[{"data":1,"prerenderedAt":819},["ShallowReactive",2],{"/en-us/blog/gitlab-ux-2020-year-in-review":3,"navigation-en-us":40,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Christie Lenneville":702,"blog-related-posts-en-us-gitlab-ux-2020-year-in-review":716,"blog-promotions-en-us":756,"next-steps-en-us":809},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":29,"path":30,"publishedDate":20,"seo":31,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/gitlab-ux-2020-year-in-review.yml","Gitlab Ux 2020 Year In Review",[7],"christie-lenneville",null,"product",{"slug":11,"featured":12,"template":13},"gitlab-ux-2020-year-in-review",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"GitLab UX 2020 Year in Review","2020 was a difficult but productive year. Let's take a look back.",[18],"Christie Lenneville","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664102/Blog/Hero%20Images/gitlab-values-cover.png","2020-11-20","\nA global pandemic and broad social unrest have made this year difficult for everyone. When times are as tough as 2020 has proven to be, it's easy to focus on the negative and forget about the many good things that happened along the way. But our product designers, user researchers, and technical writers spend every day doing great work, and we can't let that slip by unnoticed.\n\nIn this post, I want to be intentional about celebrating our successes during a year when many of us wanted to just curl up under a comfy blanket and wait for the turmoil to pass. So, let's take a moment to reflect on some of the things we can feel really proud to have achieved.\n\n## Usability is now a key consideration in our category maturity model\n\nHistorically, we rated the maturity of our product areas fairly subjectively and based almost entirely on feature availability. This year, that changed when we introduced [Category Maturity Scorecards](https://handbook.gitlab.com/handbook/product/ux/category-maturity/category-maturity-scorecards/) that are based on user research. Now, we start by considering the Job to be Done (JTBD) that our users need to accomplish, and we gather user feedback to rate the entire experience -- not just functionality, but usability, too.\n\nWe've learned some amazing things through this new approach, and those learnings have enabled us to make [valuable recommendations](https://gitlab.com/gitlab-org/gitlab/-/issues?label_name%5B%5D=cm-scorecard-rec) to improve our product experience in areas like Code Review, Logging, and Issue Management. We have several additional scorecard initiatives underway, which means that our focus on creating an exceptional experience will only continue to grow.\n\nSo often, UX departments complain that they have to fight for executives to acknowledge the importance of usability on business outcomes. In this case, refining category maturity started as an idea from [Sid](https://gitlab.com/sytses), our CEO. This is honestly amazing! It's the kind of user-centered focus that UX teams get really excited about.\n\nAs the person who leads UX at GitLab, it was awesome for me to watch our cross-functional team immediately get on board. Because measuring product maturity isn't an industry standard, through our value of [Iteration](https://handbook.gitlab.com/handbook/values/#iteration) it took us some time (and a false start) to determine the right approach. Fortunately, Product leadership was both enthusiastic and patient, UX Researchers were persistent in taking feedback and making methodological refinements, and Product Designers were courageous in trying something they've never done before. Even better: Technical Writing has been involved, too, as we've identified documentation improvements that will refine our product maturity.\n\nThis was truly a team effort, and I appreciate everyone who participated. 🤝\n\n## Our design system evolved from an idea into reality\n\nWhen I joined GitLab in early 2019, our design system, [Pajamas](https://design.gitlab.com/), was a scrappy project that the design team was working hard to get off the ground. We had designed a set of 28 single-source-of-truth components and were working hard to build them into [GitLab UI](https://gitlab.com/gitlab-org/gitlab-ui), our Vue-based component library.\nWe now have a robust design library that's implemented in Figma, and a large collection of SSOT Vue components are available to use in the product, too. Even more exciting: We're just finishing with implementing our 8 most impactful components across the entire product UI (buttons, alerts, dropdowns, modals, tabs, popovers, and tooltips), which will result in better performance and consistency when we're done. (We're so close!)\n\nMost amazing to me was watching product designers and technical writers jump in to do much of this component migration work themselves. This was no small feat, because frontend development is not something that many of us are deeply skilled at. But, apparently we're both tenacious and brave, because we did the work anyway (with lots of help from our Frontend Engineers and the awesome documentation that our UX Foundations team created). In the process, we've gotten to know both our product features (which are complex) and our code base (which is also complex) even better, which makes us more effective in our day-to-day jobs.\n\nSpeaking of our UX Foundations team, this is another related success. At the beginning of 2020, we got the budgetary support to create a team that is dedicated solely to maintaining our design system and tooling. The team may be small, but its impact certainly isn't. They've already made some big improvements to things like:\n\n* **Improving tooling for designers:** The move to Figma allows for greater collaboration, as well as community contributions. Sketch is only available on Mac platforms and there are no real-time collaboration features. Figma allows us to provide a UI Kit that is available across platforms, while being available for community contributors to use for free. It also promotes collaboration through its use of real-time editing capabilities and version history. We were able to streamline developer handoff by simply linking to the design file, reducing the need for additional plugins such as Sketch Measure.\n* **Making our color palette consistent and accessible:** We addressed color contrast for accessibility and normalized the palette across hues, so that we can better systematize variable use throughout the UI.\n* **Improving consistency in our icons:** With the creation of our own [SVG Library](http://gitlab-org.gitlab.io/gitlab-svgs/), we've been working to [deprecate our use of Font Awesome](https://gitlab.com/groups/gitlab-org/-/epics/2331) throughout the year. With the help of the Frontend department, we've closed out 156 out of 168 issues related to this effort.\n* **Moving towards more accessible workflows:** Near the end of the year, we've started focusing more on building accessibility standards into our workflows. We are currently auditing and updating our [voluntary product accessibility template](https://design.gitlab.com/accessibility/vpat), as well as [incorporating accessibility audit guides](https://gitlab.com/gitlab-org/gitlab-services/design.gitlab.com/-/merge_requests/2158) into Pajamas.\n\n## Actionable insights\n\nUser research is so incredibly valuable... when you take action on it. But it can be a challenge for research teams to condense their powerful findings into small but compelling insights and then track those insights to determine whether they actually make it into the product.\n\nIn the second half of this year, our user research team made two big strides in this area. First, we started using [Dovetail](https://dovetailapp.com/) to help us more easily analyze research data to find meangingful insights and share it collaboratively with Product Managers and Product Designers (and anyone else who may be interested). But, they took this a step farther by also beginning to [track actionable insights](https://handbook.gitlab.com/handbook/product/ux/performance-indicators/#actionable-insights) as a performance indicator.\n\nThe considerable effort it took to get both of these programs in place will be worth it as we watch our research efforts result in an even better product.\n\n## Beautifying our docs\n\nComplex products like GitLab require high-quality documentation. Some things you just can't (and shouldn't) communicate through the UI, so users rely on great docs to get their daily jobs done.\n\nOur Technical Writing team (many of whom have been with GitLab less than a year) worked hard to improve our docs site during 2020, including:\n\n- Several UX research projects to discover - and fix! - problems users encounter when using the docs site.\n- A \"Beautification\" effort that focused on an updated visual design. Our 2020 GitLab Contribute event included many rapid improvements to the docs site, and we made many more afterward. (Did you notice?)\n- Ongoing content improvements, including making our docs more consistent, findable, detailed, and easier to read.\n- Adding (a lot of) metadata information to product docs to help connect content contributors with Technical Writers.\n- Coding innovations for automation, such as grammar checking with Vale, a linter, to automatically catch errors before they’re merged.\n\nWe’ve also completed work on a Docs Strategy roadmap to drive even more improvements in the upcoming months.\n\n## And so much more...\n\n* GitLab Design Talks: In this fun video series, watch designers, technical writers, researchers, and product managers talk about [Iteration](https://www.youtube.com/playlist?list=PL05JrBw4t0KpgzLWbRCXf8o7iap-uoe7o) and [Collaboration](https://www.youtube.com/playlist?list=PL05JrBw4t0KrER807JktsL-addVZa4N0-) at GitLab. (Special thanks to host [Nick Post](https://gitlab.com/npost)!)\n* UX Showcase: See [100+ videos](https://www.youtube.com/playlist?list=PL05JrBw4t0Kq89nFXtkVviaIfYQPptwJz) highlighting exciting UX work happening across GitLab. I learn something new everytime I watch one of these.\n* Blog posts: Read about a variety of topics we were thinking about in 2020, including:\n    * [Designing in an all-remote company](https://about.gitlab.com/blog/designing-in-an-all-remote-company/)\n    * [Running an asynchronous sketching workshop for UX](https://about.gitlab.com/blog/async-sketching/)\n    * [Synchronous collaboration as a remote designer at GitLab](https://about.gitlab.com/blog/synchronous-collaboration-as-a-remote-designer-at-gitlab/)\n    * [A tale of two file editors](https://about.gitlab.com/blog/a-tale-of-two-editors/)\n    * [How holistic UX design increased GitLab.com free trial signups](https://about.gitlab.com/blog/how-holistic-ux-design-increased-gitlab-free-trial-signups/)\n    * [Improving iteration and collaboration with user stories](https://about.gitlab.com/blog/how-we-utilize-user-stories-as-a-collaborative-design-tool/)\n    * [Designing incident management from scratch](https://about.gitlab.com/blog/designing-alerts-and-incidents/)\n    * [Why GitLab is the right design collaboration tool for the entire team ](https://about.gitlab.com/blog/why-gitlab-is-the-right-design-collaboration-tool-for-the-whole-team/)\n\nAgain, the GitLab UX team does amazing work every single day, and there is no way to capture all of that effort in a single blog post. As this year wraps up, I hope you personally take time to think about your own successes and the impact they had on our fast-moving company.\n\nI also hope you know that we value every one of you. You are appreciated. 💜\n\n{::options parse_block_html=\"true\" /}\n\n\u003Cdiv class=\"panel panel-gitlab-purple\">\n  \u003Cp class=\"panel-heading\">\u003Cstrong>One more thing...\u003C/strong>\u003C/p>\n\u003Cdiv class=\"panel-body\">\n\n\u003Cp>The final 2020 highlight I wanted to ensure is here was Christie Lenneville's own promotion to be GitLab's first \u003Cstrong>Vice President of User Experience (UX)\u003C/strong>. I knew that as both the author of this article, and as a humble (and great) leader she'd be hesitant to add this herself. But it's not only a recognition of her achievements and her potential. VP-level leadership of UX at GitLab should \u003Ci>also\u003C/i> be a signal of how important UX is to our organization and to our community. And it should indicate that usability is an important differentiator for GitLab, and a critical part of our company's strategy. Congratulations again, Christie!\u003C/p>\n\n— Eric Johnson, Chief Technology Officer\n\n\u003C/div>\n\u003C/div>\n\n{::options parse_block_html=\"false\" /}\n",[23,24,25,26],"UX","design","inside GitLab","research","yml",{},true,"/en-us/blog/gitlab-ux-2020-year-in-review",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":32,"ogSiteName":33,"ogType":34,"canonicalUrls":32},"https://about.gitlab.com/blog/gitlab-ux-2020-year-in-review","https://about.gitlab.com","article","en-us/blog/gitlab-ux-2020-year-in-review",[37,24,38,26],"ux","inside-gitlab","l0rzx_PhvzIb3GXitwFM7VEiMqZAMpO_IwhNXelEhXg",{"data":41},{"logo":42,"freeTrial":47,"sales":52,"login":57,"items":62,"search":370,"minimal":401,"duo":420,"switchNav":429,"pricingDeployment":440},{"config":43},{"href":44,"dataGaName":45,"dataGaLocation":46},"/","gitlab logo","header",{"text":48,"config":49},"Get free 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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",[724,725],"patch releases","security releases",{"featured":12,"template":13,"externalUrl":727},"https://docs.gitlab.com/releases/patches/patch-release-gitlab-18-11-1-released/",{"content":729,"config":741},{"title":730,"description":731,"body":732,"category":9,"tags":733,"date":736,"authors":737,"heroImage":740},"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.",[277,734,735],"AWS","AI/ML","2026-04-21",[738,739],"Joe Mann","Mark Kriaf","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362275/ozbwn9tk0dditpnfddlz.png",{"featured":29,"template":13,"slug":742},"gitlab-amazon-platform-orchestration-on-a-trusted-ai-foundation",{"content":744,"config":754},{"title":745,"description":746,"authors":747,"heroImage":749,"date":750,"body":751,"category":9,"tags":752},"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.",[748],"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/).",[9,735,753],"news",{"featured":12,"template":13,"slug":755},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"promotions":757},[758,772,783,795],{"id":759,"categories":760,"header":762,"text":763,"button":764,"image":769},"ai-modernization",[761],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":765,"config":766},"Get your AI maturity score",{"href":767,"dataGaName":768,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":773,"categories":774,"header":775,"text":763,"button":776,"image":780},"devops-modernization",[9,570],"Are you just managing tools or shipping innovation?",{"text":777,"config":778},"Get your DevOps maturity score",{"href":779,"dataGaName":768,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":784,"categories":785,"header":787,"text":763,"button":788,"image":792},"security-modernization",[786],"security","Are you trading speed for security?",{"text":789,"config":790},"Get your security maturity score",{"href":791,"dataGaName":768,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":793},{"src":794},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":796,"paths":797,"header":800,"text":801,"button":802,"image":807},"github-azure-migration",[798,799],"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":803,"config":804},"See how GitLab compares to GitHub",{"href":805,"dataGaName":806,"dataGaLocation":244},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":808},{"src":782},{"header":810,"blurb":811,"button":812,"secondaryButton":817},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":813,"config":814},"Get your free trial",{"href":815,"dataGaName":51,"dataGaLocation":816},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":506,"config":818},{"href":55,"dataGaName":56,"dataGaLocation":816},1777302628727]