What is the DevOps lifecycle?

The DevOps process describes how software moves from ideation, through production and feedback, and back to ideation, with development and operations teams working as a single, collaborative unit. It comprises an end-to-end flow of practices and automation workflows that DevOps teams can use to plan, develop, run and optimize software releases.

The phases of the DevOps lifecycle are often depicted as part of an “infinity loop” with development activities on the left and operations activities on the right, connected in one continuous path.

On the “plan → code → build → test” side of the loop, teams refine software requirements into small user stories, commit code to code repositories incrementally (but frequently) and use continuous integration (CI) tools to automatically build and test every code change.

On the “release → deploy → operate → monitor” side, automation workflows push changes through IT environments and out to users. Meanwhile, observability tools track system health, software performance and user behavior to detect issues and measure overall impact.

The infinity loop emphasizes that each phase of the DevOps lifecycle influences the others. Teams share work, knowledge and responsibilities, instead of passing them back and forth between silos. The loop also illustrates how DevOps workflows can streamline software delivery and optimization processes. Ideally, every cycle will be faster, safer and more automated than the last.

As such, the DevOps lifecycle enables enterprises to align people, processes and tools to deliver high-quality software applications and frictionless user experiences.

Every phase of the DevOps lifecycle informs and overlaps with the other phases, but each one has a clear purpose and a set of associated activities. The phases include:

In the planning phase, DevOps teams translate business needs and user feedback into clear, prioritized, time‑bound work plans and project management workflows that guide development and operations activities in subsequent phases. This phase clarifies the vision, scope and roadmap for a software release or system build so that everyone understands what they’re building, why it matters and how they will measure success.

Typical planning activities might include:

• Refining and triaging product backlogs (such as new features, bug fixes and technical debt).

• Analyzing stakeholder input to capture user needs and constraints.

• Breaking work into tasks, user stories (software feature descriptions written from the user’s perspective) and epics (user stories too expansive to address in a single software iteration) to estimate resource requirements and identify dependencies.

• Defining key performance indicators (KPIs), service level indicators (SLIs) and service level objectives, so later phases know what “good” looks like.

During the coding phase, developers turn the planned infrastructure and software requirements into working source code with an eye toward quality assurance and future operations. It’s not just about writing new code. It’s also about setting up all the necessary components for automated build, test, integration and deployment processes later in the pipeline.

The coding process entails:

• Writing code to implement new functionality, fix defects and address non-functional needs (security, for instance).

• Committing small, incremental code changes to shared repositories within version control systems (such as Git and GitHub) for review.

• Testing code commits so they can be seamlessly built and integrated with the codebase.

• Adding or updating automated unit and component tests so that future CI runs can automatically validate code behavior after each commit.

In the building phase, source code changes are automatically compiled, validated and packaged into deployable build artifacts. DevOps tools fetch code changes from the version control system, run automated checks and produce packages (such as Docker container images and compiled binaries) that are versioned and ready for deployment or further testing.

More specifically:

• Automated scripts and build tools compile the source code, resolve and download dependencies (including libraries, frameworks and modules) and link everything into an executable or image.

• Basic automated tests run as part of the build. If the build fails, it’s marked as broken and remains in the build phase. If the build passes, it’s advanced to the next phase of the lifecycle.

The testing phase—also called validation—aims to help ensure that a code change behaves as expected in real-world conditions. DevOps teams use an array of advanced testing tools to continuously validate that each change is functional, secure and performant before it moves further down the pipeline or reaches production.

Typically, testing processes are tightly integrated into the continuous integration/continuous delivery (CI/CD) pipeline, so developers get fast, frequent feedback on code quality.

The primary objective of software testing is to identify defects as early as possible (when they’re cheaper and easier to fix) and to prevent faulty builds from progressing toward release. Testing also verifies that new changes won’t break existing functionality, helping developers maintain overall system stability as software products evolve.

The release phase provides a controlled gateway between “tested build” and “live deployment,” wherein code changes are approved for production and made visible to target users. Development teams prepare proven, tested code for deployment by packaging it, validating that it’s production-ready and coordinating how and when it will move into staging and production environments.

Common release processes include:

• Conducting final quality, security and compliance checks against predefined acceptance criteria.

• Attaching build artifacts to specific release versions and creating change logs and documentation.

• Identifying vulnerabilities in the upcoming release and planning mitigation strategies (such as rollbacks) to address issues.

During the deployment phase, tested and packaged code is automatically delivered to the target environments (staging or production). Configuration management and infrastructure as code (IaC) tools—which use configuration files and high-level descriptive coding languages to automate infrastructure provisioning and orchestration for software releases—help DevOps teams ensure that software is delivered in a reliable, repeatable way.

Deployment is typically driven by continuous delivery (CD) and containerization platforms (Kubernetes, for example) that orchestrate software deployment processes (pulling build artifacts, updating containers and binaries, running final tests and switching traffic to the new version).

DevOps teams often use different deployment strategies to make sure that new versions are introduced gradually and can be rolled back quickly, if necessary.

With blue-green deployment, for example, apps are deployed in two parallel production environments, and each environment runs a different release of the application. The “blue” environment runs the live application, while the “green” environment handles testing and validation for new app versions. When the new iteration passes, the green environment becomes the live environment, and the blue remains idle—but available—to handle rollbacks.

With canary deployments, teams deploy applications to a small subset of users—or “canaries”—for live-environment monitoring and testing before rolling the app out to the entire user base.

