Overview of the Global DevOps Platform Market

Around 2018, analyst firms began publishing dedicated market research on the emerging DevOps Platform category. These reports tracked how organizations were adopting integrated toolchains—spanning continuous integration, continuous delivery, monitoring, and collaboration—to accelerate software delivery. The forecast window of 2018 to 2023 reflected a period of rapid consolidation and maturation in the DevOps tooling ecosystem.

The market research examined product classifications, regional adoption patterns, competitive dynamics, and demand from multiple vertical industries. It also applied standard analytical frameworks such as SWOT analysis and demand-supply modeling to project market trajectories.

Key Vendors in the DevOps Platform Space

The report highlighted several prominent vendors competing in the DevOps Platform market during this period. While the original source cited in the post does not reproduce exact market-share figures, the following companies were named as notable players:

• Rackspace – Offered managed DevOps services and cloud infrastructure support, helping teams operationalize CI/CD pipelines without maintaining all underlying infrastructure in-house.
• SaltStack – Provided infrastructure automation and configuration management capabilities, often used in large-scale DevOps environments for orchestration and event-driven automation.
• CA Technologies – Offered enterprise-grade DevOps tooling including release automation, agile planning, and application performance management.
• Atlassian – Known for its integrated suite of collaboration and development tools including Jira, Confluence, Bitbucket, and Bamboo, which together supported end-to-end DevOps workflows.

Other vendors active in the broader DevOps toolchain ecosystem at this time included GitLab, Jenkins (open source), HashiCorp, Chef, Puppet, and Microsoft (Azure DevOps, then called VSTS).

Market Segmentation

By Product Maturity Classification

The report classified DevOps Platform offerings into three maturity tiers, a framework sometimes used by analysts to differentiate vendor capabilities:

• DevOps Capable – Platforms that provide foundational tooling enabling DevOps practices, but may require significant integration effort or custom configuration.
• DevOps Ready – Platforms with pre-integrated toolchains and reference architectures, reducing time-to-value for teams adopting DevOps.
• DevOps Enabled – Fully integrated platforms that natively embed DevOps principles across the software development lifecycle, often including built-in security (DevSecOps) and compliance controls.

By Industry Vertical

DevOps Platform adoption was analyzed across multiple industry segments, each with distinct requirements and adoption rates:

• IT and Technology – The earliest and most widespread adopter, driven by the need for rapid software release cycles and infrastructure automation.
• BFSI (Banking, Financial Services, and Insurance) – Growing adoption driven by digital transformation, though constrained by regulatory compliance requirements and legacy systems.
• Telecom – Network function virtualization and cloud-native infrastructure created demand for DevOps practices in managing complex distributed systems.
• Retail – E-commerce growth pushed retailers to adopt CI/CD practices to iterate quickly on customer-facing applications.
• Education – A smaller but emerging segment, with institutions modernizing student-facing platforms and administrative systems.

Regional Market Breakdown

The report examined DevOps Platform adoption across major global regions:

• North America – Typically the largest market by revenue, driven by high cloud adoption rates and a mature software engineering culture.
• Europe (including the United Kingdom) – Strong adoption, particularly in financial services and manufacturing sectors, with growing investment in DevSecOps practices.
• Asia-Pacific – A rapidly growing region, with particularly high growth rates in India, China, and Australia, driven by large-scale digital transformation programs.
• Latin America – An emerging market with growing interest, particularly in Brazil and Mexico, as enterprises modernized their IT delivery practices.
• Middle East and Africa – A smaller but developing market, with government-led digital transformation initiatives in several Gulf states contributing to demand.

Key Market Drivers

Several factors were identified as driving growth in the DevOps Platform market during this period:

• Cloud adoption: The shift to public, private, and hybrid cloud infrastructure created demand for automated provisioning and deployment tooling.
• Agile transformation: Organizations moving from waterfall to agile development methodologies required tooling that supported iterative, continuous delivery.
• Competitive pressure: Faster software release cycles became a competitive differentiator, particularly in consumer-facing industries.
• Security and compliance demands: Integrating security testing into CI/CD pipelines (DevSecOps) emerged as a key requirement, expanding the addressable market for DevOps Platforms.
• Microservices and containerization: Growing adoption of Docker, Kubernetes, and microservices architectures increased the complexity of deployment workflows, creating demand for more sophisticated platform tooling.

Analytical Frameworks Used in Market Research

Market research reports of this type typically applied several standard analytical methodologies:

• SWOT Analysis: Evaluating strengths, weaknesses, opportunities, and threats for individual vendors and the market as a whole.
• CAGR Modeling: Compound Annual Growth Rate projections across the 2018–2023 forecast window.
• Demand-Supply Analysis: Mapping end-user demand against vendor capability and capacity.
• Porter’s Five Forces: Assessing competitive intensity, barriers to entry, and buyer/supplier power within the DevOps Platform market.
• Regional Trade Analysis: Examining import/export of services and software licenses across geographic markets.

What This Means for DevOps Practitioners

While market research reports of this kind are primarily targeted at investors, product managers, and business strategists, they offer useful signals for DevOps engineers and architects:

• The consolidation of DevOps tooling into integrated platforms (rather than point solutions) was already a clear trend by 2018, a trajectory that has continued with products like GitHub Actions, GitLab CI/CD, and Azure DevOps absorbing capabilities that previously required multiple separate tools.
• Industry-specific compliance requirements—particularly in BFSI and healthcare—shaped how vendors packaged and positioned their platforms, influencing feature roadmaps around audit trails, access controls, and policy enforcement.
• Regional differences in cloud adoption and regulatory environment remain relevant when selecting and deploying DevOps toolchains at enterprise scale.

For teams evaluating DevOps platforms today, vendor landscape reports remain useful as one input alongside hands-on proof-of-concept testing, community feedback, and total cost of ownership analysis.

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.

Overview: Five Key Testing Trends for 2018

The survey identified five headline trends shaping development and testing practices in 2018:

• Trend 1: Testing spans more browser types, with an increasing focus on only the most recent versions.
• Trend 2: A mix of real devices and simulators is becoming the norm for mobile testing.
• Trend 3: The value of automated testing is well established and broadly recognised.
• Trend 4: Agile and DevOps adoption continues to grow, but bugs are not being fixed any faster.
• Trend 5: The desire to deploy even more quickly appears to be levelling off.

Agile Adoption: Broad but Uneven

Agile methodology has become near-universal among software development organisations. According to the 2015 edition of the survey, 82% of respondents said their organisation had adopted agile. By 2018, that figure had grown to 91%.

However, depth of adoption tells a more nuanced story. The survey found that agile adoption is inversely proportional to company size — larger organisations tend to adopt it more slowly and less completely. As the report notes, “any transformation typically takes longer at a large organisation, and agile is no exception.”

Continuous Integration: Widely Practised, Continuous Testing Still Catching Up

The survey found that the overwhelming majority of respondents work in organisations that practise Continuous Integration (CI). The report highlights that as CI adoption accelerates, continuous testing must be treated as an integral part of the CI pipeline — not an afterthought. In the survey’s words: “as more companies adopt CI chains to increase the speed and quality of releasing code as well as to find bugs in the software development process itself, it will be extremely critical to include continuous testing in these conversations.”

DevOps Adoption: Growing, but Still Early for Most Organisations

DevOps, a more recent trend than agile, was first tracked in this survey series in 2017. The 2018 findings show meaningful progress, but full enterprise-wide adoption remains rare:

• In 2017, only 10% of respondents reported that their organisation had fully embraced DevOps company-wide.
• By 2018, that number had grown to 17%.
• The largest groups in the survey reported that they had either just started with DevOps or had a few teams fully immersed in its practices.

“Teams are adapting to move faster, and a rising adoption of DevOps and a commitment to automated testing reflects how modern methodologies are becoming more and more critical to successful application development.”

Diane Hagglund, Principal Researcher, Dimensional Research

Main Challenges Identified

1. Bug-Fixing Speed Has Not Improved

Perhaps the most critical finding in the report is an alarming lack of progress in how quickly bugs are fixed. Despite widespread agile and DevOps adoption, defect resolution speed has not kept pace. The survey warns that “debugging code quickly remains a barrier to reaping the full benefits of agile and DevOps.” Teams moving faster through development and deployment pipelines cannot fully capitalise on that speed if bug remediation remains slow.

2. Automated Testing Is Not Yet the Default

While the value of automated testing is widely understood and management is typically supportive, the survey found that automation has not yet become the default mode of testing for most teams. The gap between endorsement and execution remains a real obstacle to scaling quality assurance in fast-moving environments.

3. Testing Complexity Continues to Grow

The breadth of what testers must cover is expanding continuously. As the survey puts it, “testers now need to consider multiple platforms, devices, browsers, and versions of operating systems.” This fragmentation makes thorough manual testing impractical and reinforces the case for robust, scalable automated testing strategies.

Takeaways for DevOps and Testing Teams

The 2018 Sauce Labs survey paints a picture of an industry in genuine transition. Agile and DevOps adoption are moving in the right direction, and the business case for automated testing is clearly understood. But significant gaps remain:

• CI pipelines need continuous testing baked in from the start, not bolted on afterwards.
• Bug-fixing speed must improve for agile and DevOps to deliver their full value — faster deployment without faster defect resolution creates its own risks.
• Organisations need to move beyond management buy-in for automation and create the conditions — tooling, skills, and processes — for automated testing to become standard practice.
• Cross-platform and cross-browser test coverage requires a deliberate strategy, not ad-hoc expansion.

For teams looking to mature their testing practice, this survey serves as a useful benchmark: it shows where the industry stands and, more importantly, where the obstacles to faster, higher-quality software delivery still lie.

Enterprise DevOps: Past Barriers, Lasting Lessons

When analysts began calling a watershed moment the “year of enterprise DevOps,” survey data suggested roughly half of organizations had implemented some form of DevOps practice. Yet having a DevOps initiative underway is very different from having one that is mature, scaled, and delivering measurable business value. The barriers to meaningful adoption were — and in many organizations still are — real and varied.

The ten barriers outlined below were compiled from practitioner conversations spanning organizations at every stage of DevOps maturity, from early explorers to teams already attempting to scale. Understanding these barriers as a ranked list provides a useful diagnostic framework regardless of what year you are reading this.

The 10 DevOps Barriers, Ranked

10. Executive Buy-In

The barrier: Securing support, resource alignment, and budget commitment from senior leadership has historically been one of the first walls DevOps practitioners hit. Without executive sponsorship, DevOps efforts remain isolated, underfunded, and at risk of being deprioritized.

The trend: As IT-related initiatives climbed in priority among CEOs — with technology investment ranked among the top business priorities in Gartner surveys — DevOps initiatives that demonstrably balance agility with risk management gained a clearer path to C-suite support. The key accelerator: proactively educating business stakeholders using business outcomes language, not engineering jargon. Top-down sponsored DevOps programs became increasingly common as the practice matured.

9. Fragmented Toolchain

The barrier: The DevOps ecosystem grew rapidly, producing a sprawl of open-source and commercial tools that often did not integrate cleanly. Teams accumulated overlapping tools for CI, CD, monitoring, and configuration management, creating inconsistency across teams and slowing standardization.

The trend: Toolchain consolidation is a multi-year process. Governance frameworks and platform engineering practices have since helped organizations rationalize their toolsets, but fragmentation remains a real challenge for large enterprises with distributed teams and multiple lines of business.

8. Budget Allocation

The barrier: A common misconception — that DevOps was essentially free because many tools are open source — led to chronic under-budgeting. In practice, enterprise-grade support, skilled engineers, and the cost of non-standardized adoption added up quickly.

The trend: As organizations gained experience, the true cost of DevOps became clearer. Mature programs began budgeting explicitly for tooling support contracts, training, and deliberate experimentation — including the cost of controlled failure. Recognizing that “open source” does not mean “free at enterprise scale” is a prerequisite for realistic planning.

7. Skills Gaps

The barrier: DevOps expertise was scarce and often treated as a floating consultant overlay rather than an embedded organizational capability. Infrastructure and operations professionals needed structured upskilling, but training programs were inconsistent.

The trend: Structured training programs, standardized blueprints, and common toolsets began normalizing DevOps competency development. The emergence of internal DevOps champions — engineers who advocate for and teach practices across teams — proved effective. Specialized roles such as NetOps and ITOps developed focused DevOps literacy tracks. This barrier lowers over time but does not disappear entirely.

6. Application Complexity

The barrier: Legacy monolithic and three-tier applications are difficult to deploy using modern CI/CD pipelines. Application modernization — breaking monoliths into services, migrating to cloud-native architectures — is a prerequisite for many DevOps patterns, yet it is a massive undertaking.

The trend: Application modernization is a decade-long initiative at most large enterprises. DevOps practices themselves accelerate modernization by improving feedback loops and enabling incremental refactoring. Progress is real but necessarily gradual.

5. No Formal DevOps Plan

The barrier: Many early DevOps efforts were grassroots, bottom-up movements disconnected from formal planning processes, measurable objectives, or business goals. Without a plan, DevOps remained a technical experiment rather than a strategic capability.

The trend: As DevOps became mainstream, organizations began attaching it to formal program management structures — with budget, staffing, roadmaps, and KPIs tied to measurable business outcomes. Treating DevOps as a program, not a project, changes its trajectory significantly.

4. Managing Environment Complexity

The barrier: As applications became distributed across multi-cloud environments and accessed from diverse endpoints — including mobile and IoT — replicating and managing consistent environments across development, testing, and production became increasingly difficult.

The trend: Infrastructure-as-code, environment blueprinting, and sandbox automation addressed this directly. Automating the creation and teardown of environment replicas within CI/CD pipelines reduces manual configuration drift, improves governance, and increases both team productivity and release velocity.

3. Legacy Infrastructure (Technical Debt)

The barrier: Brownfield deployments — existing production systems running on on-premises infrastructure with proprietary tools and manual processes — create significant friction for cloud-native DevOps patterns. Physical infrastructure rigidity, long provisioning cycles, and accumulated technical debt all slow adoption.

The trend: Infrastructure automation platforms and as-a-service models provide a bridge. Tools that span both legacy brownfield and modern greenfield environments allow organizations to introduce new workflows without forcing a disruptive “rip and replace” approach. Protecting existing investment while incrementally modernizing is the pragmatic path.

2. Test Automation

The barrier: Organizations often implemented CI/CD pipelines while treating test automation — and security — as afterthoughts. Manual, ad hoc testing becomes a bottleneck as deployment frequency increases, directly undermining the value of a fast pipeline.

The trend: Continuous testing emerged as a recognized, critical component of the CI/CD pipeline rather than a phase that follows development. Automated test environments, sandbox-based testing, and shift-left security practices help break the manual testing bottleneck, improving both quality and release velocity at scale.

1. Culture

The barrier: Consistently rated the number one barrier to DevOps adoption — especially in enterprise environments — culture encompasses the willingness to collaborate across silos, embrace shared responsibility, tolerate controlled risk-taking, and treat failure as a learning opportunity rather than a punishable event. “Business as usual” attitudes actively undermine DevOps transformation.

The trend: Culture change does not happen by accident. Enterprises that succeed treat cultural transformation as an explicit program deliverable, not a byproduct of deploying tools. Executive sponsorship, the deliberate selection and empowerment of DevOps champions, and collaborative toolsets together create the conditions for a sustainable shift — both top-down and bottom-up — that breaks functional silos and builds genuine shared ownership.

The Bigger Picture: DevOps as a Continuous Practice

Taken together, these ten barriers paint a picture of an organizational transformation that is neither quick nor simple. DevOps is not an on/off switch. It is a set of recursive learnings — each improvement informing the next — applied iteratively as an organization discovers the right mix of culture, process, and tooling for its specific context.

Several important implications follow from this framework:

• No two organizations share the same barrier profile. Size, industry, existing infrastructure, and organizational culture all shape which barriers are most acute.
• Tool adoption without cultural change yields limited results. The top-ranked barrier — culture — enables or constrains progress on every other barrier below it.
• Budget realism matters from the start. Underestimating the true cost of DevOps (tooling, talent, training, experimentation) sets programs up for failure.
• Expectation management prevents disillusionment. DevOps adoption follows a maturity curve. Overpromising early outcomes creates backlash that can derail programs before they deliver value.

Understanding these barriers — and the trends that reduce them — remains as relevant for organizations planning DevOps investments today as it was when this analysis was first published. The specifics of tooling evolve; the organizational and cultural dynamics change more slowly.

DevOps: Still Evolving, Still Expanding

DevOps—the practice of integrating software development and IT operations teams into a unified, collaborative workflow—has spread rapidly across organizations of all sizes. The core promise is straightforward: break down the silos between Dev and Ops so that software can be delivered faster, at lower cost, and with higher quality. Despite significant momentum, DevOps remains a discipline in flux. Its success depends not just on tooling, but on cultural alignment, process redesign, and organizational buy-in. The five trends below reflect where DevOps has been heading and why each one still deserves attention.

1. Microservices Architecture

The adoption of microservices accelerated significantly as teams looked for ways to decompose large, monolithic applications into independently deployable units. Rather than deploying a single large codebase, microservices break functionality into discrete services, each running in its own isolated environment with its own allocated compute and memory resources.

This isolation makes microservices a natural fit for DevOps workflows:

• Independent deployment: Teams can release individual services without coordinating a full application deployment.
• Targeted scaling: A single high-demand service can be scaled horizontally without scaling the entire application stack.
• Clearer ownership: Small, cross-functional teams can own specific services end-to-end, reinforcing DevOps accountability models.
• Composability: Discrete microservices can be combined to build larger, more complex systems incrementally.

Containerization platforms such as Docker and orchestration tools like Kubernetes have become tightly coupled with this trend, providing the runtime infrastructure microservices need to operate reliably at scale.

2. Tool Consolidation

The DevOps toolchain ecosystem is vast. Configuration management tools such as Chef, Puppet, Ansible, and SaltStack each offer overlapping but distinct capabilities. Add in CI/CD platforms, monitoring stacks, artifact repositories, and secrets managers, and the result is a sprawling, difficult-to-maintain toolchain.

Organizations have responded by consolidating toward a smaller set of well-integrated, “best-in-breed” tools rather than adopting every available option. This consolidation trend also drove significant M&A activity—Cisco’s acquisition of AppDynamics for approximately $3.7 billion is one frequently cited example of a major vendor expanding its DevOps and observability portfolio through acquisition.

The practical benefits of consolidation include:

• Reduced cognitive overhead for engineers managing fewer tools
• Lower licensing and maintenance costs
• Improved integration between pipeline stages
• Easier onboarding for new team members

3. Security Shifting Left (DevSecOps)

Security has historically been treated as a gate at the end of the software delivery lifecycle—reviewed by a separate team shortly before release. DevOps practices have pushed security earlier into the development process, a shift commonly called “shifting left” or, more formally, DevSecOps.

The 2017 Puppet State of DevOps Report highlighted a meaningful performance gap tied to this approach. According to that report, high-performing teams who had integrated security practices into their delivery pipeline spent approximately 50% less time remediating security issues compared to low-performing teams—a finding consistent across both the 2016 and 2017 survey years.

Practical DevSecOps patterns include:

• Static application security testing (SAST) integrated into CI pipelines
• Dependency vulnerability scanning on every build
• Infrastructure-as-code security linting (e.g., checking Terraform or Ansible playbooks for misconfigurations)
• Threat modeling during the design phase, not after deployment

4. Deeper and Broader Automation

Automation has always been a foundational DevOps principle, but the scope of what gets automated has expanded considerably. The 2017 Puppet State of DevOps Report found that high-performing organizations automated significantly more of their software delivery pipeline than low performers—spanning configuration management, testing, deployment, and even change-approval workflows.

Key areas where automation delivers outsized value in a DevOps context:

• Configuration management: Ensuring infrastructure state is defined in code and applied consistently (Ansible, Puppet, Chef).
• Testing: Running unit, integration, and end-to-end test suites automatically on every commit.
• Deployment: Eliminating manual handoffs in the release pipeline through continuous delivery pipelines.
• Approval workflows: Using policy-as-code to automate low-risk change approvals, reducing bottlenecks without sacrificing governance.

The cumulative effect is a reduction in toil—repetitive, manual work that does not scale—freeing engineers to focus on higher-value problem solving.

5. Big Data and Machine Learning in the Delivery Pipeline

Automated software delivery pipelines generate enormous volumes of data: build logs, test results, deployment events, performance metrics, error rates, and user behavior signals. Managing and extracting value from this data has become a discipline of its own.

Organizations have started applying machine learning and data analytics to this telemetry to:

• Identify patterns in build or test failures before they reach production
• Predict deployment risk based on historical change data
• Automatically surface anomalies in application performance metrics
• Generate actionable insights for decision-makers without requiring manual log analysis
• Integrate operational data with business analytics platforms for end-to-end visibility

This intersection of DevOps and data engineering is increasingly referred to as AIOps—applying AI and ML to IT operations data to enable faster, more informed responses to system events.

Looking Ahead

These five trends—microservices, tool consolidation, DevSecOps, expanded automation, and data-driven operations—reflect a consistent underlying direction: reducing friction, increasing feedback, and making software delivery a reliable, repeatable engineering discipline rather than a heroic, manual effort. Organizations that invest in these areas methodically, with an eye toward culture as much as tooling, tend to see the most durable improvements in delivery performance and operational stability.

1. Serverless Computing

Serverless computing is not a new concept — the underlying ideas have existed since the mid-2000s. What changed is accessibility. Modern serverless platforms such as AWS Lambda and Apache OpenWhisk dramatically lowered the barrier to entry, letting developers deploy event-driven functions without managing the underlying infrastructure. For MSPs, this means clients can reduce operational overhead and infrastructure costs. Expect serverless to become an increasingly common architecture choice for applications that demand elastic scaling and consumption-based pricing.

2. Self-Healing Infrastructure and the Push Toward NoOps

Automation has always been a foundational principle of DevOps, but maturing tooling is pushing it much further. The term NoOps describes an environment where infrastructure provisioning, scaling, and application management are so thoroughly automated that routine human intervention is no longer required. Self-healing systems can detect failures and remediate them automatically — restarting crashed containers, rerouting traffic away from unhealthy nodes, or rolling back a bad deployment without a pager alert firing at 2 a.m.

For MSPs, NoOps principles create an opportunity to deliver higher service quality at lower operational cost. The practical reality is that full NoOps remains aspirational for most organizations, but directional movement toward greater automation is well underway.

3. Shift-Left: Moving Quality, Security, and Compliance Earlier in the Pipeline

Shift-left refers to moving software delivery practices — particularly quality assurance, security reviews, and compliance checks — earlier in the development lifecycle rather than treating them as gates at the end. The metaphor comes from reading a pipeline diagram left to right: shifting activities to the left means performing them sooner.

The benefits are concrete: defects found during development cost far less to fix than those discovered after deployment. Shift-left practices include test-driven development (TDD), static code analysis integrated into CI pipelines, automated compliance policy checks, and pre-commit security scanning. For MSPs managing multiple client pipelines, embedding these checks into a shared CI/CD platform is an efficient way to deliver consistent quality across accounts.

4. DevSecOps: Integrating Security into the DevOps Workflow

In many organizations, security teams historically operated in isolation — reviewing code and infrastructure only after development work was complete. This created friction, slowed delivery, and left vulnerabilities undetected until late in the cycle. DevSecOps aims to break down that silo by treating security as a shared responsibility integrated throughout the software delivery process rather than a separate phase at the end.

Practical DevSecOps implementations include automated vulnerability scanning in CI pipelines, infrastructure-as-code security linting, secrets management tooling, and ongoing runtime threat detection. For MSPs, offering DevSecOps capabilities — or helping clients adopt them — is a meaningful differentiator as security requirements grow more stringent across regulated industries.

5. DataOps: Applying DevOps Principles to Data Pipelines

Just as security teams were initially left out of the DevOps movement, data engineering and analytics teams have often operated separately from software delivery workflows. DataOps extends DevOps principles — automation, collaboration, version control, and continuous delivery — to the data pipeline lifecycle. This means treating data ingestion, transformation, and delivery with the same rigor applied to application code: automated testing of data quality, version-controlled pipeline definitions, and monitoring of data freshness and accuracy.

Organizations that adopt DataOps can expect faster iteration on analytics products, fewer data quality incidents reaching production, and tighter alignment between data teams and the rest of engineering. MSPs with data engineering practices should evaluate how DataOps tooling and culture can be wrapped into their service offerings.

What This Means for MSPs

Each of these trends represents both a challenge and an opportunity for managed service providers. Clients will increasingly expect their MSP to have working knowledge of serverless architectures, automated remediation, shift-left pipelines, integrated security practices, and data operations. MSPs that build internal expertise in these areas — and translate that expertise into repeatable service offerings — will be better positioned to retain existing accounts and win new ones.

The common thread across all five trends is the same principle that has driven DevOps from the beginning: break down silos, automate repetitive work, and deliver faster with greater confidence. The scope of that mission is simply expanding.

What Is DevOps and Why Did It Matter in 2017?

DevOps describes the cultural and operational relationship between software development and IT operations teams in an enterprise. Rather than treating these disciplines as separate silos, DevOps promotes continuous collaboration, shared tooling, and faster feedback loops. The goal is greater organisational agility: the ability to ship higher-quality software more frequently and reliably.

By 2017, many organisations had adopted foundational DevOps practices—continuous integration, automated builds, and cloud deployment pipelines. The emerging question was: what comes next? Three themes rose to the top of the agenda: testing, security, and metrics.

“As organizations race to deliver innovations to market, they will find that ensuring the quality, security and performance of their applications are just as important as speed. DevOps plays a pivotal role in making sure these happen as businesses put software at the core of their DNA to survive and thrive in today’s application economy.”

Richard Gerdis, Vice President, DevOps, Asia Pacific & Japan, CA Technologies

1. Continuous Testing Across the Entire Pipeline

One of the most persistent anti-patterns in traditional software delivery was treating testing as a single, late-stage gate—typically performed by a dedicated QA team just before release. This approach creates several compounding problems:

• Defects discovered late are far more expensive to fix.
• A bottleneck at the QA phase slows the entire pipeline.
• Rapidly changing customer expectations mean existing, already-tested code can become functionally incorrect without anyone noticing.

The 2017 prescription from practitioners was straightforward: shift testing left and make it continuous. In practice, this means:

• Rigorous testing at every stage of the development lifecycle—from unit tests written alongside code to integration tests triggered on every commit.
• Shared ownership between developers and QA engineers. Developers write and run tests; QA engineers focus on exploratory, performance, and acceptance testing rather than acting as the sole testing layer.
• Visibility into test results for operations teams, enabling them to evaluate quality in context of production behaviour, not just pre-release snapshots.

This model—sometimes called continuous testing—aligns test coverage with deployment frequency. The faster you ship, the more automated your safety net needs to be.

2. Security Moves into the Pipeline: The Rise of DevSecOps

The increasing sophistication of cyber threats in 2017 forced a rethinking of where security fits in the delivery lifecycle. Historically, security reviews happened after development was complete—often performed by a separate security team whose findings could delay or derail a release.

The emerging answer was DevSecOps: the unification of development, operations, and security into a single, collaborative workflow. The core principle is that security is not a gate to pass through at the end; it is a property that must be built into every stage of the pipeline.

Key practices advocated at the time included:

• Deploying within a hardened architecture. Code quality alone is not sufficient; the infrastructure, networking, and access controls surrounding the application must also meet a security baseline before deployment.
• Treating security testing as a specialised discipline. Security-related testing involves unique tooling (static analysis, dynamic analysis, dependency scanning), specialist knowledge, and regulatory constituencies that differ from standard functional testing. Integrating these into the CI/CD pipeline—rather than bolting them on at the end—reduces the cost and friction of remediation.

The DevSecOps movement has only accelerated since 2017. Today, practices such as software composition analysis (SCA), secrets scanning, and infrastructure-as-code security scanning are considered standard components of a mature pipeline.

3. Metrics: Making DevOps Outcomes Measurable

The third major trend was a growing consensus that DevOps success needed to be measured, not just felt. With more pipeline data available than ever before, teams had the raw material to answer questions that had previously been answered only by intuition:

• Where are the bottlenecks in our delivery process?
• Which teams or practices are producing the most reliable outcomes?
• Where do we need coaching or process intervention?

Two categories of metrics were identified as particularly valuable:

Collective (Team-Level) Metrics

Collective metrics measure the performance of the pipeline and the team as a whole. Examples include deployment frequency, lead time for changes, mean time to recovery (MTTR), and change failure rate—what the DevOps Research and Assessment (DORA) programme would later popularise as the Four Key Metrics. These help organisations identify systemic bottlenecks and optimise resource allocation across the entire value stream.

Individual Metrics

Individual metrics track contribution and quality at the engineer level. When used carefully—and combined with psychological safety—they can surface coaching needs and help replicate the behaviours that lead to good outcomes. When misused, they create perverse incentives. The 2017 consensus was that individual metrics should inform coaching, not performance reviews.

A broader goal articulated at the time was industry-wide agreement on common metrics standards. Shared benchmarks would allow organisations to compare their DevOps maturity against peers, not just against their own historical baselines.

How These Trends Have Evolved

Looking back from the present, all three trends identified in 2017 proved accurate in direction, even if the specific tooling and terminology have evolved:

• Testing has become increasingly automated and AI-assisted, with continuous testing platforms integrated directly into IDEs and CI systems.
• DevSecOps is now a widely recognised discipline with its own toolchain, certification programmes, and dedicated engineering roles.
• Metrics have been formalised through frameworks such as DORA and SPACE, giving teams shared vocabularies for measuring engineering effectiveness.

For teams building or refining their DevOps practice today, these three pillars—quality through continuous testing, security as a first-class pipeline concern, and data-driven improvement through metrics—remain as relevant as they were in 2017.

DevOps Moves from Grassroots to Enterprise Standard

What began as a bottom-up movement within small, forward-thinking teams has become the dominant model for software delivery across organizations of every size. By 2017, DevOps had matured well beyond its scrappy origins: large, complex enterprises were no longer simply chasing “unicorns” like Netflix or Amazon — they were actively leading the way in patterns and best practices for scaling DevOps across entire organizations.

Understanding why that shift happened — and what it means for how teams work today — requires looking at the pressures that made DevOps adoption not just desirable, but necessary.

Competitive Pressure: Adaptability as a Survival Trait

Competition in the digital era has a way of accelerating expectations. Amazon redefined retail. Apple reshaped music distribution. Uber disrupted urban transportation. In each case, software was the disruptive mechanism — and incumbents that failed to respond with equal software velocity lost market share or disappeared entirely.

This pattern is visible in broader economic data as well. The average tenure of a company on the S&P 500 Index reportedly fell from roughly 61 years in 1958 to approximately 18 years in the mid-2010s, with projections suggesting it could shrink to around 10 years by 2030. Whether or not those precise figures hold up, the directional reality is widely accepted: market positions are more precarious than ever, and software delivery speed is a meaningful competitive variable.

For engineering teams, the implication is direct: organizations that invest in DevOps practices — continuous integration, automated testing, rapid deployment pipelines — are better positioned to adapt to market change than those that don’t.

Downtime Has a Real Cost — and DevOps Reduces It

Reliability is not a soft concern. IDC Research figures cited around this period estimated that infrastructure failures cost the average Fortune 1,000 company approximately $100,000 per hour in downtime, with mission-critical application failures pushing that figure to between $500,000 and $1,000,000 per hour. These numbers vary by industry and organization, but the order of magnitude makes the point: unplanned outages are expensive.

The 2016 State of the DevOps Report (published by Puppet and DORA) found that high-performing IT organizations deploying DevOps practices shipped code approximately 200 times more frequently than low performers, and recovered from failures roughly 24 times faster. Faster recovery directly translates to reduced downtime and lower incident costs.

High-performing DevOps organizations deploy 200× more frequently and recover from failures 24× faster than low-performing counterparts.

2016 State of the DevOps Report (Puppet/DORA)

Microservices and Containers Drive Modern Application Delivery

Microservices architecture emerged as a natural complement to DevOps practice. By decomposing applications into small, independently deployable services — each responsible for a single capability — teams can develop, test, and release changes with far fewer interdependencies. This reduces coordination overhead and release risk, making faster delivery cycles practical rather than heroic.

Containers provided the deployment vehicle that made microservices operationally tractable. Because containers package an application and its runtime dependencies into a single, portable unit, they simplify consistency across development, staging, and production environments. Each container runs an isolated process with minimal overhead, making them well suited to microservice deployments at scale.

Gartner projected at the time that 50% of enterprises would be running containers in production by the end of 2018. Container adoption has since grown substantially, with Kubernetes emerging as the de facto orchestration platform for managing containerized workloads at scale.

Shift-Left: Moving Quality Earlier in the Pipeline

“Shift-left” refers to the practice of moving quality-related activities earlier in the software delivery lifecycle — to the left on a typical pipeline diagram — rather than waiting until deployment or production. Activities that shift left include:

• Automated unit and integration testing triggered on every commit
• Static code analysis and security scanning integrated into the CI pipeline
• Performance testing run against pre-production environments before release
• Chaos engineering and disaster-recovery rehearsals practiced continuously, not just during planned drills

The economic rationale is straightforward: defects are exponentially cheaper to fix the earlier they are discovered. A bug caught during a developer’s local build costs minutes to resolve. The same bug caught in production can cost hours of incident response, rollback, and customer remediation.

Big Data and Machine Learning Enter the Delivery Pipeline

DevOps pipelines generate significant volumes of structured data: build durations, test pass rates, deployment frequencies, error rates, rollback events, and more. As organizations matured their pipelines, the opportunity to apply analytics and machine learning to this data became apparent.

Practical applications include:

• Predictive failure detection: identifying patterns in pipeline metrics that historically precede production incidents
• Pipeline optimization: spotting bottlenecks in build or test stages that inflate lead times
• Anomaly detection in deployments: flagging unusual behavior in newly released code before it fully propagates

This trend has accelerated. AIOps platforms, intelligent observability tools, and ML-driven incident management systems are now established product categories — all rooted in the insight that pipeline and operational data is a valuable, underutilized signal.

Consolidation: Standardizing DevOps for Scale, Compliance, and Cost

As DevOps spreads across large organizations, a common pattern emerges: each team independently selects tools, builds pipelines, and defines processes. The result is a sprawling landscape of “snowflake” setups — difficult to govern, audit, or support at scale.

The organizational response is consolidation: investing in a shared, self-service DevOps platform that provides standardized pipelines, approved tool integrations, shared environments, and consistent security and compliance controls — while remaining flexible enough to accommodate team-specific requirements.

The benefits of this approach include:

• Reusable pipeline templates that reduce setup time for new projects
• Centralized visibility into deployment frequency, lead time, and change failure rates across all teams
• Consistent application of security scanning, compliance checks, and audit logging
• Reduced licensing and tooling costs through standardization

Internal developer platforms (IDPs) — now a well-established concept — are the mature expression of this consolidation trend.

Supporting Hybrid-Everything: The Reality of Complex Enterprises

Few large organizations get to start with a clean slate. In practice, engineering teams must support a hybrid state across multiple dimensions simultaneously:

• Architecture: monolithic legacy applications running alongside microservices
• Infrastructure: on-premises data centers alongside public and private cloud environments
• Workload type: virtual machines alongside containers
• Delivery model: traditional quarterly releases alongside continuous delivery pipelines

Rather than treating this hybrid state as a temporary problem to be solved by a single migration, mature DevOps organizations treat it as a permanent operational condition to be managed. This means investing in tooling and platforms that are genuinely stack-agnostic — able to integrate with legacy systems and modern cloud-native architectures without requiring a full rewrite as a precondition.

Culture Remains the Critical Enabler

Every credible analysis of DevOps transformation identifies the same constraint: tooling and process are relatively straightforward to change; organizational culture is not.

The cultural attributes that distinguish high-performing DevOps organizations include:

• Shared ownership: development and operations teams share accountability for reliability, not just for their individual outputs
• Empowered, autonomous teams: small teams with clear scope and the authority to make decisions without excessive approval chains
• Blameless post-mortems: incidents are treated as learning opportunities, not occasions for blame assignment
• Continuous learning: teams invest time in improvement, experimentation, and skill development — not just delivery

Organizations that invest only in CI/CD tooling without addressing these cultural dynamics typically see limited returns. The tooling amplifies whatever culture already exists. A culture of collaboration and learning amplified by automation produces compounding improvements; a siloed, blame-oriented culture produces automated silos.

Key Takeaways

• DevOps adoption is driven by competitive pressure, not just engineering preference — software velocity is a business survival variable.
• Microservices and containers enable independent deployment of discrete services, reducing release risk and accelerating delivery.
• Shift-left practices catch defects earlier and reduce the total cost of quality.
• Pipeline data is a valuable analytics asset; machine learning can surface predictive insights about failures and bottlenecks.
• At enterprise scale, consolidation around shared platforms is essential for governance, cost control, and consistency.
• Hybrid infrastructure and architecture states are the norm, not the exception — DevOps tooling must accommodate them.
• Culture — shared responsibility, autonomy, blameless learning — is the factor that determines whether all the other investments pay off.

DevOps Moves Beyond Dev and Ops

By early 2017, DevOps had already moved well beyond being a niche methodology. Enterprises were adopting it primarily because it breaks down the silos between development and operations teams, enabling faster, more reliable software delivery and greater organisational agility overall.

At the time, CA Technologies identified three trends it expected to dominate DevOps conversations throughout the year. Notably, none of the three were narrowly about code development or infrastructure operations. Instead, they centred on testing, security, and metrics — three disciplines that cut across the entire software delivery lifecycle.

“As organisations race to deliver innovations to market, they will find that ensuring the quality, security and performance of their applications are just as important as speed.”

Richard Derdis, DevOps Vice President, Asia Pacific & Japan, CA Technologies

Trend 1: Continuous Testing

Faster release cycles create pressure on quality assurance. When testing is treated as a single, discrete phase at the end of the development pipeline, it becomes a bottleneck — and shortcuts become tempting. The result is code that reaches production before it has been adequately validated.

Continuous testing addresses this by embedding quality checks throughout the entire DevOps lifecycle rather than confining them to a single gate before release. In practice, this means:

• Unit and integration tests run automatically on every code commit.
• Test results are immediately visible to both developers and operations teams.
• Performance and regression tests are executed in staging environments that mirror production as closely as possible.
• Failed tests block promotion to the next pipeline stage rather than being deferred.

The underlying principle is straightforward: speed without quality is counterproductive. Continuous testing is the mechanism that lets teams move fast without accumulating technical debt or shipping defects to end users.

Trend 2: DevSecOps — Security Joins the Pipeline

Security had historically been treated as an afterthought in software delivery — a final review conducted by a separate team before a release was signed off. As cybercrime grew in both frequency and sophistication, this approach became untenable.

CA Technologies framed security validation as a specialised form of testing. Like functional testing, it needs to happen continuously and early — not just at the end of a release cycle. This approach gave rise to the term DevSecOps: the integration of security practices directly into the DevOps workflow.

What makes security testing distinct from standard functional testing includes:

• Dynamic requirements: Threat landscapes change constantly, so security test cases must be updated continuously.
• Specialist involvement: Security engineers, compliance officers, and sometimes external auditors need to be part of the process — stakeholders not traditionally included in dev or ops workflows.
• Toolchain integration: Static analysis (SAST), dynamic analysis (DAST), and dependency scanning tools must be integrated into the CI/CD pipeline so they run automatically.

The shift-left security model — catching vulnerabilities earlier in the development process — is now a mainstream practice, but it was only beginning to gain serious traction in 2017.

Trend 3: DevOps Metrics and Measurement

It is difficult to improve a process you cannot measure. Yet in 2017, many organisations had implemented DevOps tooling and culture without putting meaningful measurement frameworks in place. They knew deployments were happening faster, but they could not quantify by how much, or identify where the remaining bottlenecks lived.

The growing focus on DevOps metrics reflected a maturation of the discipline. Teams were moving past initial adoption and asking harder questions about optimisation and return on investment. Key metrics that began receiving wider attention include:

• Deployment frequency: How often code is successfully released to production.
• Lead time for changes: The time from a code commit to that code running in production.
• Mean time to recovery (MTTR): How quickly a team can restore service after an incident.
• Change failure rate: The percentage of deployments that result in a degraded service or require a hotfix.

These four metrics — later formalised as the DORA metrics — give teams an evidence-based view of their delivery performance. Identifying bottlenecks (such as a slow manual approval step or an unreliable test suite) becomes much easier when the data is in front of you.

The Common Thread: Balancing Speed with Quality

All three trends point to the same underlying challenge: organisations adopting DevOps primarily to move faster were discovering that velocity alone is not a sufficient goal. Shipping broken or insecure software quickly is worse than shipping good software slowly.

Continuous testing ensures quality keeps pace with speed. DevSecOps ensures security is not sacrificed for delivery timelines. Metrics ensure teams can see where they are succeeding and where they are not, so improvement is grounded in data rather than intuition.

These themes — testing throughout the lifecycle, security as a shared responsibility, and measurement as a foundation for improvement — have only grown in importance in the years since. They remain as relevant today as they were when first highlighted in 2017.

Cloud computing had already transformed many enterprises in the years before 2017, but analysts at Forrester Research argued the market was entering a meaningful second wave — one driven not by startups and early adopters, but by large enterprises migrating core business workloads. Forrester predicted the cloud market would accelerate significantly in 2017 as organizations sought compute efficiency at scale.

“The No. 1 trend is here come the enterprises,” said Forrester analyst Dave Bartoletti, primary author of the research. “Enterprises with big budgets, data centers and complex applications are now looking at cloud as a viable place to run core business applications.”

Forrester surveyed more than 1,000 North American and European enterprise infrastructure technology decision-makers and found:

• 38% were actively building private clouds.
• 32% were procuring public cloud services.
• The remainder planned to implement some form of cloud in the next 12 months.
• 59% said they were adopting a hybrid cloud model.

Based on this research, Forrester offered six cloud predictions for 2017. Each remains instructive for understanding how enterprise cloud strategy has evolved.

1. Regional Cloud Players Complement Mega-Providers

Amazon Web Services launched in 2006 with a handful of simple compute and storage services. By 2017, AWS was operating at an $11 billion annual run rate. Microsoft Azure, Google Cloud and IBM joined AWS as what Bartoletti termed “mega-cloud providers,” opening new data centers around the world and making regulatory concessions — such as Microsoft’s agreement to have T-Systems manage its cloud infrastructure in Germany to comply with data localisation requirements.

Despite their scale, these providers cannot address every regional compliance requirement, latency constraint or industry-specific need. Forrester predicted smaller regional cloud providers would see increased adoption in 2017 as a result. The practical guidance from Bartoletti: “Keep your options open and don’t be afraid to use multiple providers.”

This prediction proved accurate. Multi-cloud strategy became a dominant enterprise architecture pattern in the years that followed, driven by exactly the factors Forrester identified: regulatory complexity, data sovereignty and the limitations of any single vendor’s footprint.

Capital One’s public shift away from private cloud to AWS illustrated the broader enterprise momentum. CIO Rob Alexander explained the rationale: “We recognised that we were spending a lot of time, energy, effort and management bandwidth to create infrastructure that already exists out there in a much better state and is evolving at a furious pace.”

2. Cloud Cost Containment Becomes a Core Discipline

A common assumption in 2017 was that moving to public cloud automatically reduced IT costs. Forrester challenged this. CIOs managing multiple cloud providers faced increasingly complex vendor relationships, and poorly managed cloud consumption — such as leaving instances running over weekends when not needed — could result in spending more than an equivalent on-premises deployment.

Forrester predicted that IT organisations would mature their cost management practices in 2017. As a concrete example, Bartoletti cited a cloud architect at a large software company who reduced a $2.5 million annual cloud bill by $300,000 simply through consumption monitoring.

Cost management tooling from providers including AWS, Cloudability and Cloudyn was already available at the time. Bartoletti was direct: “There’s no reason in 2017 for your cloud costs to grow out of control.”

Cloud financial management — now commonly called FinOps — has since become its own engineering discipline, validating this prediction entirely.

3. Lift-and-Shift Migration Tools Accelerate Cloud Adoption

The ideal approach to cloud migration has always been to refactor or re-architect applications to take full advantage of cloud-native capabilities such as elasticity, managed services and auto-scaling. However, re-architecting is expensive and time-consuming — often impractical for large application portfolios.

Forrester predicted that lift-and-shift migration tools — which move existing applications to the cloud with minimal or no code changes — would lower the barrier to bulk application migrations in 2017. The trade-off is well understood: lift-and-shift gets workloads into the cloud quickly and cheaply, but organisations typically leave cloud-native efficiency gains on the table until they refactor later.

As Bartoletti put it: “In 2017, lift-and-shift migration tools will accelerate the rate of cloud migration, given their low cost for bulk application migrations.”

4. Hyperconverged Infrastructure Becomes the Private Cloud Foundation

Not every enterprise CIO was ready to move sensitive data to a third-party public cloud provider. For organisations committed to private cloud, building a capable, self-service infrastructure required advanced virtualisation, automation, standardisation and resource monitoring — capabilities that were difficult and expensive to assemble from discrete components.

Hyperconverged infrastructure (HCI) offered a compelling alternative: pre-integrated compute and storage resources in a single appliance or software-defined platform, designed to simplify private cloud deployment and reduce time to value. Forrester recommended HCI as the default foundation for private cloud builds, particularly for workloads requiring rapid, automated scale-out.

“HCI is quickly becoming the default infrastructure platform upon which to build the private portion of a hybrid cloud,” Bartoletti said.

Vendors such as Nutanix, VMware and Dell EMC drove significant HCI adoption in the years following this prediction, confirming the trend.

5. Containers Reach Every Major Cloud Platform

Containers — most prominently Docker — had been gaining developer adoption for several years by 2017. Forrester predicted that Linux containers would be available across every major public and private cloud platform by early 2017, moving from developer experimentation into production workloads at scale.

The adoption of containers brought new operational challenges, including security hardening, persistent storage, networking between containers, and production monitoring. Forrester advised organisations to carefully evaluate on-premises private Platform-as-a-Service (PaaS) options against managed public cloud development platforms before committing to a container strategy — noting that some organisations would need both.

Kubernetes, which Google open-sourced in 2014, was already emerging as the leading container orchestration system by 2017, and its subsequent dominance validated Forrester’s focus on containers as a defining trend of the era.

6. Enterprise Applications Move to Public Cloud

Perhaps the most significant signal of cloud maturity in 2017 was the willingness of large enterprises to run mission-critical business applications in the public cloud. Examples cited in the original Forrester research included:

• Dollar Shave Club running Apache Spark analytics workloads on AWS.
• Cardinal Health running Splunk on AWS.
• Multiple enterprises running SAP on AWS.

These examples represented a meaningful shift in CIO risk tolerance. Where earlier cloud adoption had focused on development environments, test workloads and commodity infrastructure, enterprises were now entrusting revenue-generating and compliance-sensitive applications to public cloud providers.

“Enterprises are turning great ideas into software and insights faster and the cloud is the best place to get quick insights out of enterprise data,” Bartoletti concluded.

Key Takeaways

Viewed from the present, Forrester’s 2017 predictions were largely accurate. Enterprise cloud adoption accelerated, multi-cloud became standard, FinOps emerged as a discipline, HCI grew rapidly, Kubernetes achieved dominance in the container space, and public cloud is now the default platform for enterprise application development. The six trends below summarise the framework:

1. Regional players complement mega-providers — Multi-cloud and regional compliance requirements drive vendor diversity.
2. Cloud cost containment — Consumption monitoring and FinOps practices become essential.
3. Lift-and-shift migration — Tooling lowers the barrier to bulk migrations even without refactoring.
4. Hyperconverged infrastructure — HCI simplifies private cloud builds within hybrid architectures.
5. Containers — Linux containers reach every major platform, introducing new operational challenges.
6. Enterprise apps on public cloud — CIOs grow comfortable running core business applications outside the data centre.

Original reporting by cio.com, based on Forrester Research analysis by Dave Bartoletti.

From Grassroots Movement to Enterprise Priority

For much of its early history, DevOps was associated with high-growth technology startups — the so-called “unicorns” — that had the freedom to build culture and tooling from scratch. Traditional enterprises, burdened with legacy systems, regulatory requirements, and large organisational structures, were seen as poor candidates for DevOps adoption.

By 2016, that assumption had been overtaken by reality. Large, complex organisations — banks, retailers, insurers, and government agencies — were not merely following the trail blazed by startups. In many cases they were advancing DevOps practices in ways that smaller companies could not, precisely because the problems they solved were harder: mainframe integration, compliance pipelines, regulated release processes, and multi-team coordination at scale.

What DOES16 Revealed About Enterprise DevOps Maturity

The DevOps Enterprise Summit has always focused on practitioners inside large organisations rather than on vendors or tools. DOES16 continued that tradition, surfacing several themes that reflected the state of enterprise DevOps in late 2016:

• Leadership alignment matters as much as tooling. Presenters consistently pointed to executive sponsorship and cross-functional leadership as prerequisites for sustainable DevOps transformation, not optional extras.
• Value stream mapping went mainstream. Enterprises were using value stream mapping to surface bottlenecks in software delivery — often discovering that the slowest parts of the pipeline had nothing to do with code and everything to do with approval gates and handoffs.
• The “us vs. them” narrative was fading. Development, operations, security, and compliance teams were increasingly framing their work around shared outcomes rather than departmental boundaries.
• Metrics moved beyond deployment frequency. While the four key metrics popularised by research such as the State of DevOps Report (deployment frequency, lead time, mean time to restore, and change failure rate) were widely referenced, mature organisations were beginning to connect delivery metrics to business outcomes.
• Scaling DevOps remained the hardest problem. Organisations that had succeeded in one team or one product line were wrestling with how to replicate that success across hundreds of teams without mandating a single prescriptive process.

Key Trends Shaping Enterprise DevOps in 2016 and Beyond

Continuous Delivery at Enterprise Scale

Continuous delivery pipelines were no longer the exclusive domain of cloud-native startups. Enterprises were investing heavily in pipeline automation, with release orchestration emerging as a distinct discipline — coordinating deployments across dozens of interdependent services, environments, and teams in a controlled and auditable way.

Cultural Transformation as a Technical Problem

One of the more striking shifts visible at DOES16 was the willingness of enterprise speakers to discuss organisational change in concrete, engineering terms: reducing batch sizes, shortening feedback loops, making work visible. Cultural change was increasingly being treated not as a soft, aspirational goal but as a system design challenge with measurable outputs.

Security and Compliance Integration

DevSecOps — integrating security practices directly into the delivery pipeline — was gaining traction. Rather than treating security review as a late-stage gate, leading enterprises were embedding automated security scanning, policy as code, and compliance checks earlier in the pipeline to catch issues without slowing delivery.

The Role of Release Orchestration

Electric Cloud, the publisher of the original article, were advocates of release orchestration as a practice distinct from simple CI/CD automation. For enterprises managing complex, multi-team deployments, orchestrating releases across heterogeneous environments — including mainframes, on-premises infrastructure, and cloud — was a genuine operational challenge that point-to-point pipeline tools did not fully address.

Why Enterprise DevOps Progress Matters

The significance of large enterprises leading DevOps innovation extends beyond any individual organisation. Enterprises operate at a scale and under constraints — regulatory, financial, reputational — that startups do not. When a major bank or a healthcare provider successfully implements continuous delivery, it demonstrates that DevOps is not a niche practice for companies that can afford to move fast and break things. It proves that disciplined, safe, and rapid software delivery is achievable inside some of the most constrained operating environments in the world.

That proof point accelerates adoption across entire industries and changes the conversation with regulators and auditors who had previously viewed agile delivery as incompatible with governance requirements.

Original Source

This article summarises themes from the Electric Cloud blog post published in November 2016: DOES16 San Francisco: The Future of DevOps in the Enterprise – Trends & Predictions. Electric Cloud (now part of CloudBees) specialised in release automation and orchestration for enterprise software delivery.