[{"data":1,"prerenderedAt":828},["ShallowReactive",2],{"/en-us/blog/getting-started-with-gitlab-understanding-ci-cd":3,"navigation-en-us":45,"banner-en-us":465,"footer-en-us":475,"blog-post-authors-en-us-GitLab":715,"blog-related-posts-en-us-getting-started-with-gitlab-understanding-ci-cd":730,"blog-promotions-en-us":765,"next-steps-en-us":818},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":26,"description":17,"extension":27,"externalUrl":28,"featured":15,"heroImage":19,"isFeatured":15,"meta":29,"navigation":15,"path":30,"publishedDate":26,"rawbody":31,"seo":32,"slug":14,"stem":37,"tagSlugs":38,"tags":43,"template":13,"updatedDate":28,"__hash__":44},"blogPosts/en-us/blog/getting-started-with-gitlab-understanding-ci-cd.md","Getting started with GitLab: Understanding CI/CD",[7],"gitlab",[9],"GitLab","*Welcome to our \"Getting started with GitLab\" series, where we help newcomers get familiar with the GitLab DevSecOps platform.*\n\nImagine a workflow where every code change is automatically built, tested, and deployed to your users. That's the power of [Continuous Integration/Continuous Delivery (CI/CD)](https://about.gitlab.com/topics/ci-cd/)! CI/CD helps you catch bugs early, ensures code quality, and delivers software faster and more frequently.\n\n### What is CI/CD?\n\n* **Continuous Integration** is a development practice where developers integrate code changes into a shared repository frequently, preferably several times a day. Each integration is then verified by an automated build and test process, allowing teams to detect problems early.  \n* **Continuous Delivery** extends CI by automating the release pipeline, ensuring that your code is *always* in a deployable state. You can deploy your application to various environments (e.g., staging, production) with a single click or automatically.  \n* **Continuous Deployment** takes it a step further by automatically deploying *every successful build* to production. This requires a high degree of confidence in your automated tests and deployment process.\n\n### Why GitLab CI/CD?\n\nGitLab CI/CD is a powerful, integrated system that comes built-in with GitLab. It offers a seamless experience for automating your entire software development lifecycle. With GitLab CI/CD, you can:\n\n* **Automate everything:** Build, test, and deploy your applications with ease.  \n* **Catch bugs early:** Detect and fix errors before they reach production.  \n* **Get faster feedback:** Receive immediate feedback on your code changes.  \n* **Improve collaboration:** Work together more effectively with automated workflows.  \n* **Accelerate delivery:** Release software faster and more frequently.  \n* **Reduce risk:** Minimize deployment errors and rollbacks.\n\n### The elements of GitLab CI/CD\n\n* `.gitlab-ci.yml`**:** This [YAML file](https://docs.gitlab.com/ci/yaml/), located in your project's root directory, defines your CI/CD pipeline, including stages, jobs, and runners.  \n* [**GitLab Runner**](https://docs.gitlab.com/runner/)**:** This agent executes your CI/CD jobs on your infrastructure (e.g. physical machines, virtual machines, Docker containers, or Kubernetes clusters).  \n* [**Stages**](https://docs.gitlab.com/ci/yaml/#stages)**:** Stages define the order of execution for your jobs (e.g. build, test, and deploy).  \n* [**Jobs**](https://docs.gitlab.com/ci/yaml/#job-keywords)**:** Jobs are individual units of work within a stage (e.g. compile code, run tests, and deploy to staging).\n\n### Setting up GitLab CI\n\nGetting started with GitLab CI is simple. Here's a basic example of a `.gitlab-ci.yml` file:\n\n```yaml\nstages:\n  - build\n  - test\n  - deploy\n\nbuild_job:\n  stage: build\n  script:\n    - echo \"Building the application...\"\n\ntest_job:\n  stage: test\n  script:\n    - echo \"Running tests...\"\n\ndeploy_job:\n  stage: deploy\n  script:\n    - echo \"Deploying to production...\"\n  environment:\n    name: production\n\n```\n\nThis configuration defines three stages: \"build,\" \"test,\" and \"deploy.\" Each stage contains a job that executes a simple script.\n\n### CI/CD configuration examples\n\nLet's explore some more realistic examples.\n\n**Building and deploying a Node.js application**\n\nThe pipeline definition below outlines using npm to build and test a Node.js application and [dpl](https://docs.gitlab.com/ci/examples/deployment/) to deploy the application to Heroku. The deploy stage of the pipeline makes use of [GitLab CI/CD variables](https://docs.gitlab.com/ci/variables/), which allow developers to store sensitive information (e.g. credentials) and securely use them in CI/CD processes. In this example, an API key to deploy to Heroku is stored under the variable key name `$HEROKU_API_KEY` used by the dpl tool.\n\n```yaml\nstages:\n  - build\n  - test\n  - deploy\n\nbuild:\n  stage: build\n  image: node:latest\n  script:\n    - npm install\n    - npm run build\n\ntest:\n  stage: test\n  image: node:latest\n  script:\n    - npm run test\n\ndeploy:\n  stage: deploy\n  image: ruby:latest\n  script:\n    - gem install dpl\n    - dpl --provider=heroku --app=$HEROKU_APP_NAME --api-key=$HEROKU_API_KEY\n\n```\n\n**Deploying to different environments (staging and production)**\n\nGitLab also offers the idea of [Environments](https://docs.gitlab.com/ci/environments/) with CI/CD. This feature allows users to track deployments from CI/CD to infrastructure targets. In the example below, the pipeline adds stages with an environment property for a staging and production environment. While the deploy_staging stage will always run its script, the deploy_production stage requires manual approval to prevent accidental deployment to production.  \n\n```yaml\nstages:\n  - build\n  - test\n  - deploy_staging\n  - deploy_production\n\nbuild:\n  # ...\n\ntest:\n  # ...\n\ndeploy_staging:\n  stage: deploy_staging\n  script:\n    - echo \"Deploying to staging...\"\n  environment:\n    name: staging\n\ndeploy_production:\n  stage: deploy_production\n  script:\n    - echo \"Deploying to production...\"\n  environment:\n    name: production\n  when: manual  # Requires manual approval\n\n```\n\n### GitLab Auto DevOps\n\n[GitLab Auto DevOps](https://docs.gitlab.com/topics/autodevops/) simplifies CI/CD by providing a pre-defined configuration that automatically builds, tests, and deploys your applications. It leverages best practices and industry standards to streamline your workflow.\n\nTo enable Auto DevOps:\n\n1. Go to your project's **Settings > CI/CD > General pipelines**.  \n2. Enable the **Auto DevOps** option.\n\nAuto DevOps automatically detects your project's language and framework and configures the necessary build, test, and deployment stages. You don’t even need to create a `.gitlab-ci.yml` file.\n\n### CI/CD Catalog\n\nThe [CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/) is a list of projects with published [CI/CD components](https://docs.gitlab.com/ci/components/) you can use to extend your CI/CD workflow. Anyone can create a component project and add it to the CI/CD Catalog or contribute to an existing project to improve the available components. You can find published components in the [CI/CD Catalog](https://gitlab.com/explore/catalog) on GitLab.com.\n\n> [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n\n### CI templates\n\nYou can also create your own [CI templates](https://docs.gitlab.com/ci/examples/) to standardize and reuse CI/CD configurations across multiple projects. This promotes consistency and reduces duplication.\n\nTo create a CI template:\n\n1. Create a `.gitlab-ci.yml` file in a dedicated project or repository.  \n2. Define your CI/CD configuration in the template.  \n3. In your project's `.gitlab-ci.yml` file, use the `include` keyword to include the template.\n\n## Take your development to the next level\n\nGitLab CI/CD is a powerful tool that can transform your development workflow. By understanding the concepts of CI/CD, configuring your pipelines, and leveraging features like Auto DevOps, the CI/CD Catalog, and CI templates, you can automate your entire software development lifecycle and deliver high-quality software faster and more efficiently.\n\n> Want to take your learning to the next level? Sign up for [GitLab University courses](https://university.gitlab.com/). Or you can get going right away with a [free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/).\n\n## \"Getting Started with GitLab\" series\n\nCheck out more articles in our \"Getting Started with GitLab\" series:\n\n- [How to manage users](https://about.gitlab.com/blog/getting-started-with-gitlab-how-to-manage-users/)\n- [How to import your projects to GitLab](https://about.gitlab.com/blog/getting-started-with-gitlab-how-to-import-your-projects-to-gitlab/)  \n- [Mastering project management](https://about.gitlab.com/blog/getting-started-with-gitlab-mastering-project-management/)\n- [Automating Agile workflows with the gitlab-triage gem](https://about.gitlab.com/blog/automating-agile-workflows-with-the-gitlab-triage-gem/)\n- [Working with CI/CD variables](https://about.gitlab.com/blog/getting-started-with-gitlab-working-with-ci-cd-variables/)","product",{"template":13,"slug":14,"featured":15},"BlogPost","getting-started-with-gitlab-understanding-ci-cd",true,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":26,"body":10},"Learn the basics of continuous integration/continuous delivery in this beginner's guide, including what CI/CD components are and how to create them.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659525/Blog/Hero%20Images/blog-getting-started-with-gitlab-banner-0497-option4-fy25.png",[21,22,23,24,11,25],"CI/CD","CI","CD","DevSecOps platform","tutorial","2025-04-25","md",null,{},"/en-us/blog/getting-started-with-gitlab-understanding-ci-cd","---\nseo:\n  title: 'Getting started with GitLab: Understanding CI/CD'\n  description: >-\n    Learn the basics of continuous integration/continuous delivery in this\n    beginner's guide, including what CI/CD components are and how to create\n    them.\n  ogTitle: 'Getting started with GitLab: Understanding CI/CD'\n  ogDescription: >-\n    Learn the basics of continuous integration/continuous delivery in this\n    beginner's guide, including what CI/CD components are and how to create\n    them.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659525/Blog/Hero%20Images/blog-getting-started-with-gitlab-banner-0497-option4-fy25.png\n  ogUrl: >-\n    https://about.gitlab.com/blog/getting-started-with-gitlab-understanding-ci-cd\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: >-\n    https://about.gitlab.com/blog/getting-started-with-gitlab-understanding-ci-cd\ntitle: 'Getting started with GitLab: Understanding CI/CD'\ndescription: Learn the basics of continuous integration/continuous delivery in this beginner's guide, including what CI/CD components are and how to create them.\nauthors:\n  - GitLab\nheroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659525/Blog/Hero%20Images/blog-getting-started-with-gitlab-banner-0497-option4-fy25.png\ntags:\n  - CI/CD\n  - CI\n  - CD\n  - DevSecOps platform\n  - product\n  - tutorial\ncategory: product\ndate: '2025-04-25'\nslug: getting-started-with-gitlab-understanding-ci-cd\nfeatured: true\ntemplate: BlogPost\n---\n\n*Welcome to our \"Getting started with GitLab\" series, where we help newcomers get familiar with the GitLab DevSecOps platform.*\n\nImagine a workflow where every code change is automatically built, tested, and deployed to your users. That's the power of [Continuous Integration/Continuous Delivery (CI/CD)](https://about.gitlab.com/topics/ci-cd/)! CI/CD helps you catch bugs early, ensures code quality, and delivers software faster and more frequently.\n\n### What is CI/CD?\n\n* **Continuous Integration** is a development practice where developers integrate code changes into a shared repository frequently, preferably several times a day. Each integration is then verified by an automated build and test process, allowing teams to detect problems early.  \n* **Continuous Delivery** extends CI by automating the release pipeline, ensuring that your code is *always* in a deployable state. You can deploy your application to various environments (e.g., staging, production) with a single click or automatically.  \n* **Continuous Deployment** takes it a step further by automatically deploying *every successful build* to production. This requires a high degree of confidence in your automated tests and deployment process.\n\n### Why GitLab CI/CD?\n\nGitLab CI/CD is a powerful, integrated system that comes built-in with GitLab. It offers a seamless experience for automating your entire software development lifecycle. With GitLab CI/CD, you can:\n\n* **Automate everything:** Build, test, and deploy your applications with ease.  \n* **Catch bugs early:** Detect and fix errors before they reach production.  \n* **Get faster feedback:** Receive immediate feedback on your code changes.  \n* **Improve collaboration:** Work together more effectively with automated workflows.  \n* **Accelerate delivery:** Release software faster and more frequently.  \n* **Reduce risk:** Minimize deployment errors and rollbacks.\n\n### The elements of GitLab CI/CD\n\n* `.gitlab-ci.yml`**:** This [YAML file](https://docs.gitlab.com/ci/yaml/), located in your project's root directory, defines your CI/CD pipeline, including stages, jobs, and runners.  \n* [**GitLab Runner**](https://docs.gitlab.com/runner/)**:** This agent executes your CI/CD jobs on your infrastructure (e.g. physical machines, virtual machines, Docker containers, or Kubernetes clusters).  \n* [**Stages**](https://docs.gitlab.com/ci/yaml/#stages)**:** Stages define the order of execution for your jobs (e.g. build, test, and deploy).  \n* [**Jobs**](https://docs.gitlab.com/ci/yaml/#job-keywords)**:** Jobs are individual units of work within a stage (e.g. compile code, run tests, and deploy to staging).\n\n### Setting up GitLab CI\n\nGetting started with GitLab CI is simple. Here's a basic example of a `.gitlab-ci.yml` file:\n\n```yaml\nstages:\n  - build\n  - test\n  - deploy\n\nbuild_job:\n  stage: build\n  script:\n    - echo \"Building the application...\"\n\ntest_job:\n  stage: test\n  script:\n    - echo \"Running tests...\"\n\ndeploy_job:\n  stage: deploy\n  script:\n    - echo \"Deploying to production...\"\n  environment:\n    name: production\n\n```\n\nThis configuration defines three stages: \"build,\" \"test,\" and \"deploy.\" Each stage contains a job that executes a simple script.\n\n### CI/CD configuration examples\n\nLet's explore some more realistic examples.\n\n**Building and deploying a Node.js application**\n\nThe pipeline definition below outlines using npm to build and test a Node.js application and [dpl](https://docs.gitlab.com/ci/examples/deployment/) to deploy the application to Heroku. The deploy stage of the pipeline makes use of [GitLab CI/CD variables](https://docs.gitlab.com/ci/variables/), which allow developers to store sensitive information (e.g. credentials) and securely use them in CI/CD processes. In this example, an API key to deploy to Heroku is stored under the variable key name `$HEROKU_API_KEY` used by the dpl tool.\n\n```yaml\nstages:\n  - build\n  - test\n  - deploy\n\nbuild:\n  stage: build\n  image: node:latest\n  script:\n    - npm install\n    - npm run build\n\ntest:\n  stage: test\n  image: node:latest\n  script:\n    - npm run test\n\ndeploy:\n  stage: deploy\n  image: ruby:latest\n  script:\n    - gem install dpl\n    - dpl --provider=heroku --app=$HEROKU_APP_NAME --api-key=$HEROKU_API_KEY\n\n```\n\n**Deploying to different environments (staging and production)**\n\nGitLab also offers the idea of [Environments](https://docs.gitlab.com/ci/environments/) with CI/CD. This feature allows users to track deployments from CI/CD to infrastructure targets. In the example below, the pipeline adds stages with an environment property for a staging and production environment. While the deploy_staging stage will always run its script, the deploy_production stage requires manual approval to prevent accidental deployment to production.  \n\n```yaml\nstages:\n  - build\n  - test\n  - deploy_staging\n  - deploy_production\n\nbuild:\n  # ...\n\ntest:\n  # ...\n\ndeploy_staging:\n  stage: deploy_staging\n  script:\n    - echo \"Deploying to staging...\"\n  environment:\n    name: staging\n\ndeploy_production:\n  stage: deploy_production\n  script:\n    - echo \"Deploying to production...\"\n  environment:\n    name: production\n  when: manual  # Requires manual approval\n\n```\n\n### GitLab Auto DevOps\n\n[GitLab Auto DevOps](https://docs.gitlab.com/topics/autodevops/) simplifies CI/CD by providing a pre-defined configuration that automatically builds, tests, and deploys your applications. It leverages best practices and industry standards to streamline your workflow.\n\nTo enable Auto DevOps:\n\n1. Go to your project's **Settings > CI/CD > General pipelines**.  \n2. Enable the **Auto DevOps** option.\n\nAuto DevOps automatically detects your project's language and framework and configures the necessary build, test, and deployment stages. You don’t even need to create a `.gitlab-ci.yml` file.\n\n### CI/CD Catalog\n\nThe [CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/) is a list of projects with published [CI/CD components](https://docs.gitlab.com/ci/components/) you can use to extend your CI/CD workflow. Anyone can create a component project and add it to the CI/CD Catalog or contribute to an existing project to improve the available components. You can find published components in the [CI/CD Catalog](https://gitlab.com/explore/catalog) on GitLab.com.\n\n> [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n\n### CI templates\n\nYou can also create your own [CI templates](https://docs.gitlab.com/ci/examples/) to standardize and reuse CI/CD configurations across multiple projects. This promotes consistency and reduces duplication.\n\nTo create a CI template:\n\n1. Create a `.gitlab-ci.yml` file in a dedicated project or repository.  \n2. Define your CI/CD configuration in the template.  \n3. In your project's `.gitlab-ci.yml` file, use the `include` keyword to include the template.\n\n## Take your development to the next level\n\nGitLab CI/CD is a powerful tool that can transform your development workflow. By understanding the concepts of CI/CD, configuring your pipelines, and leveraging features like Auto DevOps, the CI/CD Catalog, and CI templates, you can automate your entire software development lifecycle and deliver high-quality software faster and more efficiently.\n\n> Want to take your learning to the next level? Sign up for [GitLab University courses](https://university.gitlab.com/). Or you can get going right away with a [free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/).\n\n## \"Getting Started with GitLab\" series\n\nCheck out more articles in our \"Getting Started with GitLab\" series:\n\n- [How to manage users](https://about.gitlab.com/blog/getting-started-with-gitlab-how-to-manage-users/)\n- [How to import your projects to GitLab](https://about.gitlab.com/blog/getting-started-with-gitlab-how-to-import-your-projects-to-gitlab/)  \n- [Mastering project management](https://about.gitlab.com/blog/getting-started-with-gitlab-mastering-project-management/)\n- [Automating Agile workflows with the gitlab-triage gem](https://about.gitlab.com/blog/automating-agile-workflows-with-the-gitlab-triage-gem/)\n- [Working with CI/CD variables](https://about.gitlab.com/blog/getting-started-with-gitlab-working-with-ci-cd-variables/)\n",{"title":5,"description":17,"ogTitle":5,"ogDescription":17,"noIndex":33,"ogImage":19,"ogUrl":34,"ogSiteName":35,"ogType":36,"canonicalUrls":34},false,"https://about.gitlab.com/blog/getting-started-with-gitlab-understanding-ci-cd","https://about.gitlab.com","article","en-us/blog/getting-started-with-gitlab-understanding-ci-cd",[39,40,41,42,11,25],"cicd","ci","cd","devsecops-platform",[21,22,23,24,11,25],"Yw2WfpuuP5aX3qgT7woh1Fp-e0-ud6OXqz0aYPqO38w",{"logo":46,"freeTrial":51,"sales":56,"login":61,"items":66,"search":385,"minimal":416,"duo":435,"switchNav":444,"pricingDeployment":455},{"config":47},{"href":48,"dataGaName":49,"dataGaLocation":50},"/","gitlab logo","header",{"text":52,"config":53},"Get free trial",{"href":54,"dataGaName":55,"dataGaLocation":50},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free 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foundation","Pair GitLab Duo Agent Platform with Amazon Bedrock for agentic software development and orchestration.","If your team runs GitLab and has a strong AWS practice, a new combination of Duo Agent Platform and Amazon Bedrock is just for you. The model is simple: GitLab acts as your orchestration layer to help accelerate your entire software lifecycle with agentic AI, and Bedrock is designed to provide a secure, compliant foundation model layer with AI inference behind the scenes.\n\nGitLab Duo Agent Platform enables you to handle planning, merge pipelines, security scanning, vulnerability remediation, and more as part of your GitLab workflows, while the GitLab AI Gateway routes model calls to Bedrock (or GitLab-managed Bedrock-backed endpoints, depending on your setup). That means you can build on the identity and access management (IAM) policies, virtual private cloud (VPC) boundaries, regional controls, and cloud spend commitments you already have in AWS.\n\nIf you already use Amazon Bedrock and want AI to help inside the work you already do in GitLab, not in yet another standalone chat tool, this is the pairing for you.\n\n\nIn this article, we look at the real problem many teams face today: AI is fragmented, data paths are fuzzy, and Bedrock investment gets underused when AI sits outside the software development lifecycle. Then we break down your deployment options for GitLab Duo Agent Platform:\n\n* Integrated with self-hosted models on Amazon Bedrock for GitLab Self-Managed deployments and self-hosted AI gateway   \n* Integrated with GitLab-operated models on Amazon Bedrock (with GitLab-owned keys) for GitLab Self-Managed deployments and GitLab-hosted AI gateway  \n* Integrated with GitLab-operated models on Amazon Bedrock (with GitLab-owned keys) for GitLab.com instances and GitLab-hosted AI gateway\n\nWe wrap with a summary on how this approach helps avoid shadow AI and point-tool sprawl without creating a parallel tech stack for AI tooling.\n\n## AI everywhere, control nowhere\n\nSomewhere in your company right now, software teams might be using an AI tool that your security team hasn't approved. Prompt data might be leaving your environment through a path no one has fully mapped. And your organization’s Amazon Bedrock investment might be underused while individual teams expense separate AI tools, pulling workloads and cloud spend away from the platforms you’ve already committed to.\n\nInstead of being a people problem, this might be an architecture problem. And it surfaces the same three constraints in nearly every enterprise:\n\n**Operational fragmentation.** Each team, or sometimes even an individual developer, picks their own development toolset, including AI tooling and model selection. That fragmentation makes end-to-end governance within the software development lifecycle nearly impossible.\n\n**Security and sovereignty.** Where does prompt and code data actually flow? Who owns the logs?\n\n**Cloud spend optimization.** Commitments to key cloud providers like AWS are diluted as workloads and AI usage drift to point tools outside of customers’ existing agreements.\n\nGitLab Duo Agent Platform and Amazon Bedrock help solve this together. The division of labor is straightforward: Duo Agent Platform owns the workflow orchestration with agentic AI for software development, Bedrock owns the inference layer and hosts approved foundational models, and your organization has full control over the data and policy boundaries you already defined in AWS. Three jobs, three owners, no fragmentation.\n\n## GitLab Duo Agent Platform: The agentic control plane\n\nGitLab Duo Agent Platform is GitLab's agentic AI layer: a framework of specialized agents and flows that operate simultaneously and in-parallel, going beyond the traditional stage-based handoffs  and helping automate work across the entire software lifecycle. Rather than a single assistant responding to prompts, Duo Agent Platform enables teams to orchestrate many AI agents asynchronously using unified data and project context, including issues, merge requests, pipelines, and security findings. Linear workflows are turned into coordinated, continuous collaboration between software teams and their AI agents, at scale.\n\nWith that control plane in place, the natural next question is which AI foundation should power these agents. For customers who run GitLab Self-Managed on AWS and need inference traffic, prompt data, and logs to also stay within their AWS environment along with their software lifecycle data, Amazon Bedrock acting as the AI inference layer is the natural fit. \n\n## Amazon Bedrock: The trusted AI foundation\n\nAmazon Bedrock is a fully managed, serverless foundation model layer that runs entirely within your AWS environment. Customer data stays in the customer's AWS account: inputs and outputs are encrypted in transit and at rest, never shared with model providers, and never used to train base models. Bedrock carries compliance certifications across GDPR, HIPAA, and FedRAMP High, covering many regulated industry requirements out of the box. Teams can also bring fine-tuned models from elsewhere via Custom Model Import and deploy them alongside native Bedrock models through the same infrastructure, without managing separate deployment pipelines. Bedrock Guardrails adds configurable safeguards across all models for content filtering, hallucination detection, and sensitive data protection.\n\nTogether, GitLab Duo Agent Platform and Bedrock consolidate DevSecOps orchestration and AI model governance, helping eliminate the fragmentation that happens when teams roll out AI tools independently.\n\n## Choosing your deployment path\n\nThe integration delivers the same core GitLab Duo Agent Platform capabilities regardless of how it is deployed. What varies is who runs GitLab, who operates the AI Gateway, and whose Bedrock account the inference runs through. The right pattern depends on where your organization already operates.\n\nAt a high level, the integration has three main components:\n\n* **GitLab Duo Agent Platform:** agentic workflows embedded across the software development lifecycle  \n* **AI Gateway (GitLab-managed or self-hosted):** the abstraction layer between Duo Agent Platform and the foundational model backend   \n* **Amazon Bedrock:** the AI model and inference substrate\n\n![Deployment of GitLab and AWS Bedrock](https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362365/udmvmv2efpmwtkxgydch.png)\n\nChoosing a deployment pattern is informed by where an organization wants to place the levers of control. The patterns below are designed to meet teams where they already are, whether that's SaaS-first, self-managed for compliance, or all-in on AWS with existing Bedrock investments.\n\n| Deployment Model | GitLab.com instance with GitLab-hosted AI Gateway with GitLab-operated Bedrock models   | GitLab Self-Managed with GitLab-hosted AI Gateway with GitLab-operated Bedrock models | GitLab Self-Managed  with self-hosted AI Gateway and customer-operated Bedrock models |\n| :---- | :---- | :---- | :---- |\n| **Ideal if you:** | Are primarily on GitLab.com and don’t want to self-host AI gateway and Bedrock models  | Need GitLab Self-Managed for compliance and operational reasons but don’t want to manage AI layer | Are AWS-centric with existing Bedrock usage and strict data/control needs  |\n| **Key Benefits** | Fastest, turnkey way to get Duo Agent Platform workflows: GitLab runs