When deploying a new application to an environment, engineers must remember many manual steps and checks, which can lead to oversights or skipped tasks, resulting in incomplete releases and increased stress for everyone involved. The AWS team strives to eliminate these painful moments by offering development teams a fully managed CI/CD service.
In this article, I will cover everything you need to know about AWS CodePipeline: what it does, how its key features work, how much it costs, and how to keep those costs under control. By the end, you'll know exactly how to evaluate whether it's the right fit for your AWS environment.
What Is AWS CodePipeline?
AWS CodePipeline is a fully managed service that provides continuous integration and continuous delivery (CI/CD). It will automate building, testing, and deploying the application every time a change is made to the application code as long as this is based on the defined steps in a CI/CD workflow and as defined by the AWS CodePipeline user.
Every CodePipeline has stages, and each stage has actions. A typical pipeline looks like this:
Source stage: Identifies a code change in GitHub, Amazon S3, Amazon ECR, or AWS CodeCommit.
Build stage: Use AWS CodeBuild or Jenkins to compile and run unit tests.
Test stage: Runs automated tests against the build.
Deploy stage: Deploys the changes to Amazon ECS, AWS Lambda, EC2, or AWS Fargate.
Each execution proceeds through these stages, and in the case of stage failure, operation stops to safeguard your production environment from poor code.
Key Features of AWS CodePipeline
Workflow Modeling and Parallel Execution
With CodePipeline, you can model your pipeline and have a cloud-based service visualize your release workflow. You can define your own stages and include your own actions in each step and set the order in which the actions and stages run. You can configure multiple actions to run in sequence or run them in parallel to expedite the process of building your pipeline.
Deep AWS Integrations
CodePipeline integrates with other AWS services, which can provide you with a lot of value in your workflow. Your source code can come from GitHub, Amazon ECR, AWS S3, and CodeCommit; your builds run in CodeBuild; your deployments can go to ECS, Fargate, Elastic Beanstalk, or CodeDeploy; and you can run an AWS Lambda function at any of the steps in the workflow. For example, you can use it to validate a deployment or conduct a post-deployment check.
You can also use the AWS CloudFormation service to add actions in your pipeline to provision, update, or delete AWS infrastructure, which is very useful when working with the AWS SAM.
Third-Party and Custom Plugins
CodePipeline allows one-click integration with services such as GitHub and Jenkins. The CodePipeline open-source agent and Jenkins plugin allow you to register services as custom actions if you are using custom tools or build servers. This lets you keep things the way they are and custom integrate with CodePipeline at the same time.
Declarative Templates
You can define the structure of your pipeline using a JSON document. This allows you to put your pipeline configuration in a version control system. It also allows you to create copies of your pipeline in other environments easily and modify your existing pipelines through scripts.
IAM-Based Access Control
All the pipelines are protected by AWS IAM. You can control the pipeline modifications, execution of the pipeline, and viewing of the execution history by using IAM users, roles, and SAML-integrated directories.
Event-Driven Triggers and Variables (V2 Only)
V2 pipelines support advanced trigger configurations. You can control the start of a pipeline based on a Git tag, a specific branch of a repository, a pull request, or changes in specific files in the repository. Pipelines receive variables during runtime. This improves the flexibility of a pipeline without duplicate configurations.
Notifications
CodePipeline integrates with Amazon SNS so the users can receive alerts based on events in a pipeline. These events include failures in stages, successful deployments, and manual actions. This allows the users to be updated without logging in to the console and monitoring events.
AWS CodePipeline Pricing Explained
AWS CodePipeline has two versions of pipelines, called V1 and V2. Each of these offers different pricing, and there are no upfront costs and no long-term contracts for either version.
V1 Pipeline Pricing
All v1 pipelines will charge a flat monthly fee for each active pipeline.
$1.00 per active pipeline per month
All newly created v1 pipelines will incur no charges for the first 30 days after being created.
After the first 30 days, a newly created v1 pipeline will incur charges for the month it was created, provided that at least one action is executed.
Active pipelines that are not executed in a month incur no charges.
Charges are not prorated, meaning if a V1 pipeline is active for a month, it will incur the monthly charge.
AWS Free Tier: One free active V1 pipeline per month per account.
V2 Pipeline Pricing
All v2 pipelines will have a different pricing model, which is based on consumption.
$0.002 per action execution minute
Each action is charged based on the amount of time it is active for.
The time captured for an action will be rounded to the nearest minute.
Manual approval actions are not billed.
All standard action types (source, build, test, deploy) will be chargeable.
AWS Free Tier: 100 free action execution minutes per month, shared across all V2 pipelines in the account. Unused minutes do not roll over.
V1 vs. V2 Pricing: Which Costs Less?
The answer depends on your usage pattern. V1 has simple and straightforward pricing. This model works well for teams that have a mid-range number of pipelines and frequent pipeline executions. V2 is priced based on pipeline execution, so the faster your actions, the cheaper it is.
Each AWS service has a free tier, and the AWS documentation even has a Python script you can use to analyze which pipeline, V1 or V2, will be cheaper for you based on your execution history.
Pricing Example: Mixed V1 and V2 Pipelines
Imagine you have 10 V1 pipelines and 1 V2 pipeline, running 20 executions a month, with each execution having 4 actions that take 2 minutes each.
V1 charge: (10 - 1 free) × $1.00 = $9.00
V2 charge: (20 × 4 × 2) = 160 minutes. (160 - 100 free) × $0.002 = $0.12
Total: $9.12
Additional Costs to Watch
AWS service pricing may seem simple, but your bill will include more than just pipeline charges. The pipeline integrates with many services from AWS, and those services may charge additional fees.
Here's a breakdown of the services you might incur charges for:
Amazon S3: Used for storing and retrieving pipeline artifacts.
AWS CodeBuild: Costs incurred for build minutes consumed during the execution of pipelines.
CloudWatch Logs: Costs are incurred for storing logs from pipeline runs.
Data Transfer: Costs are incurred for data movement, especially with cross-region data in the pipeline.
Third-Party Integrations: Costs may be incurred for actions initiated by external services.
These secondary charges can quickly add up and significantly increase your bill, especially when build containers run for a long time or when there are frequent updates to artifacts.
When Should You Use AWS CodePipeline?
It's a strong fit if:
Your workloads run mostly on AWS.
You want IAM-native security and do not want to manage identities.
You want a scalable CI/CD service and do not want to manage any infrastructure.
You deploy often and want consistent and repeatable processes to handle releases.
You are developing serverless applications and want to integrate easily with Lambda and CloudFormation.
Consider Alternatives If:
You are working with multiple cloud providers and want a platform-agnostic option.
You need highly customized pipeline UIs or want built-in analytics dashboards.
You are new to AWS, and you have a steep learning curve for the ecosystem.
You have very complex branching workflows that would benefit from more flexible pipeline logic.
Pros and Cons of AWS CodePipeline
Pros | Cons | |
Management | Fully managed, no servers to maintain | Limited UI customization options |
Integrations | Deep native AWS integrations | Less intuitive for teams outside the AWS ecosystem |
Scalability | Scales automatically with your workload | Costs can grow significantly at large scale |
Security | IAM-based access control built in | Requires solid AWS IAM knowledge to configure correctly |
Pricing | Pay only for what you use (V2) | Additional charges from connected services can be hard to predict |
How to Reduce AWS CodePipeline Costs
1. Optimize Pipeline Triggers
Not everything needs to trigger a response.
While CodePipeline charges you for every active pipeline every month, how often a pipeline is run affects how often you get charged for CodeBuild and the other supporting services, and that is what costs the most in the CI/CD pipeline.
When setting up triggers, try to filter for specific branches, tags, or file paths so that not every trigger runs the pipeline for every response. Reducing trigger run count is an easy way to reduce costs.
2. Consolidate Pipelines Where It Makes Sense
Each active pipeline incurs a monthly charge.
If you have microservices that have the same steps in their deployment process, you can use conditional stage logic to merge their pipelines. This way you can have the same workflows for microservices to run in parallel, and that will reduce costs.
Note: Don't merge every pipeline. While that is the smart and cost-effective thing to do, if you merge too many, then you can cause issues where services are now blindly deploying at the same time. Avoiding unnecessary merges is a good approach, especially if there is a lot of added potential for risk.
3. Speed Up Your Build Actions
Amazon CodeBuild is charged based upon the time it takes and the type of computing resources used during the build. The build becomes less expensive the faster it completes. Build time can be optimized by: - Enabling caching for Docker layers; - Caching NPM, Pip, Maven, and other dependencies; - Utilizing base images that are smaller; - Caching unnecessary steps that are build dependencies. Even a build that is faster by 1-2 minutes can provide savings in a larger-scale cost.
Note: CodePipeline will not have build time charges, and time is free with the pipeline in the CodePipeline Action. Always check for your region's pricing at the publication time.
4. Set Artifact Lifecycle Policies
Pipeline artifacts are stored in S3. If you do not implement lifecycle policies, you can accrue costs for storage on unused artifacts. To fix this, you can set expiration rules on the S3 bucket for CodePipeline artifacts to delete files that are older than a specified time.
5. Monitor With AWS Cost Explorer
If you want to see more details on your CodeBuild and CodePipeline costs, you can see this in AWS Cost Explorer. Create budget alerts so that you can catch cost spikes on the issue and avoid them. It is beneficial to review the costs for teams that have many active pipelines.
Conclusion
AWS CodePipeline is a managed CI/CD service that gives your team the ability to use native integrations, IAM security, and various pricing options depending on your workflow needs.
The V1 model offers flat rate pricing, which is good for predictability, and the V2 model is more likely to reward users based on their efficiency, which is better for teams who want to focus more on performance.
Start by looking at your deployment process; if you find that there are manual processes and mistakes that are causing you to slow down, then exploring CodePipeline is worth it. You can test out CodePipeline before you scale using the AWS Free Tier.
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