DevOps Lifecycle and Phases Explained
The devops lifecycle is a repeating loop of practices that connect how software gets planned, built, tested, deployed, and monitored. Unlike the old waterfall model where each stage happened once and moved on, the life cycle of devops is continuous. Code moves from a developer’s machine to production in hours, not months, and feedback from monitoring feeds straight back into the next planning session.
That loop is what makes DevOps different from traditional software delivery. Every phase generates data. That data improves the next iteration. The result, when done well, is faster releases, fewer production incidents, and teams that spend less time firefighting and more time building.
This guide breaks down every phase of the devops cycle, explains how the stages connect in practice, and shows where teams in regulated industries (FinTech, HealthTech) need to pay extra attention. If you need a broader overview of what DevOps actually is and where the methodology came from, start there. This article assumes you know the basics and want the operational detail.
What Is the DevOps Lifecycle?
People ask: what is devops lifecycle? In short, it is the structured sequence of phases that software passes through from initial idea to running in production, then back again. The key word is back again. Every stage feeds the next, and the last stage (monitoring) feeds the first (planning). That feedback loop is the defining feature of the lifecycle of devops.
Traditional software development follows a straight line: requirements, design, development, testing, deployment, maintenance. Each phase finishes before the next begins. The DevOps lifecycle replaced that line with a loop. Changes are small, frequent, and automated. A bug found in monitoring on Tuesday becomes a fix in production by Wednesday.
The DORA (DevOps Research and Assessment) programme, now part of Google Cloud, has tracked this for over a decade. Their 2023 report, covering more than 39,000 professionals, found that elite DevOps teams deploy on demand (multiple times per day), with lead times under 24 hours from commit to production, and change failure rates below 5%. These numbers are not theoretical. They are the measurable outcome of running the DevOps lifecycle well.
| Key takeaway: The DevOps lifecycle is a continuous loop, not a linear process. Each phase generates data that improves the next cycle. DORA research shows elite teams ship code to production multiple times daily with failure rates below 5%. |
The DevOps Lifecycle Diagram: How the Devops Cycle Connects
A devops lifecycle diagram typically shows an infinity loop (a figure-eight or lemniscate). The left side represents the Dev phases: Plan, Code, Build, Test. The right side represents the Ops phases: Release, Deploy, Operate, Monitor. The crossover point at the centre is where continuous integration meets continuous delivery. That is where the devops cycle turns.
The infinity-loop shape is not just a trendy graphic. It communicates something important: there is no endpoint. The output of Monitor (alerts, performance data, user behaviour, error rates) becomes the input of Plan. A slow API endpoint spotted in monitoring becomes a backlog item. A spike in 500 errors after deployment triggers an automated rollback and a post-mortem that shapes the next sprint.
This is fundamentally different from the traditional SDLC waterfall, where operations was something that happened after development finished. In the DevOps model, operations concerns (deployment strategy, infrastructure requirements, monitoring coverage) are part of the conversation from the planning phase.
All 8 Stages of DevOps Explained
The stages of devops vary slightly depending on the source. Some models list six, some list seven. We use eight, because separating Release from Deploy and Operate from Monitor reflects how production environments actually work, especially in regulated industries where release approval and operational runbooks are distinct activities.
Here is each of the devops phases in detail, with the tools, practices, and regulated-industry considerations that matter for each.
Phase 1: Plan
Planning in a DevOps context is not a three-month requirements phase locked in a Confluence document. It is a continuous activity: sprint planning, backlog refinement, capacity allocation, and prioritisation based on production data.
Tools: Jira, Linear, GitHub Projects, Notion, Azure DevOps Boards.
What happens: Product owners and engineering leads review monitoring data, user feedback, incident reports, and business goals. They translate these into prioritised work items. In regulated environments (FCA-authorised FinTech, HIPAA-covered HealthTech), compliance requirements and audit findings also feed into planning. A PCI-DSS vulnerability scan result becomes a sprint item, not a quarterly review footnote.
Regulated-industry note: Teams building FinTech software need to plan for regulatory change. PSD2 updates, DORA (Digital Operational Resilience Act) requirements, and FCA guidance changes must be tracked and backlogged like any other feature work.
Phase 2: Code
Developers write code in feature branches, following agreed conventions for the repository. Version control (Git) is non-negotiable. Branch strategies vary: trunk-based development for teams with strong CI pipelines, GitFlow for teams that need more release structure.
Tools: Git, GitHub, GitLab, Bitbucket. Code review via pull requests.
What happens: Developers write application code, infrastructure-as-code (Terraform, Pulumi), test code, and pipeline configuration. Everything lives in version control. Pair programming and code reviews catch issues before they reach the build phase. AI-assisted coding tools (GitHub Copilot, Cursor) accelerate output, but human review remains the quality gate.
Regulated-industry note: In HIPAA-covered applications, code that handles Protected Health Information (PHI) requires stricter review. Four-eyes principles (two reviewers for security-sensitive code) are common. Audit trails on code changes are a compliance requirement, not a nice-to-have.
Phase 3: Build
The build phase compiles source code into deployable artefacts: Docker containers, compiled binaries, packaged applications. This happens automatically every time code is pushed to the repository. That automation is the first half of CI/CD (Continuous Integration).
Tools: GitHub Actions, GitLab CI, Jenkins, CircleCI, Docker, Buildpacks.
What happens: A developer pushes code. The CI server pulls it, installs dependencies, compiles if needed, runs linters and static analysis, and produces a build artefact. If the build fails, the developer gets immediate feedback. A team practising continuous integration merges code to the main branch at least once per day, and every merge triggers a build.
Common problem: Build times that grow too long. When a build takes 20 minutes, developers stop running it frequently. Keep builds under 10 minutes. Use caching, parallelisation, and incremental builds.
Phase 4: Test
Automated testing runs at multiple levels: unit tests during build, integration tests against real dependencies, end-to-end tests that simulate user flows, and security scans (SAST, DAST) that check for vulnerabilities.
Tools: Jest, PyTest, Cypress, Playwright, Selenium, SonarQube, Snyk, OWASP ZAP.
What happens: Every code change runs through the test suite automatically. Fast tests (unit, lint) run first. If they pass, slower tests (integration, e2e) follow. Security scans check for known vulnerabilities in dependencies and common code weaknesses. Performance tests catch regressions before they reach production.
Regulated-industry note: For payment systems and health data platforms, test coverage is not optional. PCI-DSS requires regular vulnerability scanning. HIPAA demands that security controls are verified. Building these checks into the automated test phase, rather than running them quarterly, catches issues in days instead of months.
Phase 5: Release
The release phase packages a tested build artefact and marks it as ready for deployment. In some organisations, this includes a manual approval gate, especially in regulated environments where change management processes require sign-off.
Tools: Argo CD, Flux, Spinnaker, GitHub Releases, Helm (for Kubernetes).
What happens: A build that has passed all tests is tagged as a release candidate. In a continuous delivery setup, the release is available for deployment at any time. In a continuous deployment setup, the release goes to production automatically without human approval. Many FinTech teams operate with continuous delivery (not deployment) because compliance requires documented release approval.
Phase 6: Deploy
Deployment pushes the release to production infrastructure. Modern deployment strategies reduce risk: blue-green deployments run two identical environments and switch traffic between them. Canary releases push changes to a small percentage of users first. Feature flags let teams deploy code that is not yet activated. Teams managing cloud infrastructure often automate deployments through infrastructure-as-code and GitOps workflows.
Tools: Kubernetes, AWS ECS/EKS, Terraform, Pulumi, Argo CD, GitHub Actions, Docker Compose.
What happens: The release is deployed to production using the chosen strategy. Automated health checks confirm the new version is responding correctly. If checks fail, an automatic rollback restores the previous version. Deployment frequency is a key DORA metric: elite teams deploy on demand, multiple times per day.
Phase 7: Operate
Operations covers everything that keeps the application running in production: infrastructure management, scaling, incident response, security patching, and database administration. In a microservices architecture, operations complexity increases with each service, making observability and automation critical.
Tools: Kubernetes, Terraform, Ansible, PagerDuty, OpsGenie, AWS CloudWatch, Datadog.
What happens: Site reliability engineers (SREs) and operations teams manage uptime, respond to incidents, scale infrastructure to handle traffic spikes, apply security patches, and maintain compliance posture. Runbooks document standard procedures. Chaos engineering (deliberately injecting failures) tests resilience before real incidents occur.
Phase 8: Monitor
Monitoring closes the loop. It collects data from every layer of the stack (application logs, infrastructure metrics, user behaviour, error rates, latency) and feeds that data back to the planning phase. Without monitoring, the DevOps lifecycle runs blind.
Tools: Prometheus, Grafana, Datadog, New Relic, ELK Stack (Elasticsearch, Logstash, Kibana), Sentry, AWS CloudWatch.
What happens: Dashboards display real-time system health. Alerts fire when metrics cross thresholds (error rate above 1%, latency above 500ms, disk usage above 80%). Log aggregation lets engineers trace a single request across multiple services. Application performance monitoring (APM) identifies slow endpoints. User analytics reveal which features get used and which do not.
The critical connection: Monitoring data becomes planning input. A pattern of 3am memory spikes becomes a backlog item. A slow database query becomes an optimisation ticket. An uptick in failed login attempts triggers a security review. This is how the devops cycle becomes self-improving.
| Key takeaway: Each of the 8 stages generates data that feeds the next. The Plan phase consumes monitoring output. The Test phase validates code changes. The Monitor phase validates deployments. Break any link in this chain and the lifecycle stops being continuous. |
The DevOps Process Flow in Regulated Industries
Understanding the devops process flow in theory is one thing. Running it inside a FinTech or HealthTech company where compliance is non-negotiable is another.
Here is how the flow looks in practice for a team building a payment platform that must meet FCA and PSD2 requirements:
Planning includes compliance. Sprint planning reviews not just product features but open compliance items. DORA operational resilience requirements, PSD2 SCA updates, and FCA reporting changes are tracked in the same backlog as product work.
Code review is a control. Every pull request touching payment logic or personal data requires review from a second engineer. This is not bureaucracy. It is an auditable control that satisfies regulatory expectations.
Testing includes security. SAST (static analysis) and DAST (dynamic analysis) run in every pipeline. Dependency scanning catches known vulnerabilities in third-party packages before they reach production.
Release requires approval. Continuous delivery, not continuous deployment. A tested release is available for deployment, but a named individual approves the promotion to production. The approval is logged and auditable.
Monitoring includes compliance metrics. Beyond standard application metrics, dashboards track transaction anomalies, data access patterns, and system availability against SLA commitments.
This is the devops process flow adapted for reality. Not slower, just more deliberate about what gets automated and what requires human judgement. Teams that get this right ship just as fast as unregulated startups while maintaining audit readiness. Our DevOps engineers build exactly these kinds of pipelines for FinTech and HealthTech clients.
DevOps Software Development Life Cycle vs Traditional SDLC
The devops software development life cycle is not a separate model that replaces the SDLC. It is the SDLC done continuously, with automation at every stage and feedback loops that connect operations back to development.
| Dimension | Traditional SDLC | DevOps SDLC |
| Release frequency | Quarterly or monthly | Daily or on-demand |
| Testing | Manual QA phase after development | Automated at every commit |
| Deployment | Manual, high-risk, weekend window | Automated, low-risk, any time |
| Feedback speed | Weeks or months | Minutes (monitoring) to hours (user data) |
| Infrastructure | Manually provisioned servers | Infrastructure as code, auto-scaling |
| Failure response | War room, hours-long outage | Automated rollback, minutes |
| Responsibility | Dev builds, Ops runs | Shared ownership across the lifecycle |
The shift from linear SDLC to the DevOps model is not just a process change. It requires different tooling, different team structures, and different thinking about how software gets from a developer’s laptop to a user’s screen. If you are building a product through end-to-end development, the DevOps lifecycle should be designed into the architecture from sprint one, not retrofitted six months later.
How to Measure DevOps Lifecycle Performance
The DORA framework provides four metrics that every team running a DevOps lifecycle should track:
Deployment frequency. How often you push code to production. Elite teams deploy on demand. Low performers deploy between once per month and once every six months.
Lead time for changes. Time from code commit to production. Elite: less than one day. Low: between one and six months.
Change failure rate. Percentage of deployments that cause a failure (incident, rollback, hotfix). Elite: 0-5%. Low: 46-60%.
Mean time to recovery (MTTR). How quickly you restore service after a failure. Elite: less than one hour. Low: between one week and one month.
These four metrics are interconnected. Teams that deploy frequently with small changes have lower change failure rates and faster recovery times. Teams that deploy rarely with large changes have the opposite experience. The DevOps lifecycle is the operating system that drives improvement across all four.
| Key takeaway: Track the four DORA metrics: deployment frequency, lead time, change failure rate, and mean time to recovery. They measure whether your DevOps lifecycle is actually working or just theoretical. |
Frequently Asked Questions About the DevOps Lifecycle
What are the main devops phases?
The eight devops phases are: Plan, Code, Build, Test, Release, Deploy, Operate, and Monitor. Some models combine Release and Deploy or Operate and Monitor into single stages, resulting in six or seven phases. The critical point is not the exact count but the continuous loop: every phase feeds the next, and monitoring output feeds back into planning.
How does the devops lifecycle differ from Agile?
Agile focuses on how development teams organise and iterate on features (sprints, standups, retrospectives). The devops lifecycle extends this to include deployment, operations, and monitoring. Agile answers “how do we build the right thing quickly?” DevOps answers “how do we get it to users reliably and keep it running?” Most modern teams practise both: Agile for development process, DevOps for the full delivery and operations lifecycle.
How long does it take to implement a devops lifecycle?
There is no single timeline. A startup with no legacy infrastructure can adopt the full DevOps lifecycle in weeks. An enterprise migrating from waterfall may need 12 to 18 months to transform all teams. The practical approach: start with CI/CD (Build, Test, Deploy automation), then layer in infrastructure-as-code, monitoring, and operational practices. Need help getting started? Our team augmentation model can embed experienced DevOps engineers into your team to accelerate the transition.
What tools are needed for each stage of the devops cycle?
The toolchain depends on your stack, scale, and team preferences. A common 2026 stack includes: GitHub for code and CI (Actions), Terraform for infrastructure, Docker and Kubernetes for containerisation and orchestration, Argo CD for GitOps deployment, and Datadog or Grafana/Prometheus for monitoring. The tools matter less than the practices: automated testing, version-controlled infrastructure, and observability at every layer.
Is the devops lifecycle relevant for small startups?
Absolutely. Small teams benefit the most from automation because they have less capacity for manual work. A two-person team running a CI/CD pipeline with automated tests and one-click deployments can ship faster than a ten-person team doing manual builds and deployments. The DevOps lifecycle scales down as well as it scales up. For startups building APIs and integrations, even a basic pipeline covering Build, Test, and Deploy phases saves hours per week.
How does the devops lifecycle handle security?
Security is not a separate phase in the DevOps lifecycle. It is embedded across multiple stages. Code review catches security issues at the Code phase. SAST and dependency scanning run during Build and Test. Infrastructure-as-code enforces security policies at Deploy. Monitoring detects anomalies at runtime. This approach, sometimes called DevSecOps, treats security as a continuous concern rather than a pre-launch checklist.
Build Your DevOps Pipeline With Experienced Engineers
Implementing the DevOps lifecycle is not just about picking tools. It requires engineers who have built pipelines for products that handle real money, real patient data, and real regulatory scrutiny. Code & Pepper has delivered DevOps and SRE support for FinTech and HealthTech teams for 19 years. From CI/CD pipeline design to Kubernetes orchestration to cloud cost optimisation, our engineers embed into your team and start shipping from week one.