How to Set Up CI/CD Pipelines in AWS CodePipeline

Understanding the Critical Role of CI/CD in Modern Software Development

In today's fast-paced digital landscape, the ability to deliver software quickly, reliably, and consistently has become a fundamental competitive advantage. Organizations that can push updates, fixes, and new features to production multiple times per day are outpacing those still relying on manual deployment processes. The difference between success and stagnation often comes down to how efficiently teams can move code from development to production. This efficiency isn't just about speed—it's about maintaining quality, reducing risk, and empowering developers to focus on creating value rather than managing infrastructure.

Continuous Integration and Continuous Deployment (CI/CD) represents a methodology where code changes are automatically built, tested, and deployed through a series of stages. AWS CodePipeline serves as Amazon's fully managed continuous delivery service that orchestrates these workflows, connecting various tools and services to create an automated release process. It eliminates manual steps, reduces human error, and provides visibility into every stage of your software release cycle.

Throughout this comprehensive guide, you'll discover how to architect and implement robust CI/CD pipelines using AWS CodePipeline. We'll explore the foundational concepts, walk through detailed setup procedures, examine integration patterns with other AWS services, and uncover best practices that professional teams use to maintain high-performing deployment systems. Whether you're migrating from traditional deployment methods or optimizing existing pipelines, you'll gain practical knowledge to transform your software delivery process.

Core Components and Architecture of AWS CodePipeline

AWS CodePipeline operates through a series of interconnected components that work together to automate your release process. Understanding these building blocks is essential before diving into implementation. At its foundation, a pipeline consists of stages, which represent logical divisions in your workflow such as source, build, test, and deploy. Each stage contains one or more actions, which are the individual tasks performed on your artifacts.

The pipeline begins with a source stage, where your code repository is monitored for changes. AWS CodePipeline integrates seamlessly with AWS CodeCommit, GitHub, GitHub Enterprise Server, and Bitbucket. When a change is detected—such as a commit to a specific branch—the pipeline automatically triggers, pulling the latest code into the workflow. This source artifact then flows through subsequent stages, being transformed and validated at each step.

Artifacts represent the files that move between stages. These might include source code, compiled binaries, configuration files, or deployment packages. CodePipeline stores these artifacts in Amazon S3 buckets, ensuring they're available throughout the pipeline execution. Each action can consume artifacts from previous actions and produce new artifacts for downstream consumption.

Pipeline Execution Flow and State Management

When a pipeline executes, it processes each stage sequentially by default, though you can configure parallel actions within a single stage. Each execution receives a unique execution ID, allowing you to track specific changes through your entire delivery process. The pipeline maintains state information, showing whether each stage succeeded, failed, or is in progress. This transparency enables teams to quickly identify and respond to issues.

"The transition from manual deployments to automated pipelines reduced our release cycle from weeks to hours, but more importantly, it gave our team confidence to deploy frequently without fear."

Transitions between stages can be configured with approval actions, requiring manual intervention before proceeding. This is particularly useful for production deployments where human oversight adds an additional safety layer. Approval actions can send notifications through Amazon SNS, alerting designated team members that their review is required.

Prerequisites and Initial AWS Environment Setup

Before creating your first pipeline, you'll need to ensure your AWS environment is properly configured with the necessary permissions and resources. Start by verifying you have appropriate IAM permissions to create and manage CodePipeline resources, along with permissions for any integrated services you'll use such as CodeBuild, CodeDeploy, or ECS.

Create a dedicated IAM service role for CodePipeline. This role allows CodePipeline to interact with other AWS services on your behalf. The role should include policies that grant permissions to access your source repository, trigger builds, execute deployments, and manage artifacts in S3. AWS provides managed policies like AWSCodePipelineFullAccess for administrative access, but production environments should follow the principle of least privilege with custom policies tailored to specific needs.

