[{"data":1,"prerenderedAt":811},["ShallowReactive",2],{"/en-us/blog/reduce-the-load-on-gitlab-gitaly-with-bundle-uri":3,"navigation-en-us":34,"banner-en-us":444,"footer-en-us":454,"blog-post-authors-en-us-Olivier Campeau":694,"blog-related-posts-en-us-reduce-the-load-on-gitlab-gitaly-with-bundle-uri":708,"blog-promotions-en-us":748,"next-steps-en-us":801},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":11,"meta":26,"navigation":27,"path":28,"publishedDate":18,"seo":29,"stem":30,"tagSlugs":31,"__hash__":33},"blogPosts/en-us/blog/reduce-the-load-on-gitlab-gitaly-with-bundle-uri.yml","Reduce The Load On Gitlab Gitaly With Bundle Uri",[7],"olivier-campeau",null,"product",{"featured":11,"template":12,"slug":13},false,"BlogPost","reduce-the-load-on-gitlab-gitaly-with-bundle-uri",{"title":15,"description":16,"heroImage":17,"date":18,"body":19,"category":9,"tags":20,"authors":23},"Reduce the load on GitLab Gitaly with bundle URI","Discover what the bundle URI Git feature is, how it is integrated into Gitaly, configuration best practices, and how GitLab users can benefit from it.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2025-06-24","Gitaly plays a vital role in the GitLab ecosystem — it is the server component that handles all Git operations. Every push and pull made to/from a repository is handled by Gitaly, which has direct access to the disk where the actual repositories are stored. As a result, when Gitaly is under heavy load, some operations like CI/CD pipelines and browsing a repository in the\nGitLab UI can become quite slow. This is particularly true when serving clones and fetches for large and busy monorepos, which can consume large amounts of CPU and memory.\n\n[Bundle URI](https://docs.gitlab.com/administration/gitaly/bundle_uris/) takes significant load off of Gitaly servers during clones by allowing Git to pre-download a bundled repository from object storage before calling the Gitaly servers to fetch the remaining objects.\n\nHere is a graph that shows the difference between clones without and with bundle URI.\n\n![Graph that shows the difference between clones without and with bundle URI](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750705069/rvbm4ru1w58msd6zv4x7.png)\n\nThis graph shows the results of a small test we ran on an isolated GitLab installation, with Gitaly running on a machine with 2 CPUs. We wanted to test bundle URI with a large repository, so we pushed the [GitLab repository](https://gitlab.com/gitlab-org/gitlab) to the instance. We also generated a bundle beforehand.\n\nThe big CPU spike is from when we performed a single clone of the GitLab repository with bundle URI disabled. It's quite noticeable. A little later, we turned on bundle URI and launched three concurrent clones of the GitLab repository. Sure enough, turning on bundle URI provides massive performance gain. We can't even distinguish the CPU usage of the three clones from normal usage.\n\n## Configure Gitaly to use bundle URI\n\nTo enable bundle URI on your GitLab installation, there are a couple of things you need to configure.\n\n### Create a cloud bucket\n\nBundles need to be stored somewhere. The ideal place is in a cloud storage bucket. Gitaly uses the [gocloud.dev](https://pkg.go.dev/gocloud.dev) library to read and write from cloud storage. Any cloud storage solution supported by this library can be used. Once you have a cloud bucket URL, you can add it in the Gitaly configuration here:\n\n```toml\n[bundle_uri]\ngo_cloud_url = \"\u003Cbucket-uri>\"\n```\n\nIt must be noted that Gitaly does not manage the lifecycle of the bundles stored in the bucket. To avoid cost issues, object lifecycle policies must be enabled on the bucket in order to delete unused or old objects.\n\n### Enable the feature flags\n\nThere are two feature flags to enable:\n\n- `gitaly_bundle_generation` enables [auto-generation](#auto-generated) of bundles.\n\n- `gitaly_bundle_uri` makes Gitaly advertise bundle URIs when they are available (either manually created or auto-generated) and allows the user to [manually](#manual) generate bundles.\n\nThese feature flags can be enabled at-large on a GitLab installation, or per repository. See the [documentation on how to enable a GitLab feature behind a feature flag](https://docs.gitlab.com/administration/feature_flags/#how-to-enable-and-disable-features-behind-flags).\n\n### How to generate bundles\n\nGitaly offers two ways for users to use bundle URI: a [manual](#manual) way and an [auto-generated](#auto-generated) way.\n\n#### Manual\n\nIt is possible to create a bundle manually by connecting over SSH with the Gitaly node that stores the repository you want to create a bundle for, and run the following command:\n\n```shell\nsudo -u git -- /opt/gitlab/embedded/bin/gitaly bundle-uri \n--config=\u003Cconfig-file>\n--storage=\u003Cstorage-name>\n--repository=\u003Crelative-path>\n```\n\nThis command will create a bundle for the given repository and store it into the bucket configured above. When a subsequent `git clone` request will reach Gitaly for the same repository, the bundle URI mechanism described above will come into play.\n\n#### Auto-generated\n\nGitaly can also generate bundles automatically, using a heuristic to determine if it is currently handling frequent clones for the same repository.\n\nThe current heuristic keeps track of the number of times a `git fetch` request is issued for each repository. If the number of requests reaches a certain `threshold` in a given time `interval`, a bundle is automatically generated. Gitaly also keeps track of the last time it generated a bundle for a repository. When a new bundle should be regenerated, based on the `threshold` and `interval`, Gitaly looks at the last time a bundle was generated for the given repository. It will only generate a new bundle if the existing bundle is older than some `maxBundleAge` configuration. The old bundle is overwritten. There can only be one bundle per repository in cloud storage.\n\n## Using bundle URI\n\nWhen a bundle exists for a repository, it can be used by the `git clone` command.\n\n### Cloning from your terminal\n\nTo clone a repository from your terminal, make sure your Git configuration enables bundle URI. The configuration can be set like so:\n\n```shell\ngit config --global transfer.bundleuri true\n```\n\nTo verify that bundle URI is used during a clone, you can run the `git clone` command with `GIT_TRACE=1` and see if your bundle is being downloaded:\n```shell\n➜  GIT_TRACE=1 git clone https://gitlab.com/gitlab-org/gitaly\n...\n14:31:42.374912 run-command.c:667       trace: run_command: git-remote-https '\u003Cbundle-uri>'\n...\n```\n\n### Cloning during CI/CD pipelines\n\nOne scenario where using bundle URI would be beneficial is during a CI/CD pipeline, where each job needs a copy of the repository in order to run. Cloning a repository during a CI/CD pipeline is the same as cloning a repository from your terminal, except that the Git client in this case is the GitLab Runner. Thus, we need to configure the GitLab Runner in such a way that it can use bundle URI.\n\n**1. Update the helper-image**\n\nThe first thing to do to configure the GitLab Runner is to [overwrite the helper-image](https://docs.gitlab.com/runner/configuration/advanced-configuration/#override-the-helper-image) that your GitLab Runner instances use. The `helper-image` is the image that is used to run the process of cloning a repository before the job starts. To use bundle URI, the image needs the following:\n\n- Git Version 2.49.0 or later\n\n- [`GitLab Runner helper`](https://gitlab.com/gitlab-org/gitlab-runner/-/tree/main/apps/gitlab-runner-helper?ref_type=heads) Version 18.1.0 or later\n\nThe helper-images can be found [here](https://gitlab.com/gitlab-org/gitlab-runner/container_registry/1472754?orderBy=PUBLISHED_AT&sort=desc&search[]=v18.1.0). Select an image that corresponds to the OS distribution and the architecture you use for your GitLab Runner instances, and verify that the image satisfies the requirements.\n\nAt the time of writing, the `alpine-edge-\u003Carch>-v18.1.0*` tag meets all requirements.\n\nYou can validate the image meets all requirements with:\n\n```shell\ndocker run -it \u003Cimage:tag>\n$ git version ## must be 2.49.0 or newer\n$ gitlab-runner-helper -v ## must be 18.0 or newer\n```\n\nIf you do not find an image that meets the requirements, you can also use the helper-image as a base image and install the requirements yourself in a custom-built image that you can host on [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/).\n\nOnce you have found the image you need, you must configure your GitLab Runner instances to use it by updating your `config.toml` file:\n\n```toml\n[[runners]]\n  (...)\n  executor = \"docker\"\n  [runners.docker]\n    (...)\n    helper_image = \"image:tag\" ## \u003C-- put the image name and tag here\n```\n\nOnce the configuration is changed, you must restart the runners for the new configuration to take effect.\n\n**2. Turn on the feature flag**\n\nNext, you must enable the `FF_USE_GIT_NATIVE_CLONE` [GitLab Runner feature flags](https://docs.gitlab.com/runner/configuration/feature-flags/) in your `.gitlab-ci.yml` file. To do that, simply add it as a variable and set to `true` :\n\n```yaml\nvariables:\n  FF_USE_GIT_NATIVE_CLONE: \"true\"\n```\n\nThe `GIT_STRATEGY` must also be [set to `clone`](\u003Chttps://docs.gitlab.com/ci/runners/configure_runners/#git-strategy>), as Git bundle URI only works with `clone` commands.\n\n## How bundle URI works\n\nWhen a user clones a repository with the `git clone` command, a process called [`git-receive-pack`](https://git-scm.com/docs/git-receive-pack) is launched on the client's machine. This process communicates with the remote repository's server (it can be over HTTP/S, SSH, etc.) and asks to start a [`git-upload-pack`](https://git-scm.com/docs/git-receive-pack) process. Those two processes then exchange information using the Git protocol (it must be noted that bundle URI is only supported with [Git protocol v2](https://git-scm.com/docs/protocol-v2)). The capabilities both processes support and the references and objects the client needs are among the information exchanged. Once the Git server has determined which objects to send to the client, it must package them into a packfile, which, depending on the size of the data it must process, can consume a good amount of resources.\n\nWhere does bundle URI fit into this interaction? If bundle URI is advertised as a capability from the `upload-pack` process and the client supports bundle URI, the Git client will ask the server if it knows about any bundle URIs. The server sends those URIs back and the client downloads those bundles.\n\nHere is a diagram that shows those interactions:\n\n```mermaid\n\nsequenceDiagram\n\n\n    participant receive as Client\n\n\n    participant upload as Server\n\n\n    participant cloud as File server\n\n\n    receive ->> upload: issue git-upload-pack\n\n\n    upload -->> receive: list of server capabilities\n\n\n    opt if bundle URI is advertised as a capability\n\n\n    receive ->> upload: request bundle URI\n\n\n    upload -->> receive: bundle URI\n\n\n    receive ->> cloud: download bundle at URI\n\n\n    cloud -->> receive: bundle file\n\n\n    receive ->> receive: clone from bundle\n\n\n    end\n\n\n    receive ->> upload: requests missing references and objects\n\n\n    upload -->> receive: packfile data\n\n```\n\nAs such, Git [bundle URI](https://git-scm.com/docs/bundