[{"data":1,"prerenderedAt":829},["ShallowReactive",2],{"/en-us/blog/getting-started-with-gitlab-working-with-ci-cd-variables":3,"navigation-en-us":44,"banner-en-us":464,"footer-en-us":474,"blog-post-authors-en-us-GitLab Team":715,"blog-related-posts-en-us-getting-started-with-gitlab-working-with-ci-cd-variables":731,"blog-promotions-en-us":766,"next-steps-en-us":819},{"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":42,"template":13,"updatedDate":28,"__hash__":43},"blogPosts/en-us/blog/getting-started-with-gitlab-working-with-ci-cd-variables.md","Getting started with GitLab: Working with CI/CD variables",[7],"gitlab-team",[9],"GitLab Team","*Welcome to our \"Getting started with GitLab\" series, where we help newcomers get familiar with the GitLab DevSecOps platform.*\n\nIn an earlier article, we explored [GitLab CI/CD](https://about.gitlab.com/blog/getting-started-with-gitlab-understanding-ci-cd/). Now, let's dive deeper into the world of **CI/CD variables** and unlock their full potential.\n\n### What are CI/CD variables?\n\nCI/CD variables are dynamic key-value pairs that you can define at different levels within your GitLab environment (e.g., project, group, or instance). These variables act as placeholders for values that you can use in your `.gitlab-ci.yml` file to customize your pipelines, securely store sensitive information, and make your CI/CD configuration more maintainable.\n\n### Why are CI/CD variables important?\n\nCI/CD variables offer numerous benefits:\n\n* **Flexibility** - Easily adapt your pipelines to different environments, configurations, or deployment targets without modifying your core CI/CD script.  \n* **Security** - Securely store sensitive information like API keys, passwords, and tokens, preventing them from being exposed directly in your code.  \n* **Maintainability** - Keep your CI/CD configuration clean and organized by centralizing values in variables, making updates and modifications easier.  \n* **Reusability** - Define variables once and reuse them across multiple projects, promoting consistency and reducing duplication.\n\n### Scopes of CI/CD variables: Project, group, and instance\n\nGitLab allows you to define CI/CD variables with different scopes, controlling their visibility and accessibility:\n\n* **Project-level variables** - These variables are specific to a single project and are ideal for storing project-specific settings, such as:\n  * Deployment URLs: Define different URLs for staging and production environments.  \n  * Database credentials: Store database connection details for testing or deployment.  \n  * Feature flags: Enable or disable features during different stages of your pipeline.  \n  * Example: You have a project called \"MyWebApp\" and want to store the production deployment URL. You create a project-level variable named `DPROD_DEPLOY_URL` with the value `https://mywebapp.com`.  \n* **Group-level variables** - These variables are shared across all projects within a GitLab group. They are useful for settings that are common to multiple projects, such as:\n\n  * API keys for shared services: Store API keys for services like AWS, Google Cloud, or Docker Hub that are used by multiple projects within the group.  \n  * Global configuration settings: Define common configuration parameters that apply to all projects in the group.  \n  * Example: You have a group called \"Web Apps\" and want to store an API key for Docker Hub. You create a group-level variable named `DOCKER_HUB_API_KEY` with the corresponding API key value.  \n* **Instance-level variables** - These variables are available to all projects on a GitLab instance. They are typically used for global settings that apply across an entire organization such as:\n\n  * Default runner registration token: Provide a default token for registering new [runners](https://docs.gitlab.com/runner/).  \n  * License information: Store license keys for GitLab features or third-party tools.  \n  * Global environment settings: Define environment variables that should be available to all projects.  \n  * Example: You want to set a default Docker image for all projects on your GitLab instance. You create an instance-level variable named `DEFAULT_DOCKER_IMAGE` with the value `ubuntu:latest`.\n\n### Defining CI/CD variables\n\nTo define a CI/CD variable:\n\n1. Click on the **Settings > CI/CD** buttons for  your project, group, or instance.  \n2. Go to the **Variables** section.  \n3. Click **Add variable**.  \n4. Enter the **key** (e.g., `API_KEY`) and **value**.  \n5. Optionally, check the **Protect variable** box for sensitive information. This ensures that the variable is only available to pipelines running on protected branches or tags.  \n6. Optionally, check the **Mask variable** box to hide the variable's value from job logs, preventing accidental exposure.  \n7. Click **Save variable**.\n\n### Using CI/CD variables\n\nTo use a CI/CD variable in your `.gitlab-ci.yml` file, simply prefix the variable name with `$`:\n\n```yaml\ndeploy_job:\n  script:\n    - echo \"Deploying to production...\"\n    - curl -H \"Authorization: Bearer $API_KEY\" https://api.example.com/deploy\n\n```\n\n### Predefined CI/CD variables\n\nGitLab provides a set of [predefined CI/CD variables](https://docs.gitlab.com/ci/variables/predefined_variables/) that you can use in your pipelines. These variables provide information about the current pipeline, job, project, and more.\n\nSome commonly used predefined variables include:\n\n* `$CI_COMMIT_SHA`: The commit SHA of the current pipeline.  \n* `$CI_PROJECT_DIR`: The directory where the project is cloned.  \n* `$CI_PIPELINE_ID`: The ID of the current pipeline.  \n* `$CI_ENVIRONMENT_NAME`: The name of the environment being deployed to (if applicable).\n\n### Best practices\n\n* Securely manage sensitive variables: Use protected and masked variables for API keys, passwords, and other sensitive information.  \n* Avoid hardcoding values: Use variables to store configuration values, making your pipelines more flexible and maintainable.  \n* Organize your variables: Use descriptive names and group related variables together for better organization.  \n* Use the appropriate scope: Choose the correct scope (project, group, or instance) for your variables based on their intended use and visibility.\n\n### Unlock the power of variables\n\nCI/CD variables are a powerful tool for customizing and securing your GitLab pipelines. 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Now, let's dive deeper into the world of **CI/CD variables** and unlock their full potential.\n\n### What are CI/CD variables?\n\nCI/CD variables are dynamic key-value pairs that you can define at different levels within your GitLab environment (e.g., project, group, or instance). These variables act as placeholders for values that you can use in your `.gitlab-ci.yml` file to customize your pipelines, securely store sensitive information, and make your CI/CD configuration more maintainable.\n\n### Why are CI/CD variables important?\n\nCI/CD variables offer numerous benefits:\n\n* **Flexibility** - Easily adapt your pipelines to different environments, configurations, or deployment targets without modifying your core CI/CD script.  \n* **Security** - Securely store sensitive information like API keys, passwords, and tokens, preventing them from being exposed directly in your code.  \n* **Maintainability** - Keep your CI/CD configuration clean and organized by centralizing values in variables, making updates and modifications easier.  \n* **Reusability** - Define variables once and reuse them across multiple projects, promoting consistency and reducing duplication.\n\n### Scopes of CI/CD variables: Project, group, and instance\n\nGitLab allows you to define CI/CD variables with different scopes, controlling their visibility and accessibility:\n\n* **Project-level variables** - These variables are specific to a single project and are ideal for storing project-specific settings, such as:\n  * Deployment URLs: Define different URLs for staging and production environments.  \n  * Database credentials: Store database connection details for testing or deployment.  \n  * Feature flags: Enable or disable features during different stages of your pipeline.  \n  * Example: You have a project called \"MyWebApp\" and want to store the production deployment URL. You create a project-level variable named `DPROD_DEPLOY_URL` with the value `https://mywebapp.com`.  \n* **Group-level variables** - These variables are shared across all projects within a GitLab group. They are useful for settings that are common to multiple projects, such as:\n\n  * API keys for shared services: Store API keys for services like AWS, Google Cloud, or Docker Hub that are used by multiple projects within the group.  \n  * Global configuration settings: Define common configuration parameters that apply to all projects in the group.  \n  * Example: You have a group called \"Web Apps\" and want to store an API key for Docker Hub. You create a group-level variable named `DOCKER_HUB_API_KEY` with the corresponding API key value.  \n* **Instance-level variables** - These variables are available to all projects on a GitLab instance. They are typically used for global settings that apply across an entire organization such as:\n\n  * Default runner registration token: Provide a default token for registering new [runners](https://docs.gitlab.com/runner/).  \n  * License information: Store license keys for GitLab features or third-party tools.  \n  * Global environment settings: Define environment variables that should be available to all projects.  \n  * Example: You want to set a default Docker image for all projects on your GitLab instance. You create an instance-level variable named `DEFAULT_DOCKER_IMAGE` with the value `ubuntu:latest`.\n\n### Defining CI/CD variables\n\nTo define a CI/CD variable:\n\n1. Click on the **Settings > CI/CD** buttons for  your project, group, or instance.  \n2. Go to the **Variables** section.  \n3. Click **Add variable**.  \n4. Enter the **key** (e.g., `API_KEY`) and **value**.  \n5. Optionally, check the **Protect variable** box for sensitive information. This ensures that the variable is only available to pipelines running on protected branches or tags.  \n6. Optionally, check the **Mask variable** box to hide the variable's value from job logs, preventing accidental exposure.  \n7. Click **Save variable**.\n\n### Using CI/CD variables\n\nTo use a CI/CD variable in your `.gitlab-ci.yml` file, simply prefix the variable name with `$`:\n\n```yaml\ndeploy_job:\n  script:\n    - echo \"Deploying to production...\"\n    - curl -H \"Authorization: Bearer $API_KEY\" https://api.example.com/deploy\n\n```\n\n### Predefined CI/CD variables\n\nGitLab provides a set of [predefined CI/CD variables](https://docs.gitlab.com/ci/variables/predefined_variables/) that you can use in your pipelines. These variables provide information about the current pipeline, job, project, and more.\n\nSome commonly used predefined variables include:\n\n* `$CI_COMMIT_SHA`: The commit SHA of the current pipeline.  \n* `$CI_PROJECT_DIR`: The directory where the project is cloned.  \n* `$CI_PIPELINE_ID`: The ID of the current pipeline.  \n* `$CI_ENVIRONMENT_NAME`: The name of the environment being deployed to (if applicable).\n\n### Best practices\n\n* Securely manage sensitive variables: Use protected and masked variables for API keys, passwords, and other sensitive information.  \n* Avoid hardcoding values: Use variables to store configuration values, making your pipelines more flexible and maintainable.  \n* Organize your variables: Use descriptive names and group related variables together for better organization.  \n* Use the appropriate scope: Choose the correct scope (project, group, or instance) for your variables based on their intended use and visibility.\n\n### Unlock the power of variables\n\nCI/CD variables are a powerful tool for customizing and securing your GitLab pipelines. 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gem](https://about.gitlab.com/blog/automating-agile-workflows-with-the-gitlab-triage-gem/)\n- [Understanding CI/CD](https://about.gitlab.com/blog/getting-started-with-gitlab-understanding-ci-cd/)\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-working-with-ci-cd-variables","https://about.gitlab.com","article","en-us/blog/getting-started-with-gitlab-working-with-ci-cd-variables",[11,21,39,40,41,25],"ci","cd","cicd",[11,21,22,23,24,25],"TML4Vv4GVG0uSYRypFK76enkuE7AywkYYq40S7uamSc",{"logo":45,"freeTrial":50,"sales":55,"login":60,"items":65,"search":384,"minimal":415,"duo":434,"switchNav":443,"pricingDeployment":454},{"config":46},{"href":47,"dataGaName":48,"dataGaLocation":49},"/","gitlab logo","header",{"text":51,"config":52},"Get free 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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. 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