[{"data":1,"prerenderedAt":816},["ShallowReactive",2],{"/en-us/blog/contributions-to-git-2-42-release":3,"navigation-en-us":39,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Christian Couder":700,"blog-related-posts-en-us-contributions-to-git-2-42-release":714,"blog-promotions-en-us":753,"next-steps-en-us":806},{"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__":38},"blogPosts/en-us/blog/contributions-to-git-2-42-release.yml","Contributions To Git 2 42 Release",[7],"christian-couder",null,"product",{"slug":11,"featured":12,"template":13},"contributions-to-git-2-42-release",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Git 2.42 release: Here are four of our contributions in detail","Find out how GitLab's Git team helped improve Git 2.42.",[18],"Christian Couder","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667792/Blog/Hero%20Images/git-241.jpg","2023-10-12","[Git 2.42](https://gitlab.com/gitlab-org/git/-/raw/master/Documentation/RelNotes/2.42.0.txt)\nwas officially released on August 21, 2023, and included some\nimprovements from GitLab's Git team. Git is the foundation of\nrepository data at GitLab. GitLab's Git team works on new features, performance improvements, documentation improvements,\nand growing the Git community. Often our contributions to Git have a\nlot to do with the way we integrate Git into our services at\nGitLab.\n\nWe previously shared [some of our improvements that were included in the Git 2.41 release](https://about.gitlab.com/blog/contributions-to-latest-git-release/). Here are some highlights from the Git 2.42 release, and a\nwindow into how we use Git on the server side at GitLab.\n\n## 1. Prevent certain refs from being packed\n\n### Write-ahead logging\nIn [Gitaly](https://docs.gitlab.com/ee/administration/gitaly/), we\nwant to use a [write-ahead log](https://gitlab.com/groups/gitlab-org/-/epics/8911)\nto replicate Git operations on different machines.\n\nThis means that the Git objects and references that should be changed\nby a Git operation are first kept in a log entry. Then, when all the\nmachines have agreed that the operation should proceed, the log entry\nis applied so the corresponding Git objects and references are\nactually added to the repositories on all the machines.\n\n### Need for temporary references\nBetween the time when a specific log entry is first written and when\nit is applied, other log entries could be applied which could remove\nsome objects and references. It could happen that these objects and\nreferences are needed to apply the specific log entry though.\n\nSo when we log an entry, we have to make sure that all the objects and\nreferences that it needs to be properly applied will not be removed\nuntil that entry is either actually applied or discarded.\n\nThe best way to make sure things are kept in Git is to create new Git\nreferences pointing to these things. So we decided to use temporary\nreferences for that purpose. They would be created when a log entry is\nwritten, and then deleted when that entry is either applied or\ndiscarded.\n\n### Packed-refs performance\nGit can store references in \"loose\" files, with one reference per\nfile, or in the `packed-refs` file, which contains many of them. The\n`git pack-refs` command is used to pack some references from \"loose\"\nfiles into the `packed-refs` file.\n\nFor reading a lot of references, the `packed-refs` file is very\nefficient, but for writing or deleting a single reference, it is not\nso efficient as rewriting the whole `packed-refs` file is required.\n\nAs temporary references are to be created and then deleted soon after,\nstoring them in the `packed-refs` file would not be efficient. It\nwould be better to store them in \"loose\" files.\n\nThe `git pack-refs` command had no way to be told precisely which refs\nshould be packed or not though. By default it would repack all the\ntags (which are refs in `refs/tags/`) and all the refs that are\nalready packed. With the `--all` option one could tell it to repack\nall the refs except the hidden refs, broken refs, and symbolic refs,\nbut that was the only thing that could be controlled.\n\n### Improving `git pack-refs`\nWe decided to improve `git pack-refs` by adding two new options to it:\n  - `--include \u003Cpattern>` which can be used to specify which refs should be packed\n  - `--exclude \u003Cpattern>` which can be used to specify which refs should not be packed\n\n[John Cai](https://gitlab.com/jcaigitlab), Gitaly:Git team engineering manager, implemented these options.\n\nFor example, if the refs managed by the write-ahead log are in\n`refs/wal/`, it's now possible the exclude them from being moved into\nthe `packed-refs` file by using:\n\n```shell\n$ git pack-refs --exclude \"refs/wal/*\"\n```\n\nDetails of the patch series, including discussions, can be found\n[here](https://lore.kernel.org/git/pull.1501.git.git.1683215331910.gitgitgadget@gmail.com/).\n\n## 2. Get machine-readable output from `git cat-file --batch`\n\n### Efficiently retrieving Git object information\nIn GitLab, we often retrieve Git object information. For example, when a\nuser navigates into the files and directories in a repository, we need\nto get the content of the corresponding Git blobs and trees so that\nwe can show it.