Setting Up Your Artifact Store

Every pipeline requires an S3 bucket to store artifacts. Create a bucket in the same region where you'll run your pipeline to minimize latency and data transfer costs. Enable versioning on this bucket to maintain historical artifacts, which proves invaluable when troubleshooting or rolling back deployments. Configure server-side encryption using either S3-managed keys (SSE-S3) or AWS KMS keys (SSE-KMS) for enhanced security.

For organizations with compliance requirements, consider implementing bucket policies that enforce encryption in transit, restrict access to specific IAM roles, and enable access logging. You might also configure lifecycle policies to automatically transition older artifacts to cheaper storage classes or delete them after a retention period.

Creating Your First Pipeline Through the AWS Console

The AWS Management Console provides an intuitive wizard that guides you through pipeline creation. Navigate to the CodePipeline service and click "Create pipeline." You'll begin by specifying basic pipeline settings including a unique name and the service role. If you haven't created a service role yet, the wizard can generate one automatically with appropriate permissions.

In the source stage configuration, select your source provider—let's use GitHub as an example. You'll need to authenticate with GitHub, authorizing AWS CodePipeline to access your repositories. Once connected, select the specific repository and branch you want to monitor. Configure the detection method: you can use GitHub webhooks for immediate triggering when code is pushed, or CloudWatch Events for scheduled polling.

Configuring the Build Stage

The build stage typically uses AWS CodeBuild, though you can integrate with Jenkins or other build providers. When selecting CodeBuild, you can either choose an existing build project or create a new one directly from the pipeline wizard. A CodeBuild project defines the build environment, including the operating system, runtime, compute resources, and build commands.

Create a buildspec.yml file in your repository root to define build instructions. This YAML file specifies phases like install, pre_build, build, and post_build, along with the commands to execute in each phase. Here's what a comprehensive buildspec structure includes:

• Version declaration specifying the buildspec syntax version
• Environment variables for configuration values and secrets
• Phases section defining commands for each build stage
• Artifacts section specifying which files to package and pass to subsequent stages
• Cache configuration to speed up builds by preserving dependencies

"Investing time in optimizing your build process pays dividends exponentially. A build that takes 15 minutes versus 3 minutes means the difference between deploying 32 times per day versus 160 times."

Adding Deployment Stages

The deployment stage is where your built application reaches its destination environment. AWS offers multiple deployment options depending on your infrastructure. For applications running on EC2 instances, use AWS CodeDeploy, which supports blue/green deployments and rolling updates. For containerized applications, deploy directly to Amazon ECS or EKS. Serverless applications can deploy to AWS Lambda through CloudFormation or SAM templates.

When configuring CodeDeploy as your deployment provider, specify the application name and deployment group. The deployment group defines which instances or resources receive the deployment and how the deployment should proceed. You'll reference an appspec.yml file in your repository that describes the deployment process, including file locations, permission settings, and lifecycle event hooks.

Advanced Pipeline Configuration with AWS CLI and CloudFormation

While the console works well for initial setup, infrastructure as code approaches using AWS CLI or CloudFormation provide repeatability, version control, and the ability to manage pipelines across multiple environments. The AWS CLI allows you to create and modify pipelines through command-line scripts, useful for automation and integration with existing tooling.

To create a pipeline via CLI, you'll define a JSON structure containing your pipeline configuration and pass it to the create-pipeline command. This JSON includes all the same information you'd provide through the console—stages, actions, artifact stores, and service roles—but in a declarative format that can be versioned in Git alongside your application code.

CloudFormation Templates for Pipeline Infrastructure

CloudFormation takes infrastructure as code further by managing not just the pipeline but all related resources as a cohesive stack. A CloudFormation template can define your pipeline, CodeBuild projects, CodeDeploy applications, IAM roles, S3 buckets, and any other resources your CI/CD process requires. This approach ensures consistency across environments and simplifies disaster recovery.

Within your CloudFormation template, use parameters to make your pipeline configuration flexible across environments. For example, parameterize repository names, branch names, deployment targets, and notification endpoints. This allows you to use the same template to create development, staging, and production pipelines with environment-specific values.

Integrating Testing Stages for Quality Assurance

A robust CI/CD pipeline includes multiple testing stages to catch issues before they reach production. After your build stage, add a test stage that runs your automated test suite. This might include unit tests, integration tests, security scans, and code quality checks. AWS CodeBuild can execute these tests, or you can integrate third-party testing tools.

Structure your testing in layers, with faster tests running first. Unit tests that complete in seconds should execute before integration tests that might take minutes. This fail-fast approach provides rapid feedback when issues are introduced. Configure your pipeline to halt execution if any test stage fails, preventing problematic code from advancing toward production.

Security Scanning and Compliance Checks

Modern pipelines incorporate security as a fundamental component rather than an afterthought. Add actions that perform static application security testing (SAST), scanning your code for vulnerabilities before deployment. Tools like Amazon CodeGuru Reviewer can automatically analyze code for security issues and provide recommendations. For container-based applications, integrate Amazon ECR image scanning to detect vulnerabilities in your container images.

"Security integrated into the pipeline isn't a bottleneck—it's a safety net that catches issues when they're cheapest to fix, during development rather than in production."

Compliance checks ensure your deployments meet organizational and regulatory requirements. You might verify that all changes have associated ticket numbers, that code has been reviewed, or that specific approval workflows have been followed. AWS Lambda functions can implement custom validation logic, integrated as pipeline actions that pass or fail based on your criteria.

Implementing Multi-Environment Deployment Strategies

Production-grade pipelines typically deploy through multiple environments—development, staging, and production at minimum. Each environment serves a specific purpose in validating your application before it reaches end users. Your pipeline should automatically deploy to development and staging environments, but require manual approval before deploying to production.

Add approval actions between your staging and production deployment stages. Configure these approvals to send notifications to specific individuals or teams through Amazon SNS. The notification can include links to review the changes, test results, and any other contextual information decision-makers need. Once approved, the pipeline automatically proceeds with the production deployment.

Blue/Green and Canary Deployment Patterns

Advanced deployment strategies minimize risk and downtime. Blue/green deployments maintain two identical production environments. When deploying, the new version goes to the inactive environment, which is thoroughly tested before traffic is switched over. If issues arise, you can instantly roll back by redirecting traffic to the previous environment. AWS CodeDeploy natively supports blue/green deployments for EC2, Lambda, and ECS applications.

Canary deployments gradually roll out changes to a small subset of users before full deployment. You might deploy to 10% of your infrastructure, monitor metrics and error rates, then automatically proceed with full deployment if everything looks healthy. This pattern catches issues that only manifest under real production load, limiting the blast radius if problems occur.

• 🎯 Rolling deployments update instances incrementally, maintaining service availability
• 🎯 Feature flags allow deploying code without activating features, decoupling deployment from release
• 🎯 Immutable deployments replace entire infrastructure rather than updating in place
• 🎯 Traffic splitting gradually shifts load from old to new versions based on metrics
• 🎯 Automated rollback reverts deployments when CloudWatch alarms trigger

Monitoring, Logging, and Troubleshooting Pipeline Executions

Visibility into pipeline executions is crucial for maintaining reliable deployments. AWS CodePipeline integrates with CloudWatch, providing metrics on pipeline execution success rates, duration, and failure points. Create CloudWatch dashboards that display pipeline health across all your projects, enabling teams to spot trends and identify problematic pipelines.

Each pipeline execution generates detailed logs accessible through the CodePipeline console. These logs show when each action started, how long it took, and whether it succeeded or failed. For actions using CodeBuild, you can drill down into build logs to see command output, error messages, and environment details. This granular logging accelerates troubleshooting when issues occur.

Setting Up Alerts and Notifications

Configure CloudWatch Events rules to trigger notifications when pipelines fail, require approval, or complete successfully. These events can publish to SNS topics, which can then fan out to email, SMS, Slack, or custom webhooks. Targeted notifications ensure the right people are informed at the right time without overwhelming teams with noise.

"The best pipeline is one you never think about because it just works. But when something does break, comprehensive logging and alerting mean you're fixing it in minutes rather than discovering it hours later."

For deeper analysis, integrate with AWS X-Ray to trace requests through your entire application stack. This is particularly valuable for microservices architectures where a deployment might affect multiple interconnected services. X-Ray helps identify performance bottlenecks and understand the impact of changes across your system.

Optimizing Pipeline Performance and Cost

As your pipelines mature, optimization becomes important for both speed and cost. Build times directly impact developer productivity—faster builds mean quicker feedback and more iterations. Start by analyzing your build logs to identify slow steps. Common optimizations include caching dependencies, using smaller Docker base images, and parallelizing independent tasks.

AWS CodeBuild supports caching, allowing you to preserve dependencies between builds. Configure cache paths in your buildspec to store items like npm packages, Maven dependencies, or Docker layers. On subsequent builds, these cached items are retrieved from S3, dramatically reducing build times for projects with large dependency trees.

Cost Management Strategies

CodePipeline charges per active pipeline per month, with no charges for inactive pipelines. Optimize costs by consolidating related workflows into single pipelines with multiple stages rather than creating separate pipelines for each environment. Use CodeBuild's compute type options strategically—smaller instances for simple builds, larger instances only when needed for resource-intensive operations.

Implement lifecycle policies on your artifact S3 bucket to automatically delete old artifacts. Retaining every build artifact indefinitely consumes storage and incurs costs. Determine an appropriate retention period based on your compliance requirements and debugging needs, then configure S3 lifecycle rules to automatically clean up older artifacts.

Security Best Practices for CI/CD Pipelines

Security in CI/CD extends beyond scanning code for vulnerabilities. It encompasses protecting your pipeline infrastructure, managing secrets, controlling access, and ensuring auditability. Start with IAM roles and policies that follow least privilege principles. Each component of your pipeline—the pipeline itself, build projects, deployment applications—should have only the permissions necessary for its specific function.

Never hardcode secrets in your pipeline configuration, buildspec files, or application code. Instead, use AWS Secrets Manager or Systems Manager Parameter Store to securely store sensitive values like database passwords, API keys, and certificates. Your build and deployment processes can retrieve these secrets at runtime, ensuring they're never exposed in logs or version control.

Audit Logging and Compliance

Enable AWS CloudTrail logging for all CodePipeline API calls. CloudTrail provides a complete audit trail of who did what and when, essential for security investigations and compliance reporting. Configure CloudTrail to deliver logs to a dedicated S3 bucket with strict access controls, and consider forwarding logs to CloudWatch Logs for real-time monitoring and alerting.

"A security breach through a compromised pipeline can be more devastating than a direct application vulnerability because it gives attackers access to deploy malicious code directly to production."

Implement pipeline policies that enforce security requirements. For example, require that all deployments to production come from specific branches, that code has passed security scans, and that appropriate approvals have been obtained. Use AWS Lambda functions as custom action providers to implement complex validation logic that enforces your organization's policies.

Cross-Account and Cross-Region Pipeline Architectures

Enterprise organizations often maintain separate AWS accounts for different environments or business units. A cross-account pipeline architecture allows a centralized deployment account to manage pipelines while deploying to resources in separate accounts. This approach provides centralized governance while maintaining account isolation for security and cost allocation.

Implementing cross-account pipelines requires configuring IAM roles and policies that allow the pipeline account to assume roles in target accounts. Create a role in each target account that grants the pipeline permissions to deploy resources. In your pipeline account, configure your deployment actions to assume these cross-account roles when executing.

Multi-Region Deployment Considerations

For applications serving global users, you might need to deploy across multiple AWS regions. CodePipeline supports cross-region actions, allowing a single pipeline to deploy to resources in different regions. However, artifacts must be replicated to S3 buckets in each target region, as CodePipeline requires artifacts to be in the same region as the actions that consume them.

Design your multi-region architecture carefully, considering data residency requirements, latency implications, and disaster recovery needs. You might create region-specific pipelines that deploy from a centralized source, or implement a hub-and-spoke model where a primary pipeline orchestrates regional deployments through secondary pipelines.

Integrating Third-Party Tools and Custom Actions

While AWS provides comprehensive CI/CD services, you may want to integrate existing tools or implement custom logic. CodePipeline supports custom actions, allowing you to extend pipeline functionality beyond built-in actions. Custom actions can be implemented as Lambda functions for lightweight logic or as job workers for long-running processes.

Popular integrations include Jenkins for build orchestration, JFrog Artifactory for artifact management, and HashiCorp Terraform for infrastructure provisioning. These tools can be integrated as custom action providers or invoked through Lambda functions. The key is ensuring proper authentication and passing artifacts correctly between CodePipeline and your external tools.

Building Custom Action Providers

Creating a custom action provider involves defining the action type, implementing a job worker that polls for jobs, and processing those jobs when they arrive. The job worker retrieves input artifacts from S3, performs its operation, uploads output artifacts back to S3, and reports success or failure to CodePipeline. While more complex than using built-in actions, custom providers enable integration with proprietary tools and implementation of organization-specific logic.

Pipeline as Code and Version Control Strategies

Treating your pipeline configuration as code provides the same benefits as application code—version control, code review, testing, and automated deployment. Store your pipeline definitions in Git alongside your application code, or in a dedicated infrastructure repository. This approach creates a complete audit trail of pipeline changes and allows you to roll back problematic modifications.

Consider implementing a "pipeline for your pipeline"—a meta-pipeline that automatically updates your CI/CD infrastructure when changes are committed to your pipeline configuration repository. This self-service approach empowers development teams to modify their own pipelines while maintaining governance through code review and automated validation.

Templating and Reusability

As your organization builds multiple pipelines, patterns emerge that can be abstracted into reusable templates. CloudFormation nested stacks allow you to create modular pipeline components that can be composed into complete pipelines. You might create templates for common patterns like "web application pipeline" or "microservice pipeline," parameterized to accommodate project-specific variations.

The AWS CDK (Cloud Development Kit) takes reusability further by allowing you to define pipeline infrastructure using programming languages like TypeScript, Python, or Java. CDK constructs can encapsulate best practices and organizational standards, ensuring consistency across all pipelines while still allowing customization where needed.

Handling Pipeline Failures and Implementing Rollback Strategies

Despite best efforts, pipeline failures occur. How you handle these failures determines whether they're minor inconveniences or major incidents. Configure your pipeline with appropriate retry logic for transient failures—network hiccups, temporary resource unavailability, or race conditions that might resolve on subsequent attempts.

For persistent failures, your pipeline should halt rather than proceeding with potentially broken deployments. Configure notifications that alert the responsible team immediately when failures occur. Include contextual information in these notifications—what failed, what was being deployed, and links to relevant logs and metrics.

Automated and Manual Rollback Procedures

Implement automated rollback mechanisms that revert deployments when issues are detected. CloudWatch alarms can trigger automatic rollbacks based on metrics like error rates, latency, or custom business metrics. For CodeDeploy deployments, configure automatic rollback when deployment fails or when alarms trigger.

Maintain the ability to manually rollback when automated mechanisms aren't sufficient. This might involve re-running a pipeline with a previous commit, deploying a specific artifact version, or executing emergency runbooks. Document and regularly test your rollback procedures to ensure they work when needed under pressure.

Advanced Testing Strategies Within Pipelines

Beyond basic unit and integration tests, mature pipelines incorporate sophisticated testing strategies. Smoke tests run immediately after deployment to verify basic functionality. These lightweight tests confirm that the application started correctly, critical endpoints respond, and essential services are reachable. If smoke tests fail, the deployment can be immediately rolled back before users are affected.

Performance testing validates that your application meets latency and throughput requirements. Integrate tools like Apache JMeter or Gatling into your pipeline to run load tests against staging environments. Compare results against baseline metrics to detect performance regressions before they reach production. This is particularly important for applications with strict performance SLAs.

Contract Testing for Microservices

In microservices architectures, contract testing ensures that services can communicate correctly even when developed by different teams. Tools like Pact allow services to define contracts specifying how they interact with dependencies. Your pipeline can verify that services honor these contracts, catching integration issues early without requiring full end-to-end test environments.

Chaos engineering can also be integrated into pipelines for critical services. Automatically inject failures into staging environments to verify that your application handles degraded conditions gracefully. This proactive approach identifies resilience gaps before they cause production incidents.

Scaling Pipeline Infrastructure for Growing Organizations

As organizations grow, pipeline management becomes more complex. You might have dozens or hundreds of pipelines across multiple teams and projects. Implement governance structures that maintain consistency without stifling innovation. This might include approved pipeline templates, automated policy validation, and centralized monitoring dashboards.

Create a center of excellence or platform team responsible for pipeline infrastructure and best practices. This team can develop shared libraries, maintain common tooling, and provide consultation to product teams. They ensure that organizational standards are met while allowing teams autonomy in their specific implementations.

Self-Service Pipeline Creation

Enable development teams to create and manage their own pipelines through self-service tooling. Provide templates and automation that allow teams to provision complete CI/CD infrastructure with a few commands or through a web interface. Include guardrails that enforce security and compliance requirements while giving teams flexibility in their workflow specifics.

Implement resource tagging standards that allow you to track pipeline costs and usage across teams and projects. Use AWS Cost Explorer and custom reports to understand your CI/CD spending and identify optimization opportunities. This visibility helps justify infrastructure investments and identify teams that might benefit from additional support or optimization.

Continuous Improvement and Pipeline Evolution

Your CI/CD pipeline should evolve as your organization and applications mature. Regularly review pipeline metrics—build times, deployment frequency, failure rates, and time to recovery. Set objectives for improvement and systematically address bottlenecks and pain points. Small incremental improvements compound over time into significant productivity gains.

Conduct periodic pipeline retrospectives with your team. What's working well? What's frustrating? What manual steps remain that could be automated? Where do failures most commonly occur? This feedback drives continuous improvement and helps prioritize optimization efforts.

"The goal isn't to build the perfect pipeline on day one. It's to build a pipeline that's good enough to start, then continuously improve it based on real usage and feedback."

Stay informed about new AWS features and services that might benefit your pipelines. AWS regularly releases improvements to CodePipeline, CodeBuild, and related services. Evaluate new capabilities against your current pain points—they might provide easier solutions to problems you've been working around.

Documentation and Knowledge Sharing

Comprehensive documentation is essential for maintaining and scaling your CI/CD infrastructure. Document your pipeline architecture, including diagrams showing how stages connect, what each action does, and how artifacts flow through the system. Explain the reasoning behind architectural decisions so future maintainers understand not just what the pipeline does but why it's designed that way.

Create runbooks for common operations—how to deploy manually if needed, how to investigate failures, how to roll back deployments, and how to modify pipeline configuration. These runbooks are invaluable during incidents when stress levels are high and memory might fail. Keep them updated as processes evolve.

Training and Onboarding

Invest in training for team members who will interact with your pipelines. This includes not just DevOps engineers but also developers who need to understand how their code moves through the deployment process. Well-trained teams can troubleshoot issues independently and contribute improvements to shared infrastructure.

When onboarding new team members, include pipeline architecture as part of their orientation. Understanding the deployment process helps developers write better code and empowers them to participate in improving the development workflow. Consider creating sandbox environments where team members can experiment with pipeline configurations without affecting production systems.

Compliance and Regulatory Considerations

Organizations in regulated industries face additional requirements for their CI/CD processes. Your pipelines might need to demonstrate separation of duties, maintain detailed audit logs, or implement specific approval workflows. AWS provides capabilities to meet these requirements while maintaining automation benefits.

Implement approval gates that require different individuals to approve different stages. For example, a developer might approve promotion from development to staging, while a change manager must approve production deployments. Use IAM policies to enforce these separation requirements technically, not just procedurally.

Audit Trails and Reporting

Maintain comprehensive audit trails that document what was deployed, when, by whom, and through what process. CloudTrail provides API-level logging, but you might need additional reporting that correlates pipeline executions with specific code changes, test results, and approvals. Consider implementing a compliance dashboard that provides real-time visibility into your deployment processes for auditors and management.

For organizations requiring immutable audit logs, configure CloudTrail to deliver logs to a separate audit account with restricted access. Enable S3 Object Lock to prevent logs from being modified or deleted, ensuring your audit trail remains trustworthy even if other accounts are compromised.

Future Trends and Emerging Practices

The CI/CD landscape continues evolving with new practices and technologies. GitOps, where Git serves as the single source of truth for both application and infrastructure configuration, is gaining adoption. Progressive delivery, which combines deployment automation with feature management and experimentation, enables more sophisticated release strategies. Machine learning is beginning to be applied to predict pipeline failures, optimize build times, and automatically remediate common issues.

Stay engaged with the DevOps community through conferences, blogs, and open-source projects. Many innovations in CI/CD emerge from practitioners sharing what works in their environments. Experiment with new approaches in non-critical systems before rolling them out broadly. The field moves quickly, and continuous learning is essential for maintaining competitive deployment capabilities.

What is the difference between CodePipeline and CodeDeploy?

CodePipeline is an orchestration service that coordinates your entire continuous delivery workflow, connecting source repositories, build systems, test frameworks, and deployment tools into an automated pipeline. CodeDeploy is specifically a deployment service that handles the mechanics of deploying applications to EC2 instances, Lambda functions, or ECS services. CodePipeline typically uses CodeDeploy as one stage within a larger pipeline, but they serve different purposes—orchestration versus deployment execution.

How much does AWS CodePipeline cost?

AWS CodePipeline charges $1 per active pipeline per month. An active pipeline is one that has existed for more than 30 days and has at least one code change executed through it during the month. New pipelines are free for the first 30 days. There are no charges for inactive pipelines. Additional costs may apply for services CodePipeline integrates with, such as CodeBuild compute time, S3 storage for artifacts, and data transfer.

Can I use CodePipeline with non-AWS tools?

Yes, CodePipeline integrates with many third-party tools through custom actions. You can use GitHub, GitHub Enterprise, or Bitbucket as source repositories. Jenkins can serve as a build or test provider. You can integrate virtually any tool by creating custom action providers that use Lambda functions or job workers to interact with external systems. This flexibility allows you to adopt CodePipeline incrementally without abandoning existing tooling investments.

How do I handle secrets and sensitive data in my pipeline?

Store secrets in AWS Secrets Manager or Systems Manager Parameter Store, never in pipeline configuration or code. Reference these secrets in your buildspec or deployment configurations using environment variables. CodeBuild can automatically retrieve secrets and inject them as environment variables during build execution. Use IAM roles to control which pipelines and build projects can access which secrets, following the principle of least privilege.

What happens if my pipeline fails partway through?

When a pipeline stage fails, execution stops at that point. Subsequent stages do not execute, preventing problematic changes from advancing toward production. The pipeline shows which stage failed and provides access to logs for troubleshooting. You can retry the failed stage once you've addressed the issue, or you can release a new change that flows through the entire pipeline from the beginning. Failed executions remain visible in the pipeline history for analysis and audit purposes.

How can I deploy to multiple AWS accounts from a single pipeline?

Configure cross-account deployment by creating IAM roles in each target account that trust your pipeline account. These roles grant permissions to deploy resources in the target account. In your pipeline configuration, specify that deployment actions should assume these cross-account roles. You'll also need to configure S3 bucket policies to allow the pipeline to access artifacts across accounts. This pattern enables centralized pipeline management while maintaining account isolation for security and cost allocation.