[{"data":1,"prerenderedAt":829},["ShallowReactive",2],{"/en-us/blog/why-gitlab-self-managed-is-the-perfect-partner-for-the-public-sector":3,"navigation-en-us":39,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Elisabeth Burrows|Ashher Syed":700,"blog-related-posts-en-us-why-gitlab-self-managed-is-the-perfect-partner-for-the-public-sector":726,"blog-promotions-en-us":766,"next-steps-en-us":819},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":27,"isFeatured":13,"meta":28,"navigation":13,"path":29,"publishedDate":22,"seo":30,"stem":35,"tagSlugs":36,"__hash__":38},"blogPosts/en-us/blog/why-gitlab-self-managed-is-the-perfect-partner-for-the-public-sector.yml","Why Gitlab Self Managed Is The Perfect Partner For The Public Sector",[7,8],"elisabeth-burrows","ashher-syed",null,"product",{"slug":12,"featured":13,"template":14},"why-gitlab-self-managed-is-the-perfect-partner-for-the-public-sector",true,"BlogPost",{"heroImage":16,"body":17,"authors":18,"updatedDate":21,"date":22,"title":23,"tags":24,"description":26,"category":10},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749662023/Blog/Hero%20Images/display-dedicated-for-government-article-image-0679-1800x945-fy26.png","State and local government leaders are navigating new fiscal realities while needing to adopt modern security practices that keep pace with the rapid speed of software development. The Department of Government Efficiency's push to reduce U.S. federal spending creates uncertainty around the [$1.1 trillion in annual funding](https://www.govtech.com/voices/state-and-local-government-efficiency-gains-hinge-on-technology) that flows to state and local governments and comprises 36% of state revenues on average. As the full impact remains unclear, government leaders are already preparing for potential budget pressures while citizens continue demanding better digital services and faster delivery.\n    \nAdd to this financial pressure accelerating workforce retirements and ongoing modernization needs. And, as if that weren't enough, cybersecurity has reclaimed the top spot in NASCIO's [2025 State CIO priorities](https://www.nascio.org/resource-center/resources/state-cio-top-ten-policy-and-technology-priorities-for-2025/), reflecting escalating cyber threats that demand secure development practices. The bottom line: Agencies must do more with less *without* sacrificing on security.\n    \n[GitLab Self-Managed](https://docs.gitlab.com/subscriptions/self_managed/) with [GitLab Duo Self-Hosted](https://docs.gitlab.com/administration/gitlab_duo_self_hosted/) provides the comprehensive DevSecOps platform government organizations need to meet this moment and deliver efficient, secure services with AI-accelerated development.\n\n[Trusted across federal civilian agencies](https://about.gitlab.com/customers/), all branches of the Department of Defense, the intelligence community, and state and local governments, GitLab delivers enterprise-grade security within an organization's own infrastructure.\n    \n\"GitLab Self-Managed allows customers to run the full GitLab platform entirely in their own secure environment. Add in self-hosted large language models and highly customized models, and GitLab becomes the ultimate tool for disconnected DevSecOps teams,\" says Bruce Marco, GitLab Public Sector Senior Manager of Solutions Architecture.\n    \nThis deployment flexibility ensures data sovereignty while providing the application security and compliance features that help public sector organizations secure their development processes and supply chains when delivering applications to constituents.\n    \n## How GitLab Self-Managed delivers value for government\n    \nHere are some of the benefits of GitLab's intelligent DevSecOps platform for state and local government.\n    \n### Complete control with AI-powered efficiency\n    \nGitLab Self-Managed with GitLab Duo Self-Hosted provides total control over your infrastructure while delivering AI capabilities in secure, air-gapped environments. You install and maintain your own GitLab instance with administrative controls to customize and secure it as needed.\n    \nKey advantages include:\n    \n**Data sovereignty**: All data stays within your infrastructure boundaries with no external dependencies for core functionality. The platform works on-premises, in private clouds, or hybrid environments, including air-gapped configurations.\n    \n**Secure AI**: GitLab Duo Self-Hosted enables advanced AI capabilities within air-gapped environments, maintaining complete data sovereignty while meeting mission-critical security standards like NIST FIPS and ICD 503. All data and code remain within your environment and are never transmitted to external model providers.\n\n> [Learn more about GitLab Duo Self-Hosted](https://about.gitlab.com/the-source/ai/transforming-government-it-ai-for-air-gapped-environments/).\n    \n**From fragmentation to efficiency:** GitLab Self-Managed replaces expensive, fragmented toolchains with a single platform. [GitLab's 2024 Global DevSecOps Survey](https://about.gitlab.com/developer-survey/) found that 62% of developers are using six or more tools for software development. According to the [Forrester Total Economic Impact Study](https://tei.forrester.com/go/Gitlab/GitLabUltimate/?lang=en-us) (TEI), organizations that replaced point solutions and consolidated their toolchains with GitLab improved developer productivity and happiness, lowered their IT costs, and enhanced security while delivering better software faster and maintaining the highest security and quality standards.\n    \n### Built-in compliance that scales with your operations\n    \nGitLab Self-Managed addresses the compliance burden that consumes government resources while enabling agencies to balance security requirements with the speed needed for innovation. According to the [President's Management Agenda](https://trumpadministration.archives.performance.gov/PMA/Presidents_Management_Agenda.pdf), federal managers spend 40% of their time using antiquated processes to monitor compliance instead of analyzing data to improve results.\n    \nGitLab offers 50+ ready-to-use [compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) covering ISO, SOC, NIST, and more, with the ability to map multiple overlapping controls into a single unified framework. GitLab's comprehensive [security scanner suite](https://docs.gitlab.com/user/application_security/continuous_vulnerability_scanning/), including SAST, DAST, container scanning, and software composition analysis, provides strong defense against emerging threats while providing continuous visibility into compliance posture rather than point-in-time assessments. Built-in support for [NIST's Secure Software Development Framework](https://trust.gitlab.com/) (SSDF), [dynamic SBOM](https://about.gitlab.com/blog/the-ultimate-guide-to-sboms/) generation, automated logging, provenance attestation, and comprehensive compliance dashboards ensures government agencies can report on and meet regulatory requirements while maintaining operational efficiency.\nForrester's TEI Study found that cybersecurity and software development teams using GitLab maintain issue-free software with 81% less effort by integrating security protocols throughout all stages of the SDLC.\n\n> Find out [how Lockheed Martin used GitLab's compliance framework](https://about.gitlab.com/customers/lockheed-martin/) to enforce software quality and automation to make releases and dependency management more efficient.\n\n## Measuring impact\n    \nWith the administration's focus on accelerating efficiencies, agencies must move beyond anecdotal evidence to concrete data-driven proof of their investments. GitLab’s comprehensive analytics dashboards ensure that agencies can not only meet efficiency mandates but demonstrate exactly how they're achieving them.\n    \nGitLab's unified platform provides the visibility agencies need to demonstrate efficiency gains through comprehensive analytics capabilities:\n\n* **End-to-end insight and visibility analytics:** Gain complete insights into your software development lifecycle, including DevSecOps maturity, [DORA metrics](https://docs.gitlab.com/user/analytics/dora_metrics/) , [usage trends](https://docs.gitlab.com/administration/analytics/usage_trends/), and [customizable dashboards](https://docs.gitlab.com/user/analytics/analytics_dashboards/) for real-time monitoring.\n\n* **Developer productivity:** Get insights into [developer productivity](https://docs.gitlab.com/user/analytics/productivity_analytics/), code coverage, and team performance on issues and merge requests. Customizable visualizations show which capabilities drive the most significant improvements.\n\n* **AI impact metrics:** GitLab's [AI Impact Dashboard](https://docs.gitlab.com/user/analytics/ai_impact_analytics/) offers clear visibility into performance improvements, letting leaders compare metrics between teams using AI and those who aren't. GitLab automatically tracks AI feature adoption to help optimize implementation strategies.\n\nThis measurement-driven approach doesn't just help justify current investments — it builds the evidence base for continued modernization that delivers measurable value to citizens while meeting efficiency mandates.\n\n## Your path forward: Start secure, scale smart\n    \nThe journey to modern DevSecOps doesn't require a wholesale transformation. Agencies can:\n     \n* **Start with core platform capabilities** to consolidate tools and reduce costs.\n     \n* **Add security automation** to strengthen compliance while reducing manual effort.\n    \n* **Introduce AI capabilities** when ready, starting with low-risk projects.\n* **Scale across the organization** as teams see success.\n\n## Experienced professionals at the ready\n\nGitLab's [Professional Services](https://about.gitlab.com/professional-services/) team has extensive experience in the public sector and understands your particular requirements. If you have multiple services, servers, and programs you need to migrate, we will help you plan that out.\n\n> Ready to migrate to GitLab? [Contact our sales team](https://about.gitlab.com/solutions/public-sector/) to start a conversation today.",[19,20],"Elisabeth Burrows","Ashher Syed","2025-07-30","2023-12-13","Why GitLab Self-Managed is the perfect partner for the public sector",[25,10],"public sector","GitLab Self-Managed helps state and local governments modernize DevSecOps with secure AI, compliance automation, and cost-efficient toolchain 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Patch Release: 18.11.1, 18.10.4, 18.9.6","Discover what's in this latests patch release.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749661926/Blog/Hero%20Images/security-patch-blog-image-r2-0506-700x400-fy25_2x.jpg","2026-04-22",[734,735],"patch releases","security releases",{"featured":32,"template":14,"externalUrl":737},"https://docs.gitlab.com/releases/patches/patch-release-gitlab-18-11-1-released/",{"content":739,"config":751},{"title":740,"description":741,"body":742,"category":10,"tags":743,"date":746,"authors":747,"heroImage":750},"GitLab + Amazon: Platform orchestration on a trusted AI 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.",[275,744,745],"AWS","AI/ML","2026-04-21",[748,749],"Joe Mann","Mark Kriaf","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362275/ozbwn9tk0dditpnfddlz.png",{"featured":13,"template":14,"slug":752},"gitlab-amazon-platform-orchestration-on-a-trusted-ai-foundation",{"content":754,"config":764},{"title":755,"description":756,"authors":757,"heroImage":759,"date":760,"body":761,"category":10,"tags":762},"GitLab 18.11: Budget guardrails for GitLab Credits","Learn how new spending caps and per-user credit limits give organizations the budget guardrails to scale GitLab Duo Agent Platform.",[758],"Bryan Rothwell","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776259080/cakqnwo5ecp255lo8lzo.png","2026-04-16","Teams using GitLab Duo Agent Platform with on-demand GitLab Credits are shipping faster, catching bugs earlier, and automating tasks that used to take entire sprints. But as adoption grows, so does oversight from finance, procurement, and platform teams to prove that AI spending is bounded, predictable, and controllable.\n\nOne of the greatest barriers to broader AI adoption isn't skepticism about the technology. It's uncertainty about managing spend. Without budget caps, a busy month could produce unexpected expenses. Without per-user limits, a handful of power users could burn through the team's credits before the month is over. And without either, engineering leaders who want to expand their use of agentic AI for software development have to jump through more hoops for budget approval.\n\nSince its [general availability](https://about.gitlab.com/blog/gitlab-duo-agent-platform-is-generally-available/), GitLab Duo Agent Platform has provided usage governance and visibility. With GitLab 18.11, we're introducing usage controls for [GitLab Credits](https://about.gitlab.com/blog/introducing-gitlab-credits/): spending caps and budget guardrails that give your organization even more control and transparency over how credits are consumed.\n\n## Managing GitLab Credits\n\nGitLab 18.11 adds three layers of control over GitLab Credits consumption: a subscription-level spending cap, per-user credit limits, and visibility into cap status and enforcement.\n\n### Subscription-level spending cap\n\nBilling account managers can now set a hard monthly ceiling for on-demand GitLab Credits consumption for their entire subscription.\n\nHere's how it works:\n\n* **Set a cap** in the `Customers Portal` under your subscription's GitLab Credits settings.  \n* **Enforce spend limits automatically.**  When on-demand usage reaches the cap, DAP access is paused for all users on that subscription until the next monthly period begins.  \n* **Make adjustments as you go.** Raise or disable the cap mid-month to restore access.\n\nThe cap resets each monthly period and your configured limit carries forward unless you change it. Because usage data is synchronized periodically rather than in real time, a small amount of additional usage may occur after the cap is reached before enforcement takes effect. See the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for details.\n\n### User-level spending caps\n\nNot every user consumes credits at the same rate, and that's expected. But when one or two power users account for a disproportionate share of the pool, the rest of the team can lose access before the month is over.\n\nPer-user credit caps prevent any single user from consuming more than their fair share:\n\n* **Flat per-user cap.** Set a uniform credit limit that applies equally to every user on the subscription through the GitLab GraphQL API. Unlike the subscription-level cap, the per-user cap applies to a user's total consumption across all credit sources.  \n* **Custom per-user overrides.** For organizations that need differentiated limits, you can set individual credit caps for specific users through the GraphQL API. For example, you could give your staff engineers a higher allocation while applying a standard limit to the broader team.  \n* **Individual enforcement.** When a user reaches their cap, they retain full access to GitLab. Only their Duo Agent Platform credit usage is paused until the next billing cycle. Everyone else keeps working uninterrupted until they hit their own limit or the subscription-level cap is reached, whichever comes first.\n\n### Visibility and notifications\n\nWhen a subscription-level cap is reached, GitLab sends an email notification to billing account managers so they can take action: raise the cap, wait for the next period, or redistribute credits.\n\nWithin GitLab, group owners (GitLab.com) and instance administrators (Self-Managed) can view which users have been blocked due to reaching their per-user cap and restore access by adjusting the cap through the GraphQL API. \n\n## How budget guardrails help organizations scale AI usage\n\nGuardrails are essential as organizations ramp up their AI adoption. Here's why:\n\n### Predictable AI budgets\n\nUsage controls for GitLab Duo Agent Platform turn AI into a bounded, predictable budget item using on-demand GitLab Credits. That makes it easier to deploy agents across the software development lifecycle and get sign-off from finance, justify renewals, and plan quarterly spend.\n\n### Governance and chargeback\n\nLarge organizations often need to align AI consumption with internal budgets, cost centers, or departmental policies. Per-user caps give platform teams a straightforward mechanism to allocate credits fairly and track consumption at the individual level. The API import options make it practical to manage caps at enterprise scale. Combined with per-user usage data from the GitLab Credits dashboard, organizations can track consumption patterns to inform their own internal chargeback or budget allocation processes.\n\n### Confidence to scale\n\nMany customers start GitLab Duo Agent Platform with a small pilot group. Usage controls remove risks associated with expanding that pilot across the organization. You can roll out Duo Agent Platform to hundreds or thousands of developers knowing there's a hard ceiling protecting your budget. If usage grows faster than expected, you'll hit the cap, not an unexpected invoice.\n\n## Addressing the seat-based and visibility conundrum\n\nMany AI coding tools take a seat-based approach to cost management. You buy a fixed number of seats at a flat per-user price, and that's your budget. It's simple, but rigid. You pay the same whether a developer uses the tool ten times a day or never touches it. And as vendors introduce premium models and usage-based overages on top of seat pricing, the cost predictability that seat-based licensing promised starts to erode.\n\n\nGitLab takes a different approach. Usage-based pricing with hard caps and a single governance dashboard. You get the flexibility of paying for what your teams actually use, with the budget predictability of enforced spending limits.\n\n## Real-world usage controls\n\n**One example is a mid-size SaaS customer that wants to protect their monthly budget.