DevOps defined

Built on the core pillars of culture, collaboration, and tools, DevOps helps organizations deliver reliable high-performing applications and services efficiently and continuously. In this approach, software development (Dev) and IT operations (Ops) teams use one set of practices, workflows, and connected technologies to build speed, agility, and reliability into every phase of the development lifecycle.

Key Takeaways

• DevOps brings development and operations together to streamline software development and delivery across the entire lifecycle.

• By combining culture, processes, and tools, DevOps helps teams release software faster without sacrificing reliability or quality.

• Automation and shared workflows reduce manual handoffs, leading to more efficient and consistent software delivery.

• Feedback loops and iterative practices help teams respond quickly to changing requirements and customer needs.

• DevOps shifts the focus from siloed work to shared responsibility, improving collaboration, accountability, and overall performance.

How DevOps works

Instead of working in silos, teams collaborate throughout the software development lifecycle in a unified workflow: planning → development → testing → deployment → monitoring → improvement. They iterate on small changes, release updates more frequently, and use feedback from real-world usage to guide the next cycle of improvements.

Automation plays a central role in facilitating this speed and consistency, helping with tasks such as code testing, application deployments, infrastructure provisioning, security checks, and system monitoring. Modern DevOps teams often rely on integrated toolchains and continuous integration/continuous delivery (CI/CD) to iteratively deliver software. Reducing manual processes helps improve reliability, minimize errors, and accelerate delivery.

DevOps emphasizes shared ownership, where no team simply “hands off” work to another. Instead, teams remain accountable for the software throughout its lifecycle, fostering stronger collaboration, faster issue resolution, and higher-quality releases. Platforms like GitHub support this approach by providing integrated tools for version control, collaboration, CI/CD automation, and workflow management, allowing teams to manage the entire lifecycle in a single, connected environment.

History of DevOps

Before the 2000s, software development and IT operations typically functioned as separate disciplines, with distinct priorities, workflows, and success metrics. Development teams focused on delivering new features, while operations teams were responsible for stability and uptime—often leading to friction, slower releases, and inefficiencies.

In the mid-to-late 2000s, industry practitioners began advocating for closer alignment between these groups. A key moment in this shift came in 2009, when the first “DevOpsDays” conference was held in Ghent, Belgium, helping to formalize and popularize the DevOps movement.

Throughout the 2010s, DevOps practices gained widespread adoption, influenced by earlier movements such as Agile methodology and Lean manufacturing principles. Organizations began restructuring teams, automating infrastructure and deployments, and standardizing collaborative workflows to support more frequent and reliable releases.

Over time, the scope of DevOps expanded. By the late 2010s and beyond, many organizations incorporated security practices into development and operations processes.

DevSecOps

DevSecOps—short for development, security, and operations—incorporates security into every part of the development lifecycle. Using automated security tools, developers identify and address security vulnerabilities as they code instead of waiting for security teams to fix them after deployment.

5 DevOps practices

DevOps concepts consistently across software development and IT operations with practices that create structured ways to build and improve applications over time.

Cross-team collaboration

DevOps fosters a culture that values teamwork and open communication. Aided by integrated processes and tools, development and IT operations teams have visibility into and can discuss each other’s priorities and concerns, helping them respond quickly to changes and issues. Also, the DevOps team is guided by shared goals, with everyone taking greater accountability for lifecycle phases outside their area of expertise.

Automation

Using robust workflow orchestration and automation tools, the DevOps team can reduce operational overhead to work with greater accuracy, consistency, and efficiency, allowing them to focus on more strategic activities. Similarly, DevSecOps automates security-focused processes, helping teams efficiently protect software against vulnerabilities.

Continuous integration and continuous delivery (CI/CD)

DevOps thrives on CI/CD practices. Continuous integration involves regularly merging code changes into a shared repository where they’re automatically tested. Continuous deployment (delivery) automatically prepares code changes for release to production, allowing for frequent and reliable software updates.

Continuous improvement

DevOps emphasizes a steady, ongoing effort to refine how software is built, delivered, and maintained. Instead of waiting for major overhauls, teams make small, incremental changes based on performance data, retrospective meetings, team insights, and evolving requirements.

Feedback loops

By collecting insights from testing, monitoring, and real-world usage, teams gain visibility into how applications perform and where issues arise. This allows them to solve problems faster, make better decisions, and focus on improvements that benefit customers and other stakeholders. Over time, these feedback loops create a system that improves software quality, reliability, and delivery speed.

Benefits of DevOps

DevOps is designed to deliver significant advantages, including these top benefits:

Faster software delivery

Automation, microservices, and CI/CD help teams release updates, features, and fixes more quickly. By reducing manual handoffs and streamlining deployment processes, teams can move code from development to production with greater speed and consistency.

Improved collaboration

Shared workflows and responsibilities help development and operations teams communicate more effectively and work toward common goals. This closer alignment reduces silos, improves transparency, and supports more coordinated decision-making across the development lifecycle.

Greater reliability

Automated testing, monitoring, and deployment practices help reduce errors and improve application stability and performance. By catching issues earlier and standardizing release processes, teams can maintain more consistent and dependable systems in production.

Faster scaling

Repeatable infrastructure management, consistent environment provisioning, and scalable operations make it easier to manage complex environments and scale applications as business needs change.

Stronger security

Integrating security into development workflows helps teams identify and address risks earlier in the lifecycle. With DevSecOps, ongoing security testing and automated checks reduce vulnerabilities and improves the security of releases.

Increased efficiency

Reducing repetitive manual tasks allows teams to spend more time on innovations that align with customer needs. Streamlined workflows and CI/CD pipelines also help improve consistency and reduce operational overhead.

DevOps culture

DevOps culture is centered on shared ownership, open communication, and continuous improvement across development and operations teams. To support this way of working, organizations often need to restructure traditional team models and reduce operational silos so that development, IT operations, security, and other stakeholders can collaborate more closely throughout the software lifecycle. Transparency and shared visibility is key to keeping teams aligned on common goals. Together, these cultural shifts support faster feedback loops, greater accountability, and more reliable software delivery that meets customer and business needs.

The DevOps lifecycle

Often represented by an infinity loop, the DevOps lifecycle is continuous and iterative in nature. Even though it has no true beginning or end, the lifecycle can be characterized by the following eight phases, each supported by its own workflow and tools:

1. Plan

Define project goals, software requirements, and timelines. Careful planning establishes a solid foundation for the coding process.

2. Code

Develop the software to meet the defined requirements. This phase involves writing and reviewing code.

3. Build

Integrate the code into the existing code base. Automated build and version control tools help ensure that the updated software is ready for testing.

4. Test

Perform automated and manual testing to identify and fix defects. Continuous testing practices help ensure the software meets quality standards.

5. Release

Prepare the software for deployment. This phase involves packaging the software so it’s ready for production, assigning it a unique version number for tracking, and performing final quality assurance checks.

6. Deploy

Push the software to a live environment where it’s accessible to customers and other users.

7. Operate

Manage the software in production. Operations teams fine-tune configuration and runtime needs so the software runs efficiently.

8. Monitor

Monitor software and infrastructure to gather performance, usage, and other data.

The role of AI in DevOps

DevOps is undergoing a significant shift made possible by advancements in AI technologies, including machine learning (ML) and generative AI. Empowered with AI-powered tools, DevOps teams can add some automation at every phase of the DevOps lifecycle.

Examples of AI in DevOps include:

• Developers teams use AI coding tools to write code more efficiently and with greater accuracy.

• Developers speed software delivery by using AI to automatically run tests on new code.

• Developers get feedback from AI code reviews to help them improve the quality of their code.

• Operations teams use AI to monitor software in real time and receive alerts when specific conditions are met, helping prevent downtime.

• Security analysts ask AI to summarize software and system vulnerabilities and provide suggestions on how to address them.

Five challenges of adopting DevOps

Modernizing the entire software development lifecycle so it efficiently supports DevOps practices requires a good deal of expertise and effort. Here are five common challenges associated with making the transition:

Updating an outdated infrastructure

Older systems often lack the flexibility and compatibility required for continuous integration and delivery. These environments weren’t built for automation or rapid iteration, making it difficult to introduce DevOps practices without extensive system reengineering or parallel operations.

Developing infrastructure as code (IaC) skills

Automated, code-driven infrastructure management, or IaC, can drastically improve development speed and consistency. However, it comes with a steep learning curve. Teams must be trained not only in coding infrastructure but also in version control, test automation, and secure deployment practices. Mistakes in IaC can have wide-reaching consequences if not carefully managed.

Managing microservices

DevOps combines IaC with microservices, which use a cloud-native architecture to build each application from smaller, independent services. Managing dozens or hundreds of services demands a mature pipeline, robust monitoring, and seamless communication—areas where many organizations struggle during early stages of DevOps adoption.

Learning to prioritize people over tools

While tools are essential to DevOps success, relying too heavily on them can lead to tool sprawl and inefficiencies. It can also detract from the fundamental principles of team collaboration, transparency, and accountability. A strong DevOps culture, supported by a clear organizational structure and aligned processes, is a top priority.

Incorporating security practices

Strong security practices, including ongoing security monitoring and security testing, must be balanced with DevOps efficiency. Careful planning, close cross-team collaboration, and robust tools are key to maintaining security without sacrificing delivery speed.

To efficiently adopt a DevOps model, your organization should gradually modify its processes and pragmatically address any technology constraints. You’ll need to steadily build an effective DevOps pipeline—the combination of automation, tools, and practices supporting every phase of the DevOps lifecycle. Also, be sure to invest in upskilling teams and promote cultural change.

Best practices for adopting DevOps

Organizations across industries—including in retail, finance, and media—have successfully implemented DevOps by embracing the following best practices:

Continuous integration

Frequently merge code into a shared repository, where automated builds and tests can catch issues early.

• Success story: A multinational e-commerce company uses CI to run automated tests every time they commit code, helping ensure a high-quality codebase despite hundreds of daily changes.

Continuous delivery

Automatically push changes through the DevOps pipeline to staging or production, resulting in faster, smoother delivery.

• Success story: A software company uses CD to test new application functionality with subsets of users before broadly rolling it out, reducing the risk of large-scale failures.

Monitoring and observability

Monitor software and infrastructure throughout the development lifecycle to detect vulnerabilities and gather performance data. Real-time monitoring of logs, metrics, and traces will help you proactively uncover and address issues.

• Success story: Whenever a large retail chain launches a new online service, it monitors IT metrics (e.g., server uptime) and business metrics (e.g., click-through rates) to help assess the service’s technical performance and strategic impact.

Infrastructure as code (IaC)

Use repeatable code to provision and configure infrastructure whether on premises or in the cloud, helping teams work faster at scale.

• Success story: A hospitality company uses IaC to spin up identical environments for development, testing, and production. This makes the deployment process smoother and more consistent across its DevOps teams.

Microservices

Adopt a microservice architecture to support the efficient development, deployment, and scaling of innovative, cloud-based services.

• Success story: DevOps teams at a ride-sharing company work independently on driver-matching, trip-tracking, and other microservices, allowing each team to evolve its service without affecting other service cycles.

Tips on what to avoid when implementing DevOps:

In addition to adhering to best practices for implementing DevOps, consider these recommendations:

1. Don’t dive into automation without fixing broken processes first—you’ll just automate chaos.

2. Don’t neglect collaboration—DevOps is about people working together.

3. Don’t force a one-size-fits-all toolset across teams—what works for one team might not work for another.

4. Don’t skimp on project and process documentation—it’s critical to team efficiency and collaboration.

Instead, do set clear goals, proceed thoughtfully, and stay open to learning new things.

DevOps tools

A broad set of DevOps tools support automation, collaboration, and continuous delivery across the software development lifecycle. Organizations typically combine multiple tool categories into a DevOps toolchain. Major cloud providers offer managed DevOps services and integrations for organizations that need support.

Version control and collaboration tools

Version control systems help teams manage and track changes to source code while allowing multiple developers to work on the same codebase. Collaboration features such as branching, pull requests, and code reviews support shared development and integration across teams.

Examples include Git, GitHub, GitLab, and Bitbucket.

CI/CD tools

CI/CD tools automate the process of building, testing, and deploying applications. They help teams integrate code changes frequently, run automated tests, and deliver updates to production environments more quickly and reliably.

Examples include Jenkins, GitHub Actions, GitLab CI/CD, Azure Pipelines, and CircleCI.

IaC tools

IaC tools allow teams to define and manage infrastructure using code rather than manual configuration. This makes it easier to provision, update, and scale environments consistently across development, testing, and production.

Examples include Terraform, AWS CloudFormation, Pulumi, and Ansible (for infrastructure provisioning use cases).

Configuration management tools

Configuration management tools automate the setup and maintenance of servers and system configurations. They help ensure systems remain consistent, compliant, and correctly configured as environments scale or change.

Examples include Ansible, Puppet, Chef, and SaltStack.

Containerization and orchestration tools

Containerization tools package applications and their dependencies into portable units, while orchestration tools manage and scale those containers across environments. Together, they improve portability, scalability, and resource efficiency.

Examples include Docker, Kubernetes, OpenShift, and containers.

Monitoring and observability tools

Monitoring tools provide real-time visibility into application and infrastructure performance. They help teams detect issues, analyze system behavior, and maintain reliability through metrics, logs, and alerts.

Examples include Prometheus, Grafana, Datadog, New Relic, and Splunk.

DevSecOps tools

Security tools incorporatetegrate automated checks into the development pipeline to identify vulnerabilities early. They help teams enforce security policies, scan code and dependencies, and reduce risk throughout the lifecycle.

Examples include Snyk, SonarQube, Checkmarx, Aqua Security, and HashiCorp Vault.

Collaboration and planning tools

These tools support project tracking, communication, and workflow management across Dev and Ops teams. They help align priorities, improve transparency, and keep development work organized across distributed teams.

Examples include Jira, Confluence, Trello, Asana, and Slack.

Measuring DevOps success with DORA metrics

To justify investments, business and technical leaders need a quantitative framework for benchmarking DevOps maturity. Many use a model developed through research by the DevOps Research and Assessment (DORA) team.

The DORA model is an industry-standard framework for measuring DevOps performance and maturity that focuses focuses on five primary metrics:

Deployment frequency

Deployment frequency measures how often teams successfully release code to production. Higher deployment frequency often reflects efficient development workflows, strong automation practices, and the ability to deliver updates continuously.

Lead time for changes

Lead time for changes tracks the amount of time from code commit to successful production deployment. Shorter lead times can indicate streamlined processes, faster testing cycles, and fewer bottlenecks.

Change failure rate

Change failure rate measures the percentage of deployments that result in failures, service disruptions, or the need for remediation. Lower failure rates suggest stronger testing, improved quality assurance, and more reliable release practices.

Mean time to recovery (MTTR)

Mean time to recovery (MTTR) measures how quickly teams can restore services after an incident or failure occurs. Faster recovery times reflect effective monitoring, incident response processes, and operational resilience.

Reliability

Reliability measures the consistency and stability of systems in meeting performance expectations and service-level goals. Higher reliability reflects resilient architectures, effective monitoring, and the ability to maintain dependable service even under changing conditions.

Together, these metrics provide a balanced view of both speed and stability. Rather than focusing only on release velocity, the DORA model helps organizations evaluate whether teams are delivering software quickly while maintaining reliability, performance, and user trust.

Key trends in DevOps

As software environments become more distributed and complex, DevOps continues to evolve beyond its original focus on development and operations collaboration. Some of the key trends include:

AIOps and autonomous operations

AIOps and autonomous operations. AI-assisted systems will move beyond supporting developers toward autonomously managing routine operational tasks such as infrastructure optimization, incident remediation, performance tuning, and resource scaling.

Platform engineering

Platform engineering is evolving toward highly standardized internal developer portals that abstract infrastructure complexity from developers. This will make software delivery more self-service, scalable, and consistent across large organizations.

Predictive and self-healing systems

Future DevOps environments will rely more heavily on predictive analytics and automated remediation. Instead of reacting to outages after they occur, systems will identify performance anomalies, forecast failures, and resolve issues automatically before users are affected.

Policy-driven automation and governance

As environments grow more complex, organizations will adopt more automated governance models. Policies related to security, compliance, infrastructure configuration, and cost management will be enforced across development pipelines and cloud environments.

Intelligent observability and monitoring

Observability platforms will become more contextual and proactive, using AI-assisted analysis to surface meaningful insights, reduce alert fatigue, and connect technical issues directly to business impact.

GitHub solutions

By embracing DevOps practices and tools, your organization can improve its operational efficiency while building more secure, high-quality software at scale.

GitHub offers a range of DevOps products, to help your organization build a complete DevOps toolchain, including:

• Git, an open-source version control system that supports parallel development, seamless collaboration, and accurate tracking of changes across teams.

• GitHub, a unified DevOps platform that boosts developer productivity, innovation, and satisfaction with AI-powered tools. Start a free trial.

• GitHub Actions, a comprehensive CI/CD platform that allows developers to automate, customize, and complete workflows while in their repositories.

• GitHub Copilot, an AI coding assistant that helps developers write code faster and with less effort.

• GitHub Advanced Security, a set of powerful security tools integrated into GitHub that automates security practices through every phase of the lifecycle, including scanning code as it’s created and suggesting fixes.