Much like canaries were used to warn coal miners of toxic gases, canary deployments warn development teams of app defects without risking large-scale performance issues that affect every user. If the application performs well with the canary group, developers progressively roll it out to larger groups until it’s available to all users.

The operation phase focuses on keeping live systems stable, performant, secure and available for real users under real workloads. It’s not the “end” of the application lifecycle. Rather, it feeds data and insights back into earlier stages.

In this phase, DevOps teams:

• Run and supervise applications in the production environment, including any associated infrastructure, services and dependencies.

• Manage capacity planning and scaling processes so traffic flows don’t degrade over time.

• Detect and triage issues, address performance bottlenecks and apply bug fixes or rollbacks as needed.

• Apply security patches and enforce governance practices (such as access controls and compliance audits) to optimize system security.

In the monitoring phase, teams continuously observe applications and infrastructure in production to detect issues, understand real user behavior and send insights back into development and operations.

DevOps teams collect observability data (metrics, logs and traces) and other information from apps, servers, networks and databases to track performance in real time. They set thresholds and alerts so that anomalies (such as high API latency or suspicious access patterns) trigger notifications for rapid investigation and remediation.

If monitoring tools find a software defect or usability issue, they forward the information to issue trackers and backlogs, so teams can adjust subsequent iterations.​

The infinity loop doesn’t explicitly include a “secure” phase, but DevOps pipelines often involve practices and technologies that incorporate security measures throughout the lifecycle.

That’s where DevSecOps enters the framework.

DevSecOps—short for development, security and operations—is a software development practice that shifts security protocols from the right (end) to the left (beginning) of the development lifecycle. With shift-left, developers implement security protocols (such as data encryption, input validation, role-based access controls and multifactor authentication) as they write code.

DevSecOps also incorporates “shift right” activities, extending security practices into post-deployment production environments. Shift-right practices prioritize monitoring, testing and protecting applications at runtime under real-world conditions. They complement shift-left security by creating a continuous feedback loop where security issues discovered in production inform earlier development phases.

Used together, shift-left and shift-right security enable enterprises to integrate security controls into every phase of the DevOps lifecycle. A dual, “shift-everywhere” security strategy helps DevOps teams implement both early prevention and post-deployment threat detection and response, enhancing the overall security posture and driving continuous improvement.

The DevOps lifecycle represents an evolution of more traditional software development approaches, such as waterfall and agile.

Waterfall is a development methodology that follows a linear, sequential process where each phase of the lifecycle—requirements gathering, design, implementation, testing, deployment and maintenance—must be fully completed and approved before the next one begins. It prioritizes proactive planning, detailed documentation and predictability, making it suitable for projects with stable, well-defined requirements and few anticipated changes (government- and healthcare-related projects, for example).

Waterfall offers strong process control, but development teams are siloed. Making changes after a phase is complete is costly and time-consuming. In short, waterfall struggles with agility.

Agile is an iterative development methodology that delivers small, functional increments of work through short cycles (known as “sprints”). It prioritizes adaptive planning, early delivery and continuous improvement based on stakeholder feedback, enabling development teams to quickly respond to changing requirements and conditions.

Agile methodologies can prove effective for dynamic environments, such as startups. They mitigate the linear rigidity of waterfall approaches with cross-functional teams that prioritize software functionality over comprehensive documentation. However, agile approaches focus on the software development process, to the exclusion of operations processes.  

DevOps offers a modern cultural and technical practice that integrates development and operations teams, leveraging CI/CD tools (such as GitLab) for testing, deployment and monitoring automation. Unlike agile and waterfall, DevOps practices can help enterprises accelerate software releases and build applications that stay performant and nimble in today’s highly dynamic microservices architectures and cloud-based IT environments.

The 7 C’s provide another way to conceptualize the DevOps lifecycle as the product of seven continuous processes.

Continuous development involves planning and coding software, with developers breaking the process into smaller, more manageable iterations. Developers store changes in version control systems and continuously align their code with stakeholder requirements, helping teams minimize risk and quickly adapt to changes.

Continuous integration merges code changes from multiple developers into a shared repository, automatically triggering builds after every commit. It enables DevOps teams to continuously improve their software applications, receive consistent feedback, catch and fix errors before they affect software performance and deliver higher-quality software on more predictable schedules.

Continuous testing enables developers to automatically initiate code reviews and testing protocols (such as unit tests). It uses testing tools to continuously examine integrated code for bugs, usability flaws and performance problems. Continuous testing gives developers the rapid feedback that they need to improve software without delaying the lifecycle.

Continuous delivery automates the delivery of applications and validated code base changes (updates, bug fixes and even new features) to all necessary infrastructure environments for further testing. Code builds that pass all integration tests and validation stages are packaged and delivered to code repositories, which centralize and store code packages in a deployable state.

Continuous deployment takes delivery a step further by automatically deploying every approved change to production, without human intervention. When code updates are tested, validated and approved, continuous deployment tools move the software artifact to a preproduction staging environment or to public servers and distribution platforms (such as application stores) where users can access it.

Continuous feedback requires DevOps teams to glean insights from monitoring data, user experience data and stakeholders’ input to refine the product backlog and determine the urgency of each improvement.

Continuous monitoring tracks application and infrastructure performance in real time, identifying issues proactively to maintain system reliability. Most monitoring tools visualize CI/CD pipeline data by using dashboards, which increase tech stack visibility and enable data-driven optimizations.

Automation tools handle infrastructure provisioning, resource scaling, data backups, traffic routing and system maintenance, enabling DevOps teams to continuously apply feedback to new app iterations.

Leveraging DevOps best practices can have numerous benefits for enterprises, including: