AWS Developer Tools - CI/CD When You're Already Stuck in AWS

🚨 CRITICAL UPDATE: AWS killed CodeCommit for new customers in July 2024. If you don't already have repos there, you're using GitHub like everyone else.

AWS Developer Tools are what happens when Amazon decides to build their own CI/CD pipeline because they got tired of everyone complaining about Jenkins. The main players are CodeBuild (builds your shit), CodeDeploy (deploys your shit), and CodePipeline (orchestrates the whole mess).

Here's the deal: if you're already drowning in AWS services, these tools actually work pretty well together. No more maintaining Jenkins servers that break on weekends or configuring Docker-in-Docker setups that make you question your life choices. The AWS Well-Architected Framework actually makes sense here - managed services reduce operational overhead.

How this shit actually works

CodeBuild - Your new build server that doesn't crash on Sundays. Spins up Docker containers, runs your tests, and disappears when done. Supports 40+ languages but honestly you're probably using Node.js, Python, or Java like everyone else. The best part? When it breaks, it's AWS's problem, not yours.

Pro tip: Build caching will save your sanity. Enable it or watch your 2-minute builds turn into 10-minute nightmares. Check out the buildspec reference for all the YAML magic you'll need.

CodeDeploy - Actually handles deployments without making you cry. Blue-green deployments work out of the box, which is fucking revolutionary if you've ever tried setting this up with Jenkins. When shit goes wrong, it rolls back automatically instead of leaving your app in some weird half-deployed state. Works with EC2, Lambda, and ECS.

Real talk: The first deployment always fails. I don't know why, it just does. Budget extra time and check the troubleshooting guide.

CodePipeline - The orchestrator that ties everything together. GitHub webhooks trigger a build, build triggers a deploy, deploy hopefully doesn't break production. The visual pipeline editor is actually decent, unlike most AWS UIs that look like they were designed by committees. Supports parallel actions and approval stages.

Warning: Pipeline debugging is still a pain in the ass. The error messages are cryptic and CloudWatch logs have a 30-second delay that will drive you insane. Learn the AWS CLI for faster debugging.

The good, the bad, and the "why the fuck doesn't this work?"

What actually works well:

• GitHub integration is solid. Push to main, pipeline triggers, life is good.
• IAM permissions are a nightmare to set up initially but bulletproof once configured
• No more "Jenkins is down again" Slack messages on Saturday mornings
• Scaling just works - no capacity planning for build agents
• AWS CloudFormation integration for infrastructure as code
• Native Docker support without the usual headaches

What will make you drink:

• Error messages like "Build failed" with zero context (check Stack Overflow for translations)
• The 30-second CloudWatch log delay when you're debugging
• IAM permissions that work in dev but randomly break in prod
• Billing surprises when you forget about data transfer costs
• VPC configuration that makes simple things complicated
• Build timeouts that fail at 99% completion

Real advice: Use this if you're already all-in on AWS. The integration with ECS, Lambda, and EC2 is genuinely tight. But if you're starting fresh or mostly use other clouds, just use GitHub Actions. It's simpler and won't lock you into AWS forever. Consider Terraform for multi-cloud flexibility.

Pro tip: Learn the `aws logs` CLI commands now. You'll need them when the AWS Console inevitably shits the bed during a critical deployment. Also bookmark the AWS Status Page - it's not always your fault.

Now that you understand what these tools do and their limitations, let's talk about the elephant in the room: what this will actually cost you. AWS pricing is about as transparent as a brick wall, so we'll break down the real numbers.

AWS Developer Tools use pay-as-you-go pricing, which sounds cheap until you realize how quickly those minutes add up. Unlike GitHub Actions with predictable monthly costs, AWS billing can surprise you. Here's what you'll actually pay:

Service-Specific Pricing

CodeCommit is dead for new customers as of July 2024. You're using GitHub like everyone else now.

CodeBuild provides 100 build minutes monthly on the general1.small compute type at no cost. Beyond the free tier, pricing starts at $0.005 per build minute for small instances, scaling to $0.255 per minute for the largest compute types. Container-based builds using custom Docker images incur the same rates. Don't forget S3 storage costs for artifacts.

CodePipeline charges $1.00 per active pipeline per month after the first pipeline (which is free). V2-type pipelines offer more granular pricing based on action executions, starting at $0.002 per action execution for source actions. Third-party integrations may have additional costs.

CodeDeploy has no additional charges for deployments to EC2 instances or on-premises servers. Lambda and ECS deployments through CodeDeploy also incur no extra fees, making it cost-effective for serverless and containerized applications.

What You'll Actually Pay (No Bullshit Edition)

Small team (3 devs) with "just a few builds a day":

• CodeBuild: $25-60/month (those 100 free minutes disappear fast)
• CodePipeline: $3-10/month (pipelines add up quickly)
• S3 storage for artifacts: $5-20/month (nobody tells you about this)
• Data transfer: $10-30/month (surprise!)

Reality: $50-120/month, not the $7-20 AWS marketing suggests. Check the pricing calculator for your actual usage.

Enterprise team (20+ devs) building multiple times per day across environments:

• CodeBuild: $300-800/month (medium instances, lots of builds)
• CodePipeline: $50-150/month (dev/staging/prod pipelines)
• S3 costs: $50-200/month (artifacts pile up)
• CloudWatch logs: $20-50/month (yes, they charge for logs)
• Data transfer: $100-300/month (moving Docker images gets expensive)
• NAT Gateway costs if using VPC: $45+/month per AZ

Reality: $500-1500/month, and that's before you add monitoring, security scanning, and all the other shit you actually need.

How to Stop the Bleeding

Enable build caching or die: Docker layer caching can cut your build times (and costs) by 60%. Enable it in your buildspec.yml or watch your bills explode.

Use the smallest instance that works: Start with general1.small for simple builds. Don't use general1.large unless you're compiling Chromium or training ML models.

Clean up your S3 artifacts: Set lifecycle policies to delete old build artifacts after 30 days. That shit adds up fast.

Watch your data transfer: Building massive Docker images and pushing them around will murder your bill. Optimize your Dockerfiles with multi-stage builds.

Use CodeBuild local for debugging: Running 50 builds to debug a YAML issue will cost you $10. Use local builds to test locally first.

Monitor your spending: Enable billing alerts before you accidentally spend $500 on a runaway build loop. Use AWS Cost Explorer to track trends. Trust me on this one.