[{"data":1,"prerenderedAt":827},["ShallowReactive",2],{"/en-us/blog/data-driven-devsecops-exploring-gitlab-insights-dashboards":3,"navigation-en-us":44,"banner-en-us":464,"footer-en-us":474,"blog-post-authors-en-us-Ricardo Amarilla Villalba":714,"blog-related-posts-en-us-data-driven-devsecops-exploring-gitlab-insights-dashboards":729,"blog-promotions-en-us":764,"next-steps-en-us":817},{"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/data-driven-devsecops-exploring-gitlab-insights-dashboards.md","Data-driven DevSecOps: Exploring GitLab Insights Dashboards",[7],"ricardo-amarilla-villalba",[9],"Ricardo Amarilla Villalba","Metrics and analytics play a crucial role in driving productivity, quality, and success. GitLab, as a comprehensive DevSecOps platform, offers powerful tools for tracking and visualizing these vital metrics through its Insights Dashboards. In this article, you'll learn how to use the Insights Dashboards in your environment.\n\n## Introduction to GitLab metrics and analytics \n\nGitLab provides an array of metrics and analytics tools that cover various aspects of the DevSecOps lifecycle:\n\n1. [Productivity Analytics](https://docs.gitlab.com/user/analytics/productivity_analytics/): Track team velocity, cycle time, and lead time.  \n2. [Code Review Analytics](https://docs.gitlab.com/user/analytics/code_review_analytics/): Measure code quality, test coverage, and review efficiency.  \n3. [CI/CD Analytics](https://docs.gitlab.com/user/analytics/ci_cd_analytics/): Monitor pipeline performance and deployment frequency.  \n4. [Value Stream Analytics](https://docs.gitlab.com/user/group/value_stream_analytics/): Visualize the flow of work from idea to production.  \n5. [Insights](https://docs.gitlab.com/user/project/insights/): Explore and visualize data about your projects and groups.\n\nThese metrics offer invaluable insights into your development process, helping teams identify bottlenecks, optimize workflows, and make data-driven decisions.\n\n## Leveraging labels for specific metrics\n\nOne of GitLab's most powerful, yet understated features, is Labels, which allows you to filter and focus on specific metrics with pinpoint accuracy. By strategically applying labels to issues, merge requests, and epics, you can create custom views that provide targeted insights into your project's performance and progress.\n\nLabels in GitLab act as versatile identifiers, allowing you to categorize and organize your work items with great flexibility. Whether you're tracking feature development, bug fixes, or team-specific tasks, labels enable you to slice and dice your project data in ways that reveal meaningful patterns and trends. This concept parallels the use of tags in cloud deployments, where resources are labeled for easier management, cost allocation, and operational insights.\n\nBy thoughtfully labeling your work items, you're essentially creating a sophisticated labeling system that can be leveraged to generate custom dashboards and reports. This approach empowers you to zoom in on the metrics that matter most to your team or stakeholders, providing a clear and focused view of your project's health and momentum.\n\n## How to configure GitLab Insights\n\nGitLab Insights allow you to explore and visualize data about your projects and groups. They provide valuable analytics on various aspects such as issues created and closed during a specified period, average time for merge requests to be merged, and triage hygiene. Insights can be configured for both projects and groups.\n\nTo configure Insights:\n\n1. For project insights:  \n   * Create a file named `.gitlab/insights.yml` in the root directory of your project.  \n2. For group insights:  \n   * Create a `.gitlab/insights.yml` file in a project that belongs to your group.  \n   * Go to your group's **Settings > General**.  \n   * Expand the **Analytics section** and find the **Insights section**.  \n   * Select the project containing the configuration file and save changes.\n\nThe `.gitlab/insights.yml` file is a YAML file where you define the structure and order of charts in a report, as well as the style of charts to be displayed. Each chart definition includes parameters such as title, description, type, and query to specify the data source and filtering conditions.\n\nTo view insights, navigate to **Analyze > Insights** in your project or group.\n\n![View default Insights Dashboard](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097218/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097217972.png)\n\n## Customize merge request insights\n\nWhile the default view provides valuable raw information, we can customize the Insights Dashboard to uncover additional layers of information, such as which team was responsible for each merge request and what type of problem each one solved.\n\n## Merge request insights for each squad and requirement type\n\nMeasuring squad productivity in GitLab can be challenging, especially when the GitLab group and subgroup structure doesn't align perfectly with your squad organization. Here's how to overcome these challenges and effectively track squad productivity:\n\n### **Setting up squad-based metrics**\n\n1. **Label creation:** Create unique scope labels for each squad (e.g., `squad::alpha`, `squad::beta`) and each requirement type (e.g., `type::bug`, `type::feature`, `type::maintenance`).\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/ZUOzORIUJeU?si=T8eHeGizS3blYFHB\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n2. **Label application:** Consistently apply these squad labels to all issues and merge requests handled by each squad, regardless of the project or group they're in.  \n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/fJ9entEBZG8?si=MlM6mKirEdkmwDDJ\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n**Hints:**  \n   * Use GitLab API to apply labels massively to existing open, merged, and closed MRs.  \n   * Add/remove/update labels as part of your GitLab CI pipeline.  \n   * Leverage the GitLab Triage Bot to automate the labeling process.  \n\n3. Dashboard setup: Create a `.gitlab/insights.yml` file in your project repository with custom charts for team-specific and type-specific merge request insights.\n\n```yaml\n## Default Merge Requests insights.yml \nmergeRequests:\n  title: Merge requests dashboard\n  charts:\n    - title: Merge requests merged per week \n      type: bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          group_by: week\n          period_limit: 12\n    - title: Merge requests merged per month\n      type: bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          group_by: month\n          period_limit: 3\n\n## Per-teams Merge Requests insights.yml\nmergeRequestsTeams:\n  title: Merge requests dashboard per teams\n  charts:\n    - title: Merge requests merged per week \n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          group_by: week\n          period_limit: 12\n          collection_labels:\n            - squad::alpha\n            - squad::beta\n    - title: Merge requests merged per month\n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          group_by: month\n          period_limit: 3\n          collection_labels:\n            - squad::alpha\n            - squad::beta\n\n## Per-teams and Type Merge Requests insights.yml\nmergeRequestsTeamsAndType:\n  title: Per Teams and Type - Merge requests dashboard\n  charts:\n    - title: Merge requests merged per week - Squad Alpha\n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          filter_labels: squad::alpha\n          collection_labels:\n            - type::feature\n            - type::bug\n            - type::maintenance\n          group_by: week\n          period_limit: 12\n    - title: Merge requests merged per month - Squad Alpha\n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          filter_labels: squad::alpha\n          collection_labels:\n            - type::feature\n            - type::bug\n            - type::maintenance\n          group_by: month\n          period_limit: 3\n    - title: Merge requests merged per week - Squad Beta\n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          filter_labels: squad::beta\n          collection_labels:\n            - type::feature\n            - type::bug\n            - type::maintenance\n          group_by: week\n          period_limit: 12\n    - title: Merge requests merged per month - Squad Beta\n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          filter_labels: squad::beta\n          collection_labels:\n            - type::feature\n            - type::bug\n            - type::maintenance\n          group_by: month\n          period_limit: 3\n\n```\n\nBy implementing these customizations, you can create insightful dashboards that provide a clear view of merge request activity per team and requirement type, allowing you to visualize trends over time, compare performance between squads, and analyze the distribution of different types of work for each squad. \n\n![dashboards with view of MR activity per team and requirement type](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097218/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097217972.png)\n\n![dashboard comparing performance between squads](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097218/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750097217974.png)\n\n## Get started today\n\nGitLab Insights is just the tip of the iceberg when it comes to metrics and analytics. To explore the full range of GitLab's powerful analytics features, including Value Stream Analytics, CI/CD Analytics, and Code Review metrics, check out our Value Stream Management product tour:\n\n[![Value Stream Management product tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097218/Blog/Content%20Images/Blog/Content%20Images/Screenshot_2024-11-20_at_12.28.08_PM_aHR0cHM6_1750097217976.png)](https://gitlab.navattic.com/vsm)\n\n> Ready to start your own metrics journey? Sign up for a [free trial of GitLab Ultimate today](https://gitlab.com/-/trials/new?glm_content=default-saas-trial&glm_source=about.gitlab.com%2F) and unlock the full potential of data-driven DevSecOps.\n\n## Read more\n- [Scheduled Reports Generation tool simplifies value stream management](https://about.gitlab.com/blog/new-scheduled-reports-generation-tool-simplifies-value-stream-management/)\n- [Getting started with the new GitLab Value Streams Dashboard](https://about.gitlab.com/blog/getting-started-with-value-streams-dashboard/)\n- [AI Impact analytics dashboard measures the ROI of AI](https://about.gitlab.com/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai/)","product",{"template":13,"slug":14,"featured":15},"BlogPost","data-driven-devsecops-exploring-gitlab-insights-dashboards",true,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":26,"body":10},"Learn how to leverage GitLab Insights Dashboards to visualize key metrics, track project progress, and boost team productivity with customizable, data-driven views.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097210/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2811%29_78Dav6FR9EGjhebHWuBVan_1750097210214.png",[21,22,11,23,24,25],"CI/CD","DevSecOps platform","features","tutorial","solutions architecture","2024-11-20","md",null,{},"/en-us/blog/data-driven-devsecops-exploring-gitlab-insights-dashboards","---\nseo:\n  title: 'Data-driven DevSecOps: Exploring GitLab Insights Dashboards'\n  description: >-\n    Learn how to leverage GitLab Insights Dashboards to visualize key metrics,\n    track project progress, and boost team productivity with customizable,\n    data-driven views.\n  ogTitle: 'Data-driven DevSecOps: Exploring GitLab Insights Dashboards'\n  ogDescription: >-\n    Learn how to leverage GitLab Insights Dashboards to visualize key metrics,\n    track project progress, and boost team productivity with customizable,\n    data-driven views.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097210/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2811%29_78Dav6FR9EGjhebHWuBVan_1750097210214.png\n  ogUrl: >-\n    https://about.gitlab.com/blog/data-driven-devsecops-exploring-gitlab-insights-dashboards\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: >-\n    https://about.gitlab.com/blog/data-driven-devsecops-exploring-gitlab-insights-dashboards\ntitle: 'Data-driven DevSecOps: Exploring GitLab Insights Dashboards'\ndescription: Learn how to leverage GitLab Insights Dashboards to visualize key metrics, track project progress, and boost team productivity with customizable, data-driven views.\nauthors:\n  - Ricardo Amarilla Villalba\nheroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097210/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2811%29_78Dav6FR9EGjhebHWuBVan_1750097210214.png\ntags:\n  - CI/CD\n  - DevSecOps platform\n  - product\n  - features\n  - tutorial\n  - solutions architecture\ncategory: product\ndate: '2024-11-20'\nslug: data-driven-devsecops-exploring-gitlab-insights-dashboards\nfeatured: true\ntemplate: BlogPost\n---\n\nMetrics and analytics play a crucial role in driving productivity, quality, and success. GitLab, as a comprehensive DevSecOps platform, offers powerful tools for tracking and visualizing these vital metrics through its Insights Dashboards. In this article, you'll learn how to use the Insights Dashboards in your environment.\n\n## Introduction to GitLab metrics and analytics \n\nGitLab provides an array of metrics and analytics tools that cover various aspects of the DevSecOps lifecycle:\n\n1. [Productivity Analytics](https://docs.gitlab.com/user/analytics/productivity_analytics/): Track team velocity, cycle time, and lead time.  \n2. [Code Review Analytics](https://docs.gitlab.com/user/analytics/code_review_analytics/): Measure code quality, test coverage, and review efficiency.  \n3. [CI/CD Analytics](https://docs.gitlab.com/user/analytics/ci_cd_analytics/): Monitor pipeline performance and deployment frequency.  \n4. [Value Stream Analytics](https://docs.gitlab.com/user/group/value_stream_analytics/): Visualize the flow of work from idea to production.  \n5. [Insights](https://docs.gitlab.com/user/project/insights/): Explore and visualize data about your projects and groups.\n\nThese metrics offer invaluable insights into your development process, helping teams identify bottlenecks, optimize workflows, and make data-driven decisions.\n\n## Leveraging labels for specific metrics\n\nOne of GitLab's most powerful, yet understated features, is Labels, which allows you to filter and focus on specific metrics with pinpoint accuracy. By strategically applying labels to issues, merge requests, and epics, you can create custom views that provide targeted insights into your project's performance and progress.\n\nLabels in GitLab act as versatile identifiers, allowing you to categorize and organize your work items with great flexibility. Whether you're tracking feature development, bug fixes, or team-specific tasks, labels enable you to slice and dice your project data in ways that reveal meaningful patterns and trends. This concept parallels the use of tags in cloud deployments, where resources are labeled for easier management, cost allocation, and operational insights.\n\nBy thoughtfully labeling your work items, you're essentially creating a sophisticated labeling system that can be leveraged to generate custom dashboards and reports. This approach empowers you to zoom in on the metrics that matter most to your team or stakeholders, providing a clear and focused view of your project's health and momentum.\n\n## How to configure GitLab Insights\n\nGitLab Insights allow you to explore and visualize data about your projects and groups. They provide valuable analytics on various aspects such as issues created and closed during a specified period, average time for merge requests to be merged, and triage hygiene. Insights can be configured for both projects and groups.\n\nTo configure Insights:\n\n1. For project insights:  \n   * Create a file named `.gitlab/insights.yml` in the root directory of your project.  \n2. For group insights:  \n   * Create a `.gitlab/insights.yml` file in a project that belongs to your group.  \n   * Go to your group's **Settings > General**.  \n   * Expand the **Analytics section** and find the **Insights section**.  \n   * Select the project containing the configuration file and save changes.\n\nThe `.gitlab/insights.yml` file is a YAML file where you define the structure and order of charts in a report, as well as the style of charts to be displayed. Each chart definition includes parameters such as title, description, type, and query to specify the data source and filtering conditions.\n\nTo view insights, navigate to **Analyze > Insights** in your project or group.\n\n![View default Insights Dashboard](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097218/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097217972.png)\n\n## Customize merge request insights\n\nWhile the default view provides valuable raw information, we can customize the Insights Dashboard to uncover additional layers of information, such as which team was responsible for each merge request and what type of problem each one solved.\n\n## Merge request insights for each squad and requirement type\n\nMeasuring squad productivity in GitLab can be challenging, especially when the GitLab group and subgroup structure doesn't align perfectly with your squad organization. Here's how to overcome these challenges and effectively track squad productivity:\n\n### **Setting up squad-based metrics**\n\n1. **Label creation:** Create unique scope labels for each squad (e.g., `squad::alpha`, `squad::beta`) and each requirement type (e.g., `type::bug`, `type::feature`, `type::maintenance`).\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/ZUOzORIUJeU?si=T8eHeGizS3blYFHB\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n2. **Label application:** Consistently apply these squad labels to all issues and merge requests handled by each squad, regardless of the project or group they're in.  \n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/fJ9entEBZG8?si=MlM6mKirEdkmwDDJ\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n**Hints:**  \n   * Use GitLab API to apply labels massively to existing open, merged, and closed MRs.  \n   * Add/remove/update labels as part of your GitLab CI pipeline.  \n   * Leverage the GitLab Triage Bot to automate the labeling process.  \n\n3. Dashboard setup: Create a `.gitlab/insights.yml` file in your project repository with custom charts for team-specific and type-specific merge request insights.\n\n```yaml\n## Default Merge Requests insights.yml \nmergeRequests:\n  title: Merge requests dashboard\n  charts:\n    - title: Merge requests merged per week \n      type: bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          group_by: week\n          period_limit: 12\n    - title: Merge requests merged per month\n      type: bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          group_by: month\n          period_limit: 3\n\n## Per-teams Merge Requests insights.yml\nmergeRequestsTeams:\n  title: Merge requests dashboard per teams\n  charts:\n    - title: Merge requests merged per week \n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          group_by: week\n          period_limit: 12\n          collection_labels:\n            - squad::alpha\n            - squad::beta\n    - title: Merge requests merged per month\n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          group_by: month\n          period_limit: 3\n          collection_labels:\n            - squad::alpha\n            - squad::beta\n\n## Per-teams and Type Merge Requests insights.yml\nmergeRequestsTeamsAndType:\n  title: Per Teams and Type - Merge requests dashboard\n  charts:\n    - title: Merge requests merged per week - Squad Alpha\n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          filter_labels: squad::alpha\n          collection_labels:\n            - type::feature\n            - type::bug\n            - type::maintenance\n          group_by: week\n          period_limit: 12\n    - title: Merge requests merged per month - Squad Alpha\n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          filter_labels: squad::alpha\n          collection_labels:\n            - type::feature\n            - type::bug\n            - type::maintenance\n          group_by: month\n          period_limit: 3\n    - title: Merge requests merged per week - Squad Beta\n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          filter_labels: squad::beta\n          collection_labels:\n            - type::feature\n            - type::bug\n            - type::maintenance\n          group_by: week\n          period_limit: 12\n    - title: Merge requests merged per month - Squad Beta\n      type: stacked-bar\n      query:\n        data_source: issuables\n        params:\n          issuable_type: merge_request\n          issuable_state: merged\n          filter_labels: squad::beta\n          collection_labels:\n            - type::feature\n            - type::bug\n            - type::maintenance\n          group_by: month\n          period_limit: 3\n\n```\n\nBy implementing these customizations, you can create insightful dashboards that provide a clear view of merge request activity per team and requirement type, allowing you to visualize trends over time, compare performance between squads, and analyze the distribution of different types of work for each squad. \n\n![dashboards with view of MR activity per team and requirement type](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097218/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097217972.png)\n\n![dashboard comparing performance between squads](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097218/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750097217974.png)\n\n## Get started today\n\nGitLab Insights is just the tip of the iceberg when it comes to metrics and analytics. To explore the full range of GitLab's powerful analytics features, including Value Stream Analytics, CI/CD Analytics, and Code Review metrics, check out our Value Stream Management product tour:\n\n[![Value Stream Management product tour](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097218/Blog/Content%20Images/Blog/Content%20Images/Screenshot_2024-11-20_at_12.28.08_PM_aHR0cHM6_1750097217976.png)](https://gitlab.navattic.com/vsm)\n\n> Ready to start your own metrics journey? Sign up for a [free trial of GitLab Ultimate today](https://gitlab.com/-/trials/new?glm_content=default-saas-trial&glm_source=about.gitlab.com%2F) and unlock the full potential of data-driven DevSecOps.\n\n## Read more\n- [Scheduled Reports Generation tool simplifies value stream management](https://about.gitlab.com/blog/new-scheduled-reports-generation-tool-simplifies-value-stream-management/)\n- [Getting started with the new GitLab Value Streams Dashboard](https://about.gitlab.com/blog/getting-started-with-value-streams-dashboard/)\n- [AI Impact analytics dashboard measures the ROI of AI](https://about.gitlab.com/blog/developing-gitlab-duo-ai-impact-analytics-dashboard-measures-the-roi-of-ai/)\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/data-driven-devsecops-exploring-gitlab-insights-dashboards","https://about.gitlab.com","article","en-us/blog/data-driven-devsecops-exploring-gitlab-insights-dashboards",[39,40,11,23,24,41],"cicd","devsecops-platform","solutions-architecture",[21,22,11,23,24,25],"yVAfRC7DXFXLrUmQ0UOGbQpSmrj3_BtCtSNQVYUCAYU",{"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). 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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.",[291,755,756],"AWS","AI/ML","2026-04-21",[759,760],"Joe Mann","Mark Kriaf","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362275/ozbwn9tk0dditpnfddlz.png",{"featured":15,"template":13,"slug":763},"gitlab-amazon-platform-orchestration-on-a-trusted-ai-foundation",{"promotions":765},[766,780,791,803],{"id":767,"categories":768,"header":770,"text":771,"button":772,"image":777},"ai-modernization",[769],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":773,"config":774},"Get your AI maturity score",{"href":775,"dataGaName":776,"dataGaLocation":254},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":781,"categories":782,"header":783,"text":771,"button":784,"image":788},"devops-modernization",[11,582],"Are you just managing tools or shipping innovation?",{"text":785,"config":786},"Get your DevOps maturity score",{"href":787,"dataGaName":776,"dataGaLocation":254},"/assessments/devops-modernization-assessment/",{"config":789},{"src":790},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":792,"categories":793,"header":795,"text":771,"button":796,"image":800},"security-modernization",[794],"security","Are you trading speed for security?",{"text":797,"config":798},"Get your security maturity score",{"href":799,"dataGaName":776,"dataGaLocation":254},"/assessments/security-modernization-assessment/",{"config":801},{"src":802},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":804,"paths":805,"header":808,"text":809,"button":810,"image":815},"github-azure-migration",[806,807],"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":811,"config":812},"See how GitLab compares to GitHub",{"href":813,"dataGaName":814,"dataGaLocation":254},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":816},{"src":790},{"header":818,"blurb":819,"button":820,"secondaryButton":825},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":821,"config":822},"Get your free trial",{"href":823,"dataGaName":54,"dataGaLocation":824},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":520,"config":826},{"href":58,"dataGaName":59,"dataGaLocation":824},1777934929875]