GitLab.com, the AI Gateway, integrated with Bedrock AI models. | Keep GitLab deployed in your own environment while consuming Bedrock models via a GitLab-managed AI Gateway, combining deployment control with simplified AI operations. | Run GitLab and AI Gateway in your AWS account, reuse existing IAM/VPC/regions, keep logs and data in your environment, and draw Bedrock usage from your existing AWS spend commitments. |\n\n## How customers use GitLab Duo Agent Platform with Amazon Bedrock\n\nPlatform teams can use GitLab Duo Agent Platform with Amazon Bedrock to standardize which models handle code suggestions, security analysis, and pipeline remediation. This helps enforce guardrails and logging centrally rather than letting individual teams adopt separate tools independently.\n\nSecurity workflows see particular benefit. GitLab Duo Agent Platform agents can propose and validate fixes for security findings within GitLab, helping reduce the manual triage work developers would otherwise handle outside the platform.\n\nFor enterprises already committed to AWS, routing AI workloads through Bedrock from within GitLab enables you to keep developer AI usage aligned with existing cloud agreements rather than generating separate, unplanned spend.\n\n## Closing the loop\n\nThe constraints that slow enterprise AI adoption are often not technical. They are organizational: fragmented tooling, ungoverned data flows, and cloud spend that never consolidates. Those are the problems that can stall AI programs even after the pilots succeed.\n\nGitLab Duo Agent Platform and Amazon Bedrock help address each one directly. Platform teams get consistent governance, auditability, and standardized paths for AI usage across the software development lifecycle. Development teams get streamlined, agentic workflows that feel native to GitLab. And AWS-centric organizations get to extend their existing Bedrock investment rather than build parallel AI infrastructure alongside it.\n\nThe result is an AI program that scales without fragmenting. Governance and velocity on the same stack, serving the same teams, under policies the organization already owns.\n\n\n> To explore which deployment pattern is right for your organization and how to align GitLab Duo Agent Platform and Amazon Bedrock with your existing AWS strategy, [contact the GitLab sales team](https://about.gitlab.com/sales/) and we’ll help you design and implement the best architecture for your environment. You can also [visit our AWS partner page](https://about.gitlab.com/partners/technology-partners/aws/) to learn more.",[292,756,757],"AWS","AI/ML","2026-04-21",[760,761],"Joe Mann","Mark Kriaf","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362275/ozbwn9tk0dditpnfddlz.png",{"featured":15,"template":13,"slug":764},"gitlab-amazon-platform-orchestration-on-a-trusted-ai-foundation",{"promotions":766},[767,781,792,804],{"id":768,"categories":769,"header":771,"text":772,"button":773,"image":778},"ai-modernization",[770],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":774,"config":775},"Get your AI maturity score",{"href":776,"dataGaName":777,"dataGaLocation":255},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":782,"categories":783,"header":784,"text":772,"button":785,"image":789},"devops-modernization",[11,583],"Are you just managing tools or shipping innovation?",{"text":786,"config":787},"Get your DevOps maturity score",{"href":788,"dataGaName":777,"dataGaLocation":255},"/assessments/devops-modernization-assessment/",{"config":790},{"src":791},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":793,"categories":794,"header":796,"text":772,"button":797,"image":801},"security-modernization",[795],"security","Are you trading speed for security?",{"text":798,"config":799},"Get your security maturity score",{"href":800,"dataGaName":777,"dataGaLocation":255},"/assessments/security-modernization-assessment/",{"config":802},{"src":803},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":805,"paths":806,"header":809,"text":810,"button":811,"image":816},"github-azure-migration",[807,808],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":812,"config":813},"See how GitLab compares to GitHub",{"href":814,"dataGaName":815,"dataGaLocation":255},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":817},{"src":791},{"header":819,"blurb":820,"button":821,"secondaryButton":826},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":822,"config":823},"Get your free trial",{"href":824,"dataGaName":55,"dataGaLocation":825},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":521,"config":827},{"href":59,"dataGaName":60,"dataGaLocation":825},1777934932721]