[{"data":1,"prerenderedAt":824},["ShallowReactive",2],{"/en-us/blog/5-gitlab-premium-features-to-help-your-team-scale":3,"navigation-en-us":39,"banner-en-us":460,"footer-en-us":470,"blog-post-authors-en-us-Julie Griffin":711,"blog-related-posts-en-us-5-gitlab-premium-features-to-help-your-team-scale":726,"blog-promotions-en-us":761,"next-steps-en-us":814},{"id":4,"title":5,"authorSlugs":6,"authors":8,"body":10,"category":11,"categorySlug":11,"config":12,"content":16,"date":23,"description":17,"extension":24,"externalUrl":25,"featured":15,"heroImage":19,"isFeatured":15,"meta":26,"navigation":27,"path":28,"publishedDate":23,"rawbody":29,"seo":30,"slug":14,"stem":34,"tagSlugs":35,"tags":37,"template":13,"updatedDate":25,"__hash__":38},"blogPosts/en-us/blog/5-gitlab-premium-features-to-help-your-team-scale.md","5 GitLab Premium features to help your team scale",[7],"julie-griffin",[9],"Julie Griffin","As development teams grow, what once worked for a small team often becomes a bottleneck. Code standards become inconsistent, operational silos develop, and technical debt accumulates faster. What was a well-oiled machine is now dysfunctional as more team members, projects, and tools are added on.\n\nMany teams experience these challenges as they grow, but how you handle and address these growing pains can save you time, energy, and money in the long run. In this article, we’ll explore the common pitfalls growing teams face and how successful organizations address them.\n\n## 1. Consistent code quality\n\nOne of the challenges growing teams face is [maintaining consistent code quality](https://about.gitlab.com/blog/transform-code-quality-and-compliance-with-automated-processes/) as more developers contribute to the codebase. Quality issues that were once caught quickly now take longer to identify and fix.\n\nSuccessful teams address these challenges through automated code analysis throughout their development workflow. Instead of relying solely on manual reviews, they implement systems to identify potential issues and enforce consistent standards before code even reaches human reviewers. This approach helps detect complexity issues early and flags potential security vulnerabilities, allowing reviewers to focus on more strategic aspects of code review.\n\n### Features that maintain consistent code quality\n\n* Start by automating code analysis in your workflow. With GitLab Premium, you can set up [Code Quality Reports](https://docs.gitlab.com/ci/testing/code_quality/) in your merge requests. This helps catch issues early by analyzing code complexity and quality before review begins. For example, when a developer submits changes that might increase technical debt, the report will flag these issues automatically.\n* Next, establish automated quality standards. Configure Quality Gates to define what \"good code\" means for your team. This could include test coverage requirements, complexity limits, or specific coding patterns. When code doesn't meet these standards, merges are automatically blocked until issues are addressed.\n* Finally, prevent issues before they even reach review. [Push Rules](https://docs.gitlab.com/user/project/repository/push_rules/) let you enforce standards right at commit time. You might start with simple rules like requiring certain commit message formats, then gradually add more sophisticated checks as your team adapts.\n\n## 2. Improve collaboration and productivity\n\nThe priorities for startups are often budget and speed, but as businesses grow, tracking DevSecOps workflows across a patchwork of tools can actually deter productivity.\n\nDisparate tools cause developers to context switch between platforms, decreasing focus time and development speed. Toolchain sprawl also limits visibility among teams, creating operational silos that lead to miscommunication.\n\nTo address these challenges, teams often turn to Agile solutions to help with project management, align timelines, and improve cross-team collaboration. When combined with a DevSecOps environment, [Agile](https://about.gitlab.com/topics/agile-devsecops/) creates a powerful system for software development that marries the iterative Agile approach with a security-first mindset.\n\n### Features that improve collaboration and productivity\n\n* With GitLab Premium, teams can access enterprise-grade Agile tools within their DevSecOps platforms. You can start by creating [groups and projects](http://%20epics), assigning team members roles, and determining their level of permission.\n* [Milestones](https://docs.gitlab.com/user/project/milestones/) and [epics](https://docs.gitlab.com/user/group/epics/) help teams plan large-scale initiatives across multiple projects to track dependencies, progress, and align on deliverables. This gives everyone clear visibility into the process.\n* Then, dive deeper into each task with [issues](https://docs.gitlab.com/user/project/issue_board/). With customizable workflows and multi-assignee capabilities, teams can visualize project progress, dynamically adjust priorities, and collaborate on issue resolution.\n\n## 3. Increase deployment velocity\n\nIn theory, teams should be more productive as they scale. However, if tools aren’t updated to accommodate a growing team, the CI/CD pipeline can feel clunky and inefficient.\n\nTeams turn to tools that help them automate and optimize the [CI/CD pipeline](https://about.gitlab.com/topics/ci-cd/cicd-pipeline/). By automating components like code reviews, merge trains, and permissions, teams can streamline the CI/CD pipeline and improve deployment speed.\n\n### Features that increase deployment velocity\n\nGitLab Premium offers advanced features that help you build, maintain, deploy, and monitor complex pipelines. Increase the speed of deployment through the CI/CD pipeline with more control over code reviews and merge request processes.\n\n* You can automate the merging of multiple changes in a controlled sequence with Merge Trains. This reduces integration issues and improves deployment efficiency.\n* Gain visibility into whether your jobs passed or failed with Multi-Project Pipeline Graphs. Access all related jobs for a single commit and the net result of each stage of your pipeline to quickly see what failed and fix it.\n* Team leaders can access comprehensive insights and make data-informed decisions with [Code Review Analytics](https://docs.gitlab.com/user/analytics/code_review_analytics/) that provide detailed metrics and merge request analytics. This helps teams identify bottlenecks, optimize review cycles, and establish data-driven process improvements.\n\n## 4. Enhance security and compliance controls\n\nWithout rigorous governance policies, inefficient and insecure code may be released. With smaller companies, security reviews are often manual and the reviews often take a backseat to speed. This can lead to teams releasing incorrect or unsafe code to production causing costly delays.\n\nTo [evolve their security practices](https://about.gitlab.com/blog/3-signs-your-team-is-ready-to-uplevel-security-controls-in-gitlab/), teams turn to stricter access controls, a more refined and delineated review process, as well as features that enable teams to review and track changes.\n\n### Features that enhance security and compliance controls\n\n* With stricter access controls, such as [Protected Branches and Protected Environments](https://docs.gitlab.com/user/project/repository/branches/protected/), you can restrict push and merge access, securing those areas from unwanted changes by unauthorized users.\n* To strengthen security review processes, implement [Multiple Approvers in Merge Requests](https://docs.gitlab.com/user/project/merge_requests/approvals/). This requires team members to review and approve code changes before they’re pushed through.\n* Review who performed a certain action within the repository and at what time with [Audit Events](https://docs.gitlab.com/administration/audit_event_reports/). By tracking changes, you’re able to stay on top of compliance requirements.\n\n## 5. Avoid downtime and delays\n\nWithout support, teams are left to troubleshoot issues themselves. This can lead to major delays or periods of downtime where the company is unable to deliver value. As companies grow, this downtime becomes more and more detrimental to the business.\n\nIt’s important to evaluate what your company’s threshold is for downtime. When the value of the downtime outweighs the cost of support, it’s time to scale your DevSecOps platform to meet those needs.\n\n### Support services to avoid downtime and delays\n\nWith GitLab Premium, customers of both SaaS and self-managed instances have access to [Priority Support](https://support.gitlab.com/hc/en-us/articles/11626483177756-GitLab-Support#priority-support). GitLab customer support offers Tiered Support response times, ranging from emergency to low-impact services, and can help you resolve issues quickly, minimizing downtime and disruption to your development cycle.\n\nPlus, for self-managed customers moving to Premium, GitLab offers support for any issues that occur after implementation and upgrade assistance to provide a seamless transition.\n\n## Build today, scale for tomorrow with GitLab Premium\n\nInstead of struggling with the challenges that growing teams face, scale your DevSecOps platform with GitLab Premium.\n\nGitLab Premium provides teams with the project management, pipeline tools, security, and support needed to work efficiently and effectively across the software development lifecycle.\n\n> #### Learn more about [why you should upgrade to GitLab Premium](https://about.gitlab.com/pricing/premium/why-upgrade/).","product",{"template":13,"slug":14,"featured":15},"BlogPost","5-gitlab-premium-features-to-help-your-team-scale",false,{"title":5,"description":17,"authors":18,"heroImage":19,"tags":20,"category":11,"date":23,"body":10},"Explore how GitLab Premium boosts team collaboration and productivity, enabling organizations to scale with streamlined workflows and advanced capabilities.",[9],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665151/Blog/Hero%20Images/blog-image-template-1800x945__27_.png",[21,22],"DevSecOps platform","features","2024-12-18","md",null,{},true,"/en-us/blog/5-gitlab-premium-features-to-help-your-team-scale","---\nseo:\n  title: 5 GitLab Premium features to help your team scale\n  description: >-\n    Explore how GitLab Premium boosts team collaboration and productivity,\n    enabling organizations to scale with streamlined workflows and advanced\n    capabilities.\n  ogTitle: 5 GitLab Premium features to help your team scale\n  ogDescription: >-\n    Explore how GitLab Premium boosts team collaboration and productivity,\n    enabling organizations to scale with streamlined workflows and advanced\n    capabilities.\n  noIndex: false\n  ogImage: >-\n    https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665151/Blog/Hero%20Images/blog-image-template-1800x945__27_.png\n  ogUrl: >-\n    https://about.gitlab.com/blog/5-gitlab-premium-features-to-help-your-team-scale\n  ogSiteName: https://about.gitlab.com\n  ogType: article\n  canonicalUrls: >-\n    https://about.gitlab.com/blog/5-gitlab-premium-features-to-help-your-team-scale\ntitle: 5 GitLab Premium features to help your team scale\ndescription: Explore how GitLab Premium boosts team collaboration and productivity, enabling organizations to scale with streamlined workflows and advanced capabilities.\nauthors:\n  - Julie Griffin\nheroImage: https://res.cloudinary.com/about-gitlab-com/image/upload/v1749665151/Blog/Hero%20Images/blog-image-template-1800x945__27_.png\ntags:\n  - DevSecOps platform\n  - features\ncategory: product\ndate: '2024-12-18'\nslug: 5-gitlab-premium-features-to-help-your-team-scale\nfeatured: false\ntemplate: BlogPost\n---\n\nAs development teams grow, what once worked for a small team often becomes a bottleneck. Code standards become inconsistent, operational silos develop, and technical debt accumulates faster. What was a well-oiled machine is now dysfunctional as more team members, projects, and tools are added on.\n\nMany teams experience these challenges as they grow, but how you handle and address these growing pains can save you time, energy, and money in the long run. In this article, we’ll explore the common pitfalls growing teams face and how successful organizations address them.\n\n## 1. Consistent code quality\n\nOne of the challenges growing teams face is [maintaining consistent code quality](https://about.gitlab.com/blog/transform-code-quality-and-compliance-with-automated-processes/) as more developers contribute to the codebase. Quality issues that were once caught quickly now take longer to identify and fix.\n\nSuccessful teams address these challenges through automated code analysis throughout their development workflow. Instead of relying solely on manual reviews, they implement systems to identify potential issues and enforce consistent standards before code even reaches human reviewers. This approach helps detect complexity issues early and flags potential security vulnerabilities, allowing reviewers to focus on more strategic aspects of code review.\n\n### Features that maintain consistent code quality\n\n* Start by automating code analysis in your workflow. With GitLab Premium, you can set up [Code Quality Reports](https://docs.gitlab.com/ci/testing/code_quality/) in your merge requests. This helps catch issues early by analyzing code complexity and quality before review begins. For example, when a developer submits changes that might increase technical debt, the report will flag these issues automatically.\n* Next, establish automated quality standards. Configure Quality Gates to define what \"good code\" means for your team. This could include test coverage requirements, complexity limits, or specific coding patterns. When code doesn't meet these standards, merges are automatically blocked until issues are addressed.\n* Finally, prevent issues before they even reach review. [Push Rules](https://docs.gitlab.com/user/project/repository/push_rules/) let you enforce standards right at commit time. You might start with simple rules like requiring certain commit message formats, then gradually add more sophisticated checks as your team adapts.\n\n## 2. Improve collaboration and productivity\n\nThe priorities for startups are often budget and speed, but as businesses grow, tracking DevSecOps workflows across a patchwork of tools can actually deter productivity.\n\nDisparate tools cause developers to context switch between platforms, decreasing focus time and development speed. Toolchain sprawl also limits visibility among teams, creating operational silos that lead to miscommunication.\n\nTo address these challenges, teams often turn to Agile solutions to help with project management, align timelines, and improve cross-team collaboration. When combined with a DevSecOps environment, [Agile](https://about.gitlab.com/topics/agile-devsecops/) creates a powerful system for software development that marries the iterative Agile approach with a security-first mindset.\n\n### Features that improve collaboration and productivity\n\n* With GitLab Premium, teams can access enterprise-grade Agile tools within their DevSecOps platforms. You can start by creating [groups and projects](http://%20epics), assigning team members roles, and determining their level of permission.\n* [Milestones](https://docs.gitlab.com/user/project/milestones/) and [epics](https://docs.gitlab.com/user/group/epics/) help teams plan large-scale initiatives across multiple projects to track dependencies, progress, and align on deliverables. This gives everyone clear visibility into the process.\n* Then, dive deeper into each task with [issues](https://docs.gitlab.com/user/project/issue_board/). With customizable workflows and multi-assignee capabilities, teams can visualize project progress, dynamically adjust priorities, and collaborate on issue resolution.\n\n## 3. Increase deployment velocity\n\nIn theory, teams should be more productive as they scale. However, if tools aren’t updated to accommodate a growing team, the CI/CD pipeline can feel clunky and inefficient.\n\nTeams turn to tools that help them automate and optimize the [CI/CD pipeline](https://about.gitlab.com/topics/ci-cd/cicd-pipeline/). By automating components like code reviews, merge trains, and permissions, teams can streamline the CI/CD pipeline and improve deployment speed.\n\n### Features that increase deployment velocity\n\nGitLab Premium offers advanced features that help you build, maintain, deploy, and monitor complex pipelines. Increase the speed of deployment through the CI/CD pipeline with more control over code reviews and merge request processes.\n\n* You can automate the merging of multiple changes in a controlled sequence with Merge Trains. This reduces integration issues and improves deployment efficiency.\n* Gain visibility into whether your jobs passed or failed with Multi-Project Pipeline Graphs. Access all related jobs for a single commit and the net result of each stage of your pipeline to quickly see what failed and fix it.\n* Team leaders can access comprehensive insights and make data-informed decisions with [Code Review Analytics](https://docs.gitlab.com/user/analytics/code_review_analytics/) that provide detailed metrics and merge request analytics. This helps teams identify bottlenecks, optimize review cycles, and establish data-driven process improvements.\n\n## 4. Enhance security and compliance controls\n\nWithout rigorous governance policies, inefficient and insecure code may be released. With smaller companies, security reviews are often manual and the reviews often take a backseat to speed. This can lead to teams releasing incorrect or unsafe code to production causing costly delays.\n\nTo [evolve their security practices](https://about.gitlab.com/blog/3-signs-your-team-is-ready-to-uplevel-security-controls-in-gitlab/), teams turn to stricter access controls, a more refined and delineated review process, as well as features that enable teams to review and track changes.\n\n### Features that enhance security and compliance controls\n\n* With stricter access controls, such as [Protected Branches and Protected Environments](https://docs.gitlab.com/user/project/repository/branches/protected/), you can restrict push and merge access, securing those areas from unwanted changes by unauthorized users.\n* To strengthen security review processes, implement [Multiple Approvers in Merge Requests](https://docs.gitlab.com/user/project/merge_requests/approvals/). This requires team members to review and approve code changes before they’re pushed through.\n* Review who performed a certain action within the repository and at what time with [Audit Events](https://docs.gitlab.com/administration/audit_event_reports/). By tracking changes, you’re able to stay on top of compliance requirements.\n\n## 5. Avoid downtime and delays\n\nWithout support, teams are left to troubleshoot issues themselves. This can lead to major delays or periods of downtime where the company is unable to deliver value. As companies grow, this downtime becomes more and more detrimental to the business.\n\nIt’s important to evaluate what your company’s threshold is for downtime. When the value of the downtime outweighs the cost of support, it’s time to scale your DevSecOps platform to meet those needs.\n\n### Support services to avoid downtime and delays\n\nWith GitLab Premium, customers of both SaaS and self-managed instances have access to [Priority Support](https://support.gitlab.com/hc/en-us/articles/11626483177756-GitLab-Support#priority-support). 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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. 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