How to Build a CI/CD Pipeline on AWS

An AWS CI/CD pipeline is a set of connected services (CodePipeline, CodeBuild, CodeDeploy) that take over the parts of software delivery teams used to do by hand. Code gets developed, tested, and pushed to production without any manual intervention.

If your team is still deploying manually, you already know the drill.

A deploy that "should take ten minutes" turns into an hour because someone forgot to run a script. Something that worked fine in staging breaks in production because the two environments quietly drifted apart.

It's rarely one big failure; it's a string of small, avoidable ones. The problem gets worse as the codebase grows:

• More services
• More environments
• More people touching the release process
• More chances for something to slip through.

An AWS CI/CD pipeline doesn't eliminate risk entirely, but it does turn deployment into a repeatable process instead of a group effort held together by tribal knowledge.

This post walks through what an AWS CI/CD pipeline actually does, which services you'll need, and how to put one together from scratch.

What Is a CI/CD Pipeline on AWS?

Picture testing, packaging, and deploying an app by hand every single time you ship a change. It's manageable when the app is small, but it becomes an inconvenience pretty fast.

An AWS CI/CD pipeline takes that entire sequence (from the moment you write code to when it's live) and runs it the same way every time. No skipped steps, no "I thought you deployed that."

Here's roughly how it breaks down:

Continuous Integration (CI)

Every time a developer executes code, the pipeline kicks off a build and runs the test suite automatically. Bugs get caught here, before they have a chance to reach anything resembling production.

Continuous Delivery / Continuous Deployment (CD)

Continuous Delivery gets a build ready for release but stops short of pushing it live - someone still hits "deploy." Continuous Deployment goes one step further and ships anything that passes its checks straight to production, no manual approval needed.

Many teams select AWS because it introduces them to a number of services that integrate with each other. You're not stitching together five different vendors and hoping the integrations hold. Solvios's AWS cloud services team can help you evaluate whether a fully AWS-native setup makes sense for your stack.

AWS CI/CD Tools To Use For Creating A Pipeline

A modern AWS CI/CD pipeline is a mix of a few core services, each responsible for a specific stage of the software delivery process. Here’s a detailed insight into the different tools that go into creating a pipeline:

AWS CodePipeline:

CodePipeline handles the entire workflow. Automatically detects changes to your code, starts the build and test process, and promotes successful releases through each step of the deployment process.

AWS CodeBuild:

CodeBuild allows the team to compile the application, install dependencies, run automated testing, and build the architecture necessary for deployment.

AWS CodeDeploy:

CodeDeploy supports deployment strategies that minimize downtime and deploys your application to services such as Amazon EC2, Amazon ECS, or AWS Lambda.

Optional: Amazon CloudWatch (Optional):

Teams can use CloudWatch to monitor pipeline activity, collect logs, and set alerts to quickly surface deployment issues.

Optional: AWS CloudFormation or AWS CDK:

If you do infrastructure as code, CloudFormation and the AWS Cloud Development Kit (CDK) help provision and update AWS resources with version-controlled templates.

AWS has been quietly retiring parts of its older DevOps lineup. CodeStar is not operational, and CodeCommit is closed to new customers. Amazon CodeCatalyst showed up as a newer development platform, but for most new pipelines AWS now points teams toward CodePipeline, CodeBuild, and CodeDeploy, paired with GitHub or GitLab as the source repo.

AWS CI/CD Pipeline Reference Architecture

At a high level, the flow looks like this:

Source (GitHub/GitLab/Bitbucket) → AWS CodePipeline → AWS CodeBuild → Test → AWS CodeDeploy → Amazon CloudWatch

Diagram caption: A reference architecture showing how code moves from a source repository through automated build, testing, deployment, and monitoring stages in an AWS CI/CD pipeline.

A developer pushes to the repo, and the CodePipeline works on the change. Once complete, the code is handed over to CodeBuild, which compiles it with the current codebase and runs the test suite.

If everything matches, CodeDeploy ships the code to the target environment, and CloudWatch analyses its performance by watching logs, analysis metrics, and overall deployment health.

Build the AWS CI/CD Pipeline - A Clear Insight

Step 1. Set up the prerequisites

Your project needs to already live in a Git repo (GitHub or GitLab work fine). You'll also need an AWS account with permissions across CodePipeline, CodeBuild, CodeDeploy, IAM, and S3.

One tip if this is your first pipeline: don't hand every service the same IAM role. It's tempting to keep things simple, but separate roles per service save you a lot of headaches once the project grows past a handful of people.

Step 2. Connect your source repository

Create a new pipeline in CodePipeline and link it to your repo. Once that connection is live, a push to the right branch is enough to trigger a run, so nobody has to start a deployment manually again.

Step 3. Configure the build stage

CodeBuild reads a buildspec.yml file to figure out how your app should be built. Drop it in the root of your repo so every run follows the same steps.

Note: buildspec.yml belongs in the root of your repository; that's the default location CodeBuild looks for.

Step 4. Add a test stage

A build that compiles is not a build that is safe to ship. Wire in unit and integration tests so a failing check stops the pipeline before it reaches deployment, not after.

Step 5. Configure the deployment stage

Once build and test both pass, hand things off to CodeDeploy and point it at your target - EC2, ECS, or Lambda, depending on your setup. For anything customer-facing, a rolling or blue/green deployment strategy will save you from the worst kind of outage: the one caused by your own release.

Step 6. Trigger and verify the pipeline

Push a small change to the connected branch and watch what happens. You're looking for:

• The source stage picking up the commit
• A clean build
• Tests passing
• Deployment finishing without errors
• Logs and metrics showing up in CloudWatch

If something stalls, the pipeline usually tells you exactly which stage failed- a big improvement over digging through a manual deploy to figure out what went wrong.

Best Practices While Building A CI/CD Pipeline on AWS

A working pipeline is the easy part. Keeping it secure and reliable as more people rely on it is where most teams actually get tested.

Security

• IAM, least privilege:

Give each service (CodeBuild, CodeDeploy, or other tools that are a part of the pipeline) limited permissions to execute its job.

Why does it matter?

A broad IAM role acts as an open door. If one service gets compromised, scoped permissions damage the entire workflow.

• Secrets Manager or Parameter Store:

Never hardcode credentials, API keys, or tokens into buildspec.yml. Pull them in at runtime instead.

Why does it matter?

Anything sitting in a build file ends up in version control eventually, whether you meant it to or not, and repos have a way of becoming more public than intended.

• Reliability:

Have a rollback plan before you need one. Lay out exact steps on how you'll revert a bad deploy before it happens, not while it's happening.

Why does it matter?

The five minutes you spend figuring out a rollback strategy under pressure is five minutes your app is broken in production.

• Blue-green or Canary Deployments:

Release to a subset of traffic or a parallel environment first, instead of pushing to everyone at once.

Why does it matter?

It turns a full-scale outage into a contained, easily reversible one.

• Separate dev, Staging, and Production:

Each environment should be genuinely isolated, not just labeled differently.

Why does it matter?

"It worked in staging" means nothing if staging and production don't actually match.

• Automated Test Gates Before Every Deployment:

No build reaches production without clearing tests first: no exceptions, no manual override.

Why does it matter?

The one time you skip the gate "just this once" is usually the time it would've caught something.

Cost Considerations While Building CI/CD Pipeline on AWS

Most AWS CI/CD guides skip this entirely, which is odd; it's usually the first question a decision-maker asks.

Here’s a closer look at the cost considerations while building CI/CD pipelines on AWS:

CodePipeline

• Billed per active pipeline, per month
• Applies only when the pipeline runs at least once that month
• Idle pipelines generally cost nothing

CodeBuild

• Billed per build minute, not per pipeline
• Rate depends on compute type (small Linux instances cost less than large ones)
• Arm-based compute typically undercuts equivalent x86 pricing

CodeDeploy

• Free for deployments to EC2 or Lambda within your own AWS account
• On-premises deployments are billed separately
• For standard AWS-native setups, this stage adds close to nothing to the bill

Still confused? Here’s a closer look at the pricing in actual numbers (Price in USD, subject to change)

The bottom line:

Cost comes down almost entirely to build volume and compute choice, not the orchestration layer. Rates shift often, so treat any figure here as a snapshot, not a promise. If you'd rather not track pricing changes yourself, Solvios can architect and manage the pipeline for you with cost baked into the design from day one.

Want a pipeline architected with cost in mind from day one? Solvios DevOps consulting team can help you scope one out.

AWS-Native vs. Jenkins vs. GitHub Actions

When exploring their options, many teams tend to focus on AWS-Native, Jenkins, or GitHub Actions. While none of these three is objectively "best" - the right pick depends heavily on what your team already runs and how much infrastructure you're willing to own.

Here’s a closer look at what it looks like in detail:

To Summarize:

• For a team that's already deep in AWS, the native tools cut out the most friction.
• Jenkins is a good option if a pipeline needs something off-the-shelf tools aren’t executing well, or when builds have to run on-prem for compliance reasons.
• GitHub Actions is the fastest way to get CI/CD running with almost no new infrastructure, which makes it the default choice for teams that don't have a strong reason to look elsewhere.
• If your team runs workloads across more than one provider, it's worth comparing this against a similar pipeline built on Google Cloud before locking into a single ecosystem.

Common Mistakes and Troubleshooting

My CodeBuild stage is failing

Usually an IAM permissions gap - the CodeBuild role can't reach a resource it needs, like S3 or ECR. Check CloudWatch Logs first; the error almost always names the missing permission directly.

My buildspec.yml working

Most often it's not in the repo root, or the YAML is indented incorrectly. CodeBuild fails silently on malformed syntax more often than teams expect, so validate the file before pushing.

My deployment failed, how do I roll back?

Failed deployments are usually a mismatched target group, a health check that never passes, or an app that crashes on startup. CodeDeploy can auto-rollback on failure if you've configured it; check that setting before you need it, not after.

CodePipeline fails to access S3 artifact bucket

Typically, a bucket policy or IAM role is missing s3: GetObject/s3:PutObject permissions, or a bucket is in the wrong region. Cross-region artifact buckets are a common, easy-to-miss cause.