\n\nIn Gitaly, we use `git cat-file` to retrieve Git object information\nfrom a Git repository. As it's a frequent operation, it needs to be\nperformed efficiently, so we use the batch modes of `git cat-file`\navailable through the `--batch`, `--batch-check` and `--batch-command`\noptions.\n\nIn these modes, a pointer to a Git object can be repeatedly sent to\nthe standard input, called 'stdin', of a `git cat-file` command, while\nthe corresponding object information is read from the standard ouput,\ncalled 'stdout' of the command. This way we don't need to launch a\nnew `git cat-file` command for each object.\n\nGitLab can keep, for example, a `git cat-file --batch-command` process\nrunning in the background while feeding it commands like\n`info \u003Cobject>` or `contents \u003Cobject>` through its stdin to\nget either information about an object or its content.\n\n### Newlines in stdin, stdout, and filenames\nThe commands or pointers to Git objects that are sent through stdin\nshould be delimited using newline characters, and in the same way `git\ncat-file` will use newline characters to delimit the information from\ndifferent Git objects in its output. This is a common shell practice\nto make it easy to chain commands together. For example, one can\neasily get the size (in bytes) of the last three commits on the current\nbranch using the following:\n\n```shell\n$ git log -3 --format='%H' | git cat-file --batch-check='%(objectsize)'\n285\n646\n428\n```\n\nSometimes, though, the pointer to a Git object can contain a filename\nor a directory name, as such a pointer is allowed to be in the form\n`\u003Cbranch>:\u003Cpath>`. For example `HEAD:Documentation` is a valid\npointer to the blob or the tree corresponding to the `Documentation`\npath on the current branch.\n\nThis used to be an issue because on some systems newline characters\nare allowed in file or directory names. So the `-z` option was\nintroduced last year in Git 2.38 to allow users to change the input\ndelimiter in batch modes to the NUL character.\n\n### Error output\nWhen the `-z` option was introduced, it wasn't considered useful to\nchange the output delimiter to be also the NUL character. This is\nbecause only tree objects can contain paths and the internal format\nof tree objects already uses NUL characters to delimit paths.\n\nUnfortunately, it was overlooked that in case of an error the pointer\nto the object is displayed in the error message:\n\n```shell\n$ echo 'HEAD:does-not-exist' | git cat-file --batch\nHEAD:does-not-exist missing\n```\n\nAs the error messages are printed along with the regular ouput of the\ncommand on stdout, passing in an invalid pointer with a number of\nnewline characters in it could make it very difficult to parse the\noutput.\n\n### -Z comes to the rescue\n[Toon Claes](https://gitlab.com/toon), Gitaly senior engineer, initially worked on a\npatch to just quote the pointer in the error message, but it was\ndecided in the Git mailing list discussions related to the patch that\nit would be better to just create a new `-Z` option. This option would\nchange both the input and the output delimiter to the NUL character,\nwhile the old `-z` option would be deprecated over time.\n\nSo [Patrick Steinhardt](https://gitlab.com/pks-gitlab), Gitaly staff engineer, implemented that new `-Z` option.\n\nDetails of the patch series, including discussions, can be found\n[here](https://lore.kernel.org/git/20221209150048.2400648-1-toon@iotcl.com/)\nand [here](https://lore.kernel.org/git/cover.1685710884.git.ps@pks.im/).\n\n## 3. Pass pseudo-options to `git rev-list --stdin`\n\n### Computing sizes\nIn GitLab, we need to have different ways to compute the size of Git\nrelated content. For example, we need to know:\n  - how much disk space a repository is using\n  - how big a specific Git object is\n  - how much additional space on a repository is required by a\n    specific set of revisions (and the objects they reference)\n\nKnowing \"how much disk space a repository is using\" is useful to\nenforce repository-related quotas and is easy to get using regular\nshell and OS features.\n\nSize information about a specific Git object is useful to enforce\nquotas related to maximum file size. It can be obtained using, for\nexample, `git cat-file -s \u003Cobject>` or\n`echo \u003Cobject> | git cat-file --batch-check='%(objectsize)'`\nas already seen above.\n\nComputing the space required by a set of revisions is useful, too, as\nforks can share Git content in what we call\n\"[pool repositories](https://docs.gitlab.com/ee/development/git_object_deduplication.html),\"\nand we want to discriminate how much content belongs to each forked\nrepository. Fortunately, `git rev-list` has a `--disk-usage` option\nfor this purpose.\n\n### Passing arguments to `git rev-list`\n`git rev-list` can take a number of different arguments and has a lot\nof different options. It's a fundamental command to traverse commit\ngraphs, and it should be flexible enough to fulfill a lot of different\nuser needs.\n\nWhen repositories grow, they often store a lot of references and a lot\nof files and directories, so there is often the need to pass a big\nnumber of references or paths as arguments to the\ncommand. References and paths can be quite long though.\n\nTo avoid hitting platform limits related to command line length, long\nago, a `--stdin` mode was added that allowed users to pass revisions\nand paths through stdin, instead of as command line\narguments. However, when that was implemented, it was not considered\nnecessary to allow options or pseudo-options, like `--not`,\n`--glob=...`, or `--all` to be passed through stdin.\n\nThis appeared to be a problem for GitLab, as for computing sizes for\nforked repositories we needed some of the pseudo-options, and it would\nhave been intricate and possibly buggy to pass some of them and their\narguments as arguments on the command line while others were passed\nthrough stdin.\n\n### Allowing pseudo-options\nTo fix this issue, Patrick Steinhardt implemented a small patch series to\nallow pseudo-options through stdin.\n\nWith it, in Git 2.42, one can now pass pseudo-options, like `--not`,\n`--glob=...`, or `--all` through stdin when the `--stdin` mode is used.\n\nDetails of the patch series, including discussions, can be found\n[here](https://lore.kernel.org/git/cover.1686744685.git.ps@pks.im/).\n\n## 4. Code and test improvements\nWhile looking at some Git code, we are often tempted to modify nearby\ncode, either to change only its style when the code is ancient and it\nwould look better using Git's current code style, or to refactor it to\nmake it cleaner. This is why we sometimes send small patch series that\ndon't have a real GitLab related purpose.\n\nIn Git 2.42, examples of style code improvements we made are the\n[part1](https://lore.kernel.org/git/pull.1513.git.git.1684440205.gitgitgadget@gmail.com/)\nand\n[part2](https://lore.kernel.org/git/pull.1514.git.git.1684599239.gitgitgadget@gmail.com/)\ntest code modernization patches from John Cai.\n\nAnd [here](https://lore.kernel.org/git/cover.1684324059.git.ps@pks.im/) is\nan example of a refactoring to cleanup some code by Patrick Steinhardt.\n",[23,24,25,26],"git","news","open 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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",[722,723],"patch releases","security releases",{"featured":12,"template":13,"externalUrl":725},"https://docs.gitlab.com/releases/patches/patch-release-gitlab-18-11-1-released/",{"content":727,"config":739},{"title":728,"description":729,"body":730,"category":9,"tags":731,"date":734,"authors":735,"heroImage":738},"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.",[275,732,733],"AWS","AI/ML","2026-04-21",[736,737],"Joe Mann","Mark Kriaf","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362275/ozbwn9tk0dditpnfddlz.png",{"featured":29,"template":13,"slug":740},"gitlab-amazon-platform-orchestration-on-a-trusted-ai-foundation",{"content":742,"config":751},{"title":743,"description":744,"authors":745,"heroImage":747,"date":748,"body":749,"category":9,"tags":750},"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.",[746],"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/).",[9,733,24],{"featured":12,"template":13,"slug":752},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"promotions":754},[755,769,780,792],{"id":756,"categories":757,"header":759,"text":760,"button":761,"image":766},"ai-modernization",[758],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":762,"config":763},"Get your AI maturity score",{"href":764,"dataGaName":765,"dataGaLocation":243},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":770,"categories":771,"header":772,"text":760,"button":773,"image":777},"devops-modernization",[9,568],"Are you just managing tools or shipping innovation?",{"text":774,"config":775},"Get your DevOps maturity score",{"href":776,"dataGaName":765,"dataGaLocation":243},"/assessments/devops-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":781,"categories":782,"header":784,"text":760,"button":785,"image":789},"security-modernization",[783],"security","Are you trading speed for security?",{"text":786,"config":787},"Get your security maturity score",{"href":788,"dataGaName":765,"dataGaLocation":243},"/assessments/security-modernization-assessment/",{"config":790},{"src":791},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":793,"paths":794,"header":797,"text":798,"button":799,"image":804},"github-azure-migration",[795,796],"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":800,"config":801},"See how GitLab compares to GitHub",{"href":802,"dataGaName":803,"dataGaLocation":243},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":805},{"src":779},{"header":807,"blurb":808,"button":809,"secondaryButton":814},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":810,"config":811},"Get your free trial",{"href":812,"dataGaName":50,"dataGaLocation":813},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":815},{"href":54,"dataGaName":55,"dataGaLocation":813},1777302621862]