** A 200-person engineering organization sets a subscription-level cap equal to their expected on-demand usage. Their VP of Engineering can confidently tell finance that GitLab Duo Agent Platform spend will never exceed the approved amount, even as they onboard new teams. If they approach the cap mid-month, the billing account manager gets a notification and can decide whether to raise the limit or wait for the next period.\n\n**At GitLab, we also work with large enterprises that want to keep usage fair across teams.** A global financial services company with 2,000 developers uses per-user caps to ensure equitable access. Staff engineers working on complex refactoring projects get a higher individual allocation via API, while most developers receive a standard flat cap. No single user can exhaust the pool, and the platform team uses the per-user usage data in the GitLab Credits dashboard to track consumption patterns and inform quarterly budget planning.\n\n## Getting started\n\nUsage controls are available for both GitLab.com and Self-Managed customers running GitLab 18.11. Different controls are configured in different places depending on the scope and your role.\n\n**Subscription-level cap**\n\nBilling account managers set the subscription-level on-demand cap in the Customers Portal:\n\n1. Sign in to the `Customers Portal`.  \n2. On your subscription card, navigate to **GitLab Credits** settings.  \n3. Enable the monthly on-demand credits cap and enter your desired limit.\n\n**Flat per-user cap**\n\nThe flat per-user cap can be set through the GitLab GraphQL API by namespace owners (GitLab.com) or instance administrators (Self-Managed). Check the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for the latest on available configuration surfaces.\n\n**Custom per-user overrides**\n\nFor differentiated limits, namespace owners (GitLab.com) and instance administrators (Self-Managed) can set individual caps programmatically. This is useful for automation and infrastructure-as-code workflows.\n\n**Monitor usage and cap status**\n\n* **Customers Portal:** View detailed usage and cap status.  \n* **GitLab.com:** Group owners can view blocked users under **Settings > GitLab Credits**.  \n* **Self-Managed:** Instance administrators can view cap status and blocked users under **Admin > GitLab Credits**.\n\n## GitLab Duo Agent Platform is ready to scale\n\nUsage controls are available now in GitLab 18.11. If you've been waiting for the right guardrails before expanding GitLab Duo Agent Platform across your organization, this is your moment. Set your caps, roll out Duo Agent Platform to more teams, and start shipping faster!\n\n> [Learn more about GitLab Credits and usage controls](https://docs.gitlab.com/subscriptions/gitlab_credits/).",[10,745,763],"news",{"featured":32,"template":14,"slug":765},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"promotions":767},[768,782,793,805],{"id":769,"categories":770,"header":772,"text":773,"button":774,"image":779},"ai-modernization",[771],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":775,"config":776},"Get your AI maturity score",{"href":777,"dataGaName":778,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":783,"categories":784,"header":785,"text":773,"button":786,"image":790},"devops-modernization",[10,568],"Are you just managing tools or shipping innovation?",{"text":787,"config":788},"Get your DevOps maturity score",{"href":789,"dataGaName":778,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":791},{"src":792},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":794,"categories":795,"header":797,"text":773,"button":798,"image":802},"security-modernization",[796],"security","Are you trading speed for security?",{"text":799,"config":800},"Get your security maturity score",{"href":801,"dataGaName":778,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":803},{"src":804},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":806,"paths":807,"header":810,"text":811,"button":812,"image":817},"github-azure-migration",[808,809],"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":813,"config":814},"See how GitLab compares to GitHub",{"href":815,"dataGaName":816,"dataGaLocation":242},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":818},{"src":792},{"header":820,"blurb":821,"button":822,"secondaryButton":827},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":823,"config":824},"Get your free trial",{"href":825,"dataGaName":50,"dataGaLocation":826},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":828},{"href":54,"dataGaName":55,"dataGaLocation":826},1777302627196]