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AWS CodeBuild vs GitLab CI: What are the differences?
Developers describe AWS CodeBuild as "Build and test code with continuous scaling". AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. On the other hand, GitLab CI is detailed as "GitLab integrated CI to test, build and deploy your code". GitLab offers a continuous integration service. If you add a .gitlab-ci.yml file to the root directory of your repository, and configure your GitLab project to use a Runner, then each merge request or push triggers your CI pipeline.
AWS CodeBuild and GitLab CI can be primarily classified as "Continuous Integration" tools.
"Pay per minute" is the primary reason why developers consider AWS CodeBuild over the competitors, whereas "Robust CI with awesome Docker support" was stated as the key factor in picking GitLab CI.
WebbyLab, Infoxchange, and Dial Once are some of the popular companies that use GitLab CI, whereas AWS CodeBuild is used by Convox, Volta Industries, and ChromaDex. GitLab CI has a broader approval, being mentioned in 210 company stacks & 93 developers stacks; compared to AWS CodeBuild, which is listed in 21 company stacks and 14 developer stacks.
We are a mid-size startup running Scala apps. Moving from Jenkins/EC2 to Spinnaker/EKS and looking for a tool to cover our CI/CD needs. Our code lives on GitHub, artifacts in nexus, images in ECR.
Drone is out, GitHub actions are being considered along with Circle CI and GitLab CI.
We primarily need:
- Fast SBT builds (caching)
- Low maintenance overhead (ideally serverless)
- Everything as code
- Ease of use
I think I've tried most of the CI tools out there at some point. It took me a while to get around to Buildkite because at first I didn't see much point given it seemed like you had to run the agent yourself. Eventually it dawned on me why this approach was more ingenious than I realised:
Running my app in a production (or production-like) environment was already a solved problem, because everything was already in some form of "everything as code". Having a test environment where the only difference was adding the Buildkite agent was a trivial addition.
It means that dev/test/prod parity is simple to achieve and maintain. It's also proven to be much easier to support than trying to deal with the problems that come with trying to force an app to fit into the nuances and constraints that are imposed by the containers/runtime of a CI service. When you completely control all of the environment the tests are running in you define those constraints too. It's been a great balance between a managed service and the flexibility of running it yourself.
And while none of my needs have hit the scale of Shopify (I saw one of their engineers speak about it at a conference once, I can't find the video now though 😞) it's good to know I can scale out my worker nodes to hundreds of thousands of workers to reduce the time it takes for my tests to run.
I would recommend you to consider the JFrog Platform that includes JFrog Pipelines - it will allow you to manage the full artifact life cycle for your sbt, docker and other technologies, and automate all of your CI and CD using cloud native declarative yaml pipelines. Will integrate smoothly with all your other toolset.
more configurable to setup ci/cd: * It can provide caching when build sbt, just add this section to yml file * Easy to use, many documentation
Weakness: * Need use gitlab as repository to bring more powerful configuration
Buddy is one of the most easy-to-use tools for CI I ever met. When I needed to set up the pipeline I was really impressed with how easy it is to create it with Buddy with only a few moments. It's literally like: 1. Add repo 2. Click - Click - Click 3. You're done and your app is on prod :D The top feature that I've found is a simple integration with different notification channels - not only Slack (which is the one by default), but Telegram and Discord. The support is also neat - guys respond pretty quickly on even a small issue.
Pros of AWS CodeBuild
- Pay per minute7
- Parameter Store integration for passing secrets5
- Integrated with AWS4
- Streaming logs to Amazon CloudWatch3
- Bit bucket integration3
- GitHub Webhooks support2
- AWS Config and Config rule integration for compliance2
- VPC PrivateLinks to invoke service without internet2
- Windows/.NET support1
- Jenkins plugin integration1
- Ondemand scaling of build jobs1
- Scheduled builds with CloudWatch Events integration1
- Local build debug support1
- Native support for accessing Amazon VPC resources1
- Docker based build environment1
- Support for bringing custom Docker images1
- Fully managed (no installation/updates, servers to mai1
- PCI, SOC, ISO, HIPAA compliant1
- Full API/SDKs/CLI support1
- YAML based configuration1
- Great support (forums, premium support, SO, GitHub)1
- Perpetual free tier option (100 mins/month)1
- GitHub Enterprise support1
Pros of GitLab CI
- Robust CI with awesome Docker support22
- Simple configuration13
- All in one solution9
- Source Control and CI in one place7
- Integrated with VCS on commit5
- Free and open source5
- Easy to configure own build server i.e. GitLab-Runner5
- Hosted internally2
- Built-in Docker Registry1
- Built-in support of Review Apps1
- Pipeline could be started manually1
- Enable or disable pipeline by using env variables1
- Gitlab templates could be shared across logical group1
- Easy to setup the dedicated runner to particular job1
- Built-in support of Kubernetes1
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Cons of AWS CodeBuild
- Poor branch support2
Cons of GitLab CI
- Works best with GitLab repositories2