What is CI/CD?
CI/CD stands for continuous integration and continuous delivery, or continuous deployment. It is a set of automated practices that build, test, and release code changes so teams can ship software more frequently and with fewer failures. Together these stages form a CI/CD pipeline: the automated path a code change follows from a developer’s commit to running in production.
Continuous integration covers the first half of that path. Developers merge changes into a shared repository frequently, and each merge automatically triggers a build and a test run, so conflicts and regressions surface within minutes instead of at the end of a release cycle.
Continuous delivery and continuous deployment cover the second half. Both automate testing and validation in a production-like environment. The difference is what happens at the end: continuous delivery leaves the release itself as a manual decision, while continuous deployment releases every change that passes automatically.
CI/CD is a core DevOps practice, and the pipeline is usually where development, operations, and security teams meet. It is also where they need shared visibility, since a pipeline that is slow, flaky, or silently broken slows down every team that depends on it.
What is a CI/CD pipeline?
A CI/CD pipeline is an automated workflow that continuously builds, tests, and deploys new code changes. Unlike a linear waterfall model, a CI/CD pipeline is an agile and iterative approach to software development that enhances collaboration, speeds up development and testing, and maintains a high-quality codebase.
During the CI stage of the pipeline, developers send code changes to a central repository. Each submission triggers an automated build and test process. This process can be iteratively repeated until the new code merges smoothly with the existing codebase.
At the other end of the pipeline, the CD stage tests and validates code changes before releasing them into the production environment—automatically under continuous deployment, or pending a manual release decision under continuous delivery. These iterations ensure that new features, bug fixes, and improvements are delivered to users quickly and consistently.
Continuous delivery vs. continuous deployment
Continuous delivery and continuous deployment are often used interchangeably, and both are abbreviated CD. They describe the same automated pipeline up until the final step.
In continuous delivery, every change that passes automated testing is packaged and staged so that it could be released at any moment. A person decides when it actually ships. Teams choose this when releases need to line up with a marketing date, a customer commitment, a compliance review, or a change window.
In continuous deployment, there is no such gate. Any change that passes the pipeline goes to production on its own. This shortens the path from commit to customer to minutes, and it raises the stakes on test coverage and on how quickly a bad release can be detected and rolled back.
| Continuous delivery | Continuous deployment | |
|---|---|---|
| Release to production | Manual approval | Automatic |
| Typical time from commit to production | Hours to days | Minutes |
| Requires a release decision-maker | Yes | No |
| Depends on | Reliable automated testing | Reliable automated testing, plus monitoring and fast rollback |
| Common fit | Regulated environments, coordinated launches, on-prem or customer-managed software | Web services and SaaS with mature test suites |
Continuous delivery is a prerequisite for continuous deployment. A team practicing continuous deployment is doing continuous delivery and has removed the approval step.
CI/CD vs. DevOps
DevOps is the broader practice: shared ownership of software between development and operations teams, covering culture, process, and tooling across the full lifecycle. CI/CD is the automation that makes it workable day to day.
Put another way, DevOps describes how teams organize and CI/CD describes what the machinery does. You can build a CI/CD pipeline without adopting DevOps as an operating model, and plenty of organizations have. But DevOps without CI/CD tends to stall, because the handoffs it is meant to eliminate reappear as manual build, test, and release steps.
The same relationship holds for DevSecOps, where security testing, dependency scanning, and secrets management move into the pipeline rather than sitting in a pre-release review.
Why is CI/CD important?
The efficiency (or lack thereof) of a CI/CD pipeline can impact an organization’s software development and release cycles. Organizations that implement CI/CD best practices into their DevOps cycles can improve their release cycles, troubleshooting, team collaboration, and code quality, reliability, and security. Some key benefits include:
Accelerate time to market: CI/CD pipelines automate integration and deployment, shortening release cycles for new features and updates. This can help organizations respond quickly to user needs and market changes.
Shift-left testing: CI/CD integrates testing early in the software development workflow, identifying and resolving bugs sooner and at a small scale. This helps ensure end users work with a reliable software version, and it decreases the likelihood of users experiencing software issues.
Improve DevSecOps collaboration: CI/CD practices improve collaboration across developer, operations, and security teams by providing a shared, continuously tested and updated code repository. This central repository—often referred to as the “single source of truth”—ensures that all teams collaborate more effectively, identify issues earlier, and deliver more secure, reliable software faster.
Ensure deployment success: CI/CD helps ensure code is always maintained in a deployable state by delivering smaller code changes to multiple environments more frequently. Code that is continuously tested, updated, and ready for release at any time helps streamline deployment to the production environment and reduces the risk of deployment failure or security breaches.
How does a CI/CD pipeline work?
A CI/CD pipeline consists of a series of automated tasks that streamline the software-development process and ensure code changes are continuously integrated, tested, validated, and deployed.
Many of the tasks in CI/CD are automated, which helps shorten release cycles and helps ensure code reliability. The CI/CD pipeline incorporates shift-left testing and code security tasks well before deployment, specifically at the code build, testing, and staging deployment and validation stages. The key stages in a typical CI/CD pipeline include:
Source control: Developers commit their code changes to a shared repository, ensuring all team members work with the latest version of the code. The repository acts as a “single source of truth,” which helps avoid code conflicts.
Code build: Each code commit automatically triggers a build process, which compiles the code, resolves dependencies, and generates executable files or artifacts.
Testing: Automated testing covers a comprehensive list, including unit tests, integration tests, and end-to-end tests, which verify code correctness and identify code conflicts.
Staging deployment and validation: Once the code passes testing, continuous delivery automatically sends it to a staging environment that mirrors production, where the code undergoes validation such as user acceptance testing (UAT) and performance testing.
Production deployment: Validated code is automatically and continuously deployed to the production environment, giving users the latest features, bug fixes, and software improvements in the least amount of time.
Monitoring and feedback: Post-deployment monitoring provides feedback that is used to identify problems and gain opportunities for optimization.
CI/CD tools
A CI/CD pipeline is usually assembled from several tools rather than bought as one. The common categories:
Source control. Git hosted on GitHub, GitLab, or Bitbucket. This is the trigger point for the rest of the pipeline.
CI servers and runners. The service that executes builds and tests on each commit. Jenkins remains widely deployed, particularly self-hosted; GitHub Actions, GitLab CI/CD, CircleCI, Buildkite, and Azure Pipelines are the common managed options.
Artifact and container registries. Where build outputs are stored and versioned, such as Docker Hub, Amazon ECR, or JFrog Artifactory.
Deployment and release tooling. Argo CD and Flux for GitOps-style Kubernetes deployment, Spinnaker for multi-cloud release orchestration, Terraform or Pulumi for provisioning the infrastructure a release lands on.
Testing and quality. Unit and integration frameworks specific to each language, plus browser and API testing, along with static analysis and dependency scanning.
Pipeline observability. Instrumentation of the pipeline itself: build and test durations, failure rates by stage and by runner, flaky test detection, and correlation between a deployment and what happened to the service afterward.
Most teams end up with tools from several of these categories and a pipeline that spans more than one provider, which is the practical reason pipeline visibility tends to become a problem before pipeline capability does.
CI/CD Best Practices
Keep the pipeline fast. A pipeline that takes an hour stops being a feedback loop and becomes a queue. Parallelize test execution, cache dependencies between runs, and skip tests that a given change cannot affect.
Commit small and often. The value of continuous integration comes from the frequency. Large, infrequent merges reproduce exactly the integration problems the practice exists to prevent.
Fix broken builds before anything else. A main branch that is red for a day blocks every developer behind it, and teams that tolerate it start routing around the pipeline.
Treat flaky tests as failures. A test that passes and fails unpredictably trains developers to re-run rather than investigate, which eventually costs the entire suite its credibility. Quarantine flaky tests, track them, and fix or delete them.
Build once, promote the same artifact. Rebuilding per environment means the thing you tested is not the thing you shipped. Build a single versioned artifact and promote it through staging to production.
Keep secrets out of the pipeline definition. Use a secrets manager and scoped, short-lived credentials. Hardcoded credentials in pipeline config are among the most common ways a build system becomes an attack path.
Shift testing and scanning left. Dependency scanning, static analysis, and license checks belong on the commit, not in a pre-release gate.
Make every pipeline run measurable. Track duration, failure rate, and flakiness by stage, branch, and runner. Without that, pipeline slowdowns are only discovered anecdotally, when a developer complains.
Automate rollback. Especially under continuous deployment, the ability to revert quickly matters more than the ability to prevent every bad release.
Industry shifts driving CI/CD adoption
Industries of all types and sizes face increased demands for faster release cycles. This means that real-time feedback, faster iterations, and continuous improvement are mission-critical requirements. The CI/CD core goals to automate and accelerate software development and deployment align well with this trend.
As organizations shift their business models to cloud-native services, they must handle complex architectures such as microservices and containers. CI/CD is an ideal approach for optimizing dynamic cloud environments, giving DevOps teams a time-saving way to improve infrastructure scalability, deployment flexibility, and resource management.
Security risks and threats also drive adoption. They continue to grow in sophistication and the amount of damage they can cause. Automated testing and security checks can be integrated within the CI/CD pipeline to help identify potential threats early in development and before vulnerable software is deployed to the production environment. CI/CD can also help balance the need for fast development with data protection and code quality.
CI/CD use cases
Automated testing and quality assurance (QA) can integrate a suite of CI/CD pipeline tests, such as unit, integration, and end-to-end tests, which are automatically triggered whenever code is committed to the CI/CD repository. This shift-left testing approach helps detect bugs and security vulnerabilities early in the pipeline, reducing the cost and effort required to fix them. Developers receive immediate feedback on their code, allowing them to address conflicts quickly. This approach also helps ensure that code changes meet quality standards before being deployed.
Continuous deployment to production uses automated tasks to minimize the risk of human error and help shorten release cycles, enabling smooth and reliable deployments. Frequent small code changes make it possible to push software updates to users without causing service downtime or disrupting the user experience.
CI/CD Implementation challenges
Several challenges can stand in the way of successfully implementing a CI/CD pipeline, including:
Security risks: Automated deployments must be configured to eliminate security vulnerabilities, especially for tasks such as secrets management, access approvals, and compliance checks. A misconfigured pipeline could release hardcoded credentials, potentially leaving the entire development environment exposed. If the pipeline skips a compliance step, a business could get hit with massive fines for regulatory noncompliance.
Testing bottlenecks: Testing must be fast, reliable, and automated so that accurate code changes are pushed with a high frequency. Slow unit or integration tests can block pipeline progress and frustrate developers. Flaky tests can reduce developers’ trust, so they may keep running those tests until they pass or abandon automation altogether and intervene with manual testing.
Incorrectly configured code repository: The shared code repository is the lynchpin of a CI/CD pipeline; it enables developers to run automated tests, catching and resolving code conflicts as quickly as possible. A broken, corrupted, or incorrectly configured repository cannot provide a single source of truth that enables the efficiency and accuracy of the pipeline.
Technical debt: As developers commit code more frequently, legacy systems can produce a bottleneck in a CI/CD pipeline. The bottleneck can magnify the impact of a single problematic commit or other pipeline tasks by propagating additional delays downstream. Technical debt can incur additional pipeline slowdowns, such as resource contention, slow or inefficient tests, and serial execution.
Workflow complexity: Complex workflows can overload the CI/CD pipeline, creating deployment bottlenecks. For example, a complex workflow might require an excessive number of code changes. This can lead to slow code builds, which could turn into low release velocities and long delays at the far end of the pipeline. If the pipeline needs an upgrade to handle complex workflows, it can be difficult and costly to implement.
Toolchain complexity and integration: Developer, operations, and security teams use the CI/CD pipeline to deploy code consistently to different CI providers, different cloud providers, and different environments such as on-premises, Jenkins, Docker, Kubernetes, and others. Typically, these teams must use different visibility levels and non-standard tools, making it tricky to build a toolchain that seamlessly and consistently orchestrates accurate code changes across the CI/CD pipeline.
The right observability tool can provide time-saving insights into resolving these implementation challenges. The next section recommends key features to look for.
Key CI/CD observability features to look for
Organizations usually have a limited range of options when selecting a CI/CD pipeline observability tool. Therefore, it is critical to choose the right observability tool, one that helps identify and troubleshoot fragmented repositories, pipeline bottlenecks, and workflow slowdowns. Also important is finding one that provides unified and granular visibility into code integration, testing, and optimization.
An optimal observability solution helps streamline and accelerate software development:
- Automated instrumentation of pipelines and tests.
- Granular visibility into the health and performance of every build, stage, and job.
- Execution time and failure rate metrics across all CI runners and hosts.
- Correlation performance degradation metrics, including relevant commits, error messages, infrastructure metrics, and logs.
- Flaky test management that helps prevent, detect, and mitigate tests that pass and fail unpredictably optimizing test reliability and velocity.
- Impact analysis that enables automatic skipping of tests that are not relevant to a code change.
Learn more
By making use of the CI/CD pipeline’s automated features and shift-left testing, organizations can shorten their service time to market, improve software quality, reliability, and security, and deliver new features and updates to users more quickly and reliably. Learn more about CI/CD pipelines:
The right observability tools help optimize the CI/CD pipeline, maximizing its value to the organization. Learn more about the Datadog CI Pipeline Visibility tool:
