitLab community. Given that GitLab is a platform built around DevOps principles and continuous delivery, participants are likely predisposed toward DevOps-friendly perspectives. The relatively strong showing for DevOps in the results — and the favourable attitudes toward DevOps tooling and automation — should be interpreted in that context.

Key Takeaways

• Agile is the most widely adopted software development methodology (60% of respondents), but DevOps is growing rapidly.
• High-performing teams are more than twice as likely to have a strong DevOps culture compared to low-performing teams (45% vs 21%).
• Automation of the software development lifecycle is a higher priority for DevOps practitioners than Agile practitioners.
• A perception gap exists between managers and developers on the time-saving benefits of DevOps.
• Remote teams appear more likely to have an established DevOps culture than in-office teams.
• The survey has an inherent GitLab community bias that should be factored into any interpretation of the results.

Slack has become one of the most widely adopted communication platforms for DevOps teams, and for good reason: it combines persistent chat, third-party integrations, file sharing, and audio/video calling in a single workspace. This article covers the real collaboration challenges DevOps teams face and how to configure Slack to address them.

Why Collaboration Is Still a Challenge in DevOps

Most organizations do not have a full DevOps team available around the clock. When a production incident occurs at 2 a.m., the burden of response often falls on whoever is on call — frequently just one or two engineers working without real-time support from the broader team.

This creates several compounding problems:

• Context is siloed. The engineer who knows the affected service best may be asleep. The person who is awake lacks critical context.
• Decisions slow down. Without easy access to teammates, responders hesitate or make decisions with incomplete information.
• Burnout accumulates. When the same small group repeatedly handles incidents alone, morale and retention suffer.

A well-configured communication platform does not eliminate these pressures, but it significantly lowers the friction of pulling the right people into a conversation at the right time.

Why Slack Works Well for DevOps Teams

Slack’s value for DevOps goes beyond simple chat. Its integration ecosystem connects it to virtually every tool in a modern software delivery pipeline — version control, CI/CD systems, monitoring, alerting, and log management platforms. This means your team can receive and act on signals from your infrastructure inside the same workspace where they already communicate.

Commonly used Slack integrations for DevOps include:

• GitHub / GitLab — receive notifications for pull requests, code reviews, and merge events.
• Jenkins / CircleCI / GitHub Actions — post build and deployment status directly into a channel.
• PagerDuty / OpsGenie — surface on-call alerts and allow acknowledgment from within Slack.
• Datadog / Prometheus / Grafana — send monitoring alerts and threshold breaches to dedicated channels.
• Jira / Linear — link tickets to conversations and update issue status without leaving Slack.

Custom integrations are also straightforward to build using Slack’s Bolt SDK or its incoming webhooks API, making it practical to connect internal tools that lack off-the-shelf Slack apps. For example, the team at Logz.io built a Kibana Snapshots integration that lets engineers share Kibana dashboard visualizations directly in a Slack channel, enabling the team to see and discuss log anomalies in real time without switching tools.

Practical Tips for Using Slack in a DevOps Workflow

1. Structure Your Channels Deliberately

Unstructured Slack workspaces become noisy fast. A thoughtful channel structure keeps signal separate from noise:

• #incidents or #ops-alerts — a dedicated channel for production alerts and active incident response. Pin runbooks and postmortem links here.
• #deploys — automated deployment notifications from your CI/CD pipeline, giving everyone visibility into what is shipping and when.
• #team-[service-name] — per-service or per-squad channels so conversations stay scoped and relevant.
• #monitoring — aggregated infrastructure and application metrics alerts.
• #general-devops — async discussion, questions, and knowledge sharing across the broader team.

Use channel naming conventions consistently so any team member can find the right channel without asking.

2. Use Huddles and Calls for Active Incidents

Text chat is efficient for async communication but becomes a bottleneck during high-pressure incidents. Slack Huddles (lightweight audio calls) or full video calls allow teams to coordinate verbally, which is faster when dozens of messages are flying in simultaneously.

A reasonable convention: escalate from chat to a huddle when the incident has been active for more than a few minutes and multiple engineers are involved. Reserve calls for genuine emergencies and be respectful of off-hours time — unnecessary calls outside working hours erode trust quickly.

3. Manage Notification Overload Proactively

One of the most common Slack anti-patterns is integrating every possible tool and letting every event post to a channel. The result is alert fatigue — engineers start ignoring channels because the noise-to-signal ratio is too high.

Practical mitigation strategies:

• Only post actionable alerts to Slack. Informational logs belong in your logging platform, not a chat channel.
• Set alert thresholds high enough that only genuine anomalies trigger a notification.
• Use Slack’s notification schedules feature so engineers can define quiet hours without fully disconnecting.
• Periodically audit active integrations and remove or silence those that no longer provide value.

4. Document Decisions in Threads

Slack’s threaded replies are underused in many teams. During an incident or a design discussion, use threads to keep a coherent record of the decision-making process. After the incident is resolved, copy key findings into your postmortem document or wiki. Treat Slack as a real-time communication layer, not a long-term knowledge store — important conclusions should always be captured somewhere durable.

5. Automate Incident Workflows with Slack Bots

Several incident management platforms (PagerDuty, Opsgenie, FireHydrant, and others) offer Slack bot integrations that let you acknowledge alerts, assign incident commanders, create dedicated incident channels automatically, and post status updates — all from within Slack. Automating these steps reduces the cognitive load on the engineer who first picks up an alert and ensures a consistent response process every time.

The Broader Value of Communication in DevOps

Slack is a tool, not a strategy. The underlying goal is a culture where developers and operations engineers communicate openly, share context early, and feel comfortable raising problems without blame. No chat platform can manufacture that culture on its own, but a well-organized, well-integrated Slack workspace removes a significant amount of friction that would otherwise slow teams down.

When collaboration works — when an alert fires and the right people are in a channel within minutes, with relevant dashboard snapshots and runbook links already surfaced — teams ship faster, recover faster, and spend less time firefighting. That is the DevOps promise, and thoughtful use of tools like Slack is a meaningful step toward delivering on it.

Original article sourced from TechTarget / DevOps Agenda.

Have you ever found yourself struggling to explain something that you know how to do without a moment’s thought? I’m not talking about the amygdala, the part of our brain that initiates the automatic “fight or flight” response. No, I’m referring more to the muscle memory-type behaviors that we’ve collected through life and, likely, pay little attention to while executing.

Things We Take for Granted

Muscle memory is awesome. Through the magic of muscle memory I can type fast (occasionally with okay-ish accuracy). It enables me to drive in peak-hour traffic without having a nervous breakdown, and it’s the only way I can ski down a tree-lined path without ending up in hospital.

It’s through training, practice and repetition that we can carry out an increasing list of mundane tasks without being distracted from the bigger picture. In contrast, consider the expanding influence of DevOps across organizations, and how we’re pushing developer principles and practices onto operational facilities that have never been exposed to such. We’re asking people to break a habit of a lifetime and to think like a developer practically overnight. This can create some significant speed bumps in your operation transformation toward achieving continuous deployment pipelines.

Type Mismatch Error

On one side we’ve got infrastructure operations, where we’ve seen two decades of little change in the way services are provisioned and managed. Heck, for the most part, spreadsheets are still the source of truth—spreadsheets without versioning!

There’s no “commit log” trail, no integrated testing and definitely no consistency across the vendor technologies that must be managed. Imagine what root cause analysis after an outage looks like? A team huddle for a round of “Who touched it last?”

To software engineers, it must seem like the Wild West but, it’s important to acknowledge that this problem is largely the fault of the technology vendors. Until the principles and practices of DevOps shone a very bright light on operational inefficiencies, infrastructure operations weren’t challenged to increase the speed of business. Hence, the technology vendors hadn’t been pressured to evolve.

A New Hope (Yes, Like ‘Star Wars’)

Most infrastructure vendors have responded to the DevOps calling by re-architecting products in support of programmability. Many of them have even done an alright job of it, adhering to RESTful standards and publishing API schemas. However, there’s one key element that’s missing, and will be the cause of further challenges if not addressed.

To an experienced developer, operating with declarative interfaces, idempotency and continuous integration is practically muscle memory. But to infrastructure operations, it is completely foreign. Progressing with the kind of operational transformation required to achieve continuous deployment is possible but only though closing this operating model gap.

“Give me a Swiss army knife, and MacGyver I do not become,” said I at DevOps Enterprise Summit 2017.

As the lines between development and operations increasingly blur, be super-aware of the development principles and practices that are just a part of life for the software engineers. To close the operating model gaps, run workshops for your pals in operations. Teach them all the fancy words. Show them how to manage their config files and architectural notes in a code repository, so they can familiarize themselves with the tooling.

DevOps and Machine Learning Lead Salary Rankings

Stack Overflow’s eighth Annual Developer Survey, completed by more than 100,000 developers worldwide, found that DevOps specialists and machine learning engineers sit at the top of the tech salary ladder. The survey noted that languages, frameworks, and disciplines associated with these two fields are growing rapidly — and that the professionals who work in them are rewarded accordingly.

“DevOps and machine learning are important trends in the software industry today. Languages and frameworks associated with these kinds of work are on the rise, and developers working in these areas command the highest salaries.”

Stack Overflow Annual Developer Survey (2018)

Salary Breakdown by Developer Role

The table below shows median salaries as reported in the Stack Overflow survey, converted to U.S. dollars. Global figures represent worldwide medians; U.S. figures represent U.S.-only respondents.

• Engineering manager — $89,000 globally / $137,000 U.S.
• DevOps specialist — $72,000 globally / $110,000 U.S.
• Data scientist or machine learning specialist — $60,000 globally / $102,000 U.S.
• Data or business analyst — $59,000 globally / $90,000 U.S.
• Embedded applications or devices developer — $59,000 globally / $98,000 U.S.
• Full-stack developer — $59,000 globally / $100,000 U.S.
• Desktop or enterprise applications developer — $57,000 globally / $100,000 U.S.
• Back-end developer — $56,000 globally / $102,000 U.S.
• System administrator — $56,000 globally / $93,000 U.S.
• QA or test developer — $55,000 globally / $83,000 U.S.
• Database administrator — $51,000 globally / $90,000 U.S.
• Front-end developer — $51,000 globally / $93,000 U.S.
• Designer — $46,000 globally / $85,000 U.S.
• Educator or academic researcher — $44,000 globally / $88,000 U.S.
• Mobile developer — $43,000 globally / $101,000 U.S.
• Game or graphics developer — $40,000 globally / $90,000 U.S.

The gap between global and U.S.-only figures is significant across all roles, reflecting both purchasing-power differences and the concentration of high-paying tech employers in the United States.

Experience Levels: DevOps Practitioners Are Seasoned Professionals

The survey found that DevOps specialists and desktop/enterprise application developers reported the most professional coding experience, averaging approximately eight years. This is notable given that DevOps as a formal discipline and job title is relatively recent.

“DevOps as a discipline and professional identity is relatively new, but the people working in this field are highly experienced.”

Stack Overflow Annual Developer Survey (2018)

At the other end of the spectrum, game/graphics developers and mobile developers reported the fewest years of professional experience, suggesting those communities attract more early-career entrants.

Most-Used Programming Languages and Frameworks

The survey also captured the languages and frameworks developers rely on most:

• JavaScript was the most commonly used programming language overall, used by approximately 70% of respondents.
• Python topped the list of fastest-growing languages for the sixth consecutive year and is widely used across data science, automation, and scripting workloads.
• Node.js (approximately 50% of respondents) and AngularJS (approximately 37%) were the most widely adopted frameworks.
• React (approximately 28%) and .NET Core (approximately 27%) were also significant.

For DevOps and ML practitioners specifically, Python’s growth is particularly relevant: it underpins major ML frameworks (such as TensorFlow and scikit-learn) and is commonly used in infrastructure automation and scripting tasks.

Developer Sentiment on Artificial Intelligence

When asked about the future of AI, developer sentiment skewed positive. Roughly three-quarters of respondents said they were more excited than worried about AI’s potential, while approximately one-quarter expressed more concern about its risks. This cautiously optimistic outlook aligns with the broader industry momentum around machine learning investment at the time of the survey.

Key Takeaways for Engineers and Hiring Managers

• DevOps and machine learning skills consistently command a salary premium over other developer specializations, both globally and in the U.S.
• DevOps practitioners tend to be experienced professionals, making the role competitive for those entering the field without a strong systems or software background.
• Python proficiency is increasingly a cross-cutting requirement — useful for ML pipelines, infrastructure-as-code, and automation scripts alike.
• Despite being a relatively young formal discipline, DevOps engineering attracts some of the most experienced people in the industry, suggesting that practitioners often transition from adjacent roles such as system administration, software engineering, or release engineering.

Source: Stack Overflow Annual Developer Survey 2018, as summarized by ZDNet and techworm.net.

DevOps can be a game-changer for independent software vendor (ISV) startups — accelerating delivery, tightening feedback loops, and breaking down the wall between development and operations. But the philosophy is easier to endorse than to execute. Many startups adopt DevOps in name only, stumbling into recurring traps that negate its benefits and, in the worst cases, damage the business. Below are five of the most common and consequential mistakes, along with practical guidance for avoiding them.

1. Trying to “Do DevOps” Instead of “Being DevOps”

DevOps is frequently marketed as a toolchain or a process checklist. In reality, it is a cultural philosophy. Gene Kim, co-author of The Phoenix Project, describes DevOps through what he calls The Three Ways:

• Systems thinking: Optimise the entire value stream, not isolated silos. A fix that speeds up development but creates a bottleneck in QA or operations is not a win.
• Amplify feedback loops: Create fast, continuous feedback from customers and from downstream stages of your pipeline so teams can respond and course-correct quickly.
• A culture of continual experimentation and learning: Empower team members to take calculated risks, own their work, and learn from both successes and failures without fear of blame.

If your team treats DevOps as a sequence of prescribed steps — “step one, step two, done” — you will get procedural compliance but not the cultural shift that makes DevOps valuable. Invest in educating the whole team on the philosophy, not just the tools.

2. Forgetting DevOps’ Collaborative Roots

The term “DevOps” is itself a contraction of Development and Operations — a reminder that the approach was born from breaking down the wall between two groups that historically worked in isolation. Collaboration is not a soft benefit; it is the core mechanism through which DevOps delivers value.

In fast-moving startups, it is tempting for individual contributors to go heads-down on their specific tasks and minimise communication overhead. The problem is that independent work, however well-intentioned, frequently produces components that do not integrate cleanly or behave in ways end users do not expect. Siloed delivery is the problem DevOps was designed to solve — do not recreate it inside your DevOps team.

Practical steps include shared on-call rotations, cross-functional retrospectives, visible dashboards accessible to all stakeholders, and a culture where raising a blocker early is valued rather than penalised.

3. Setting Unrealistic Timeframes

DevOps genuinely does accelerate delivery — but that acceleration is an emergent outcome of mature practices, not a starting condition. A common mistake for startups is to announce aggressive deadlines the moment a DevOps initiative launches, before any of the supporting practices are in place.

Deadlines that teams cannot meet create a cascade of problems:

• Customer and stakeholder disappointment erodes trust and damages brand reputation.
• Sustained pressure leads to engineer burnout and increased error rates.
• Shortcuts taken to hit an impossible date accumulate as technical debt that slows future delivery even further.
• A blame culture emerges that directly undermines the psychological safety DevOps requires.

Set realistic milestones based on your team’s current capabilities. Use early DevOps wins to demonstrate improved velocity over time, then let actual data drive increasingly ambitious targets.

4. Failing to Automate Everything You Reasonably Can

You can have excellent collaboration, clear systems thinking, and a healthy culture — and still deploy slowly if your pipeline relies heavily on manual steps. Automation is the force multiplier that translates DevOps culture into measurable delivery speed.

Puppet’s State of DevOps Report (2017 edition) highlighted a clear performance gap between high-performing and lower-performing organisations, with automation coverage being one of the key differentiators. High performers were shown to automate across multiple disciplines, including:

• Configuration management — ensuring environments are consistent and reproducible.
• Testing — unit, integration, and security tests running automatically on every commit.
• Deployments — continuous delivery pipelines that remove manual handoffs.
• Change approval processes — lightweight, automated compliance checks rather than manual review gates.

Audit your current pipeline and identify every manual step. Prioritise automating the steps that cause the longest delays or the most frequent errors. Even partial automation of a painful manual process can yield significant improvements.

5. Rigid Processes and Inflexible Thinking

Perhaps the most damaging mistake of all is treating your current approach as the final answer. DevOps is predicated on continuous improvement — the acknowledgement that how you do things today is not necessarily how you should do them tomorrow.

For ISV startups, this inflexibility can manifest in several ways:

• Dismissing team members’ suggestions for better tooling or processes.
• Ignoring customer feedback that points to a different product direction than the one planned.
• Penalising failure rather than treating it as a learning opportunity.
• Locking in workflows and architectures that made sense at day one but constrain growth at day three hundred.

Build structured channels for experimentation: blameless post-mortems, regular retrospectives, product feedback integration into sprint planning. Make it safe — and expected — for every team member to challenge the status quo.

The Bottom Line

DevOps offers ISV startups a genuine competitive advantage, but only when it is adopted as a philosophy rather than a superficial process overlay. Avoid treating it as a checklist, protect the collaborative culture it depends on, set honest timelines, invest in automation, and stay open to change. The startups that internalise these principles are the ones that evolve from scrappy newcomers into established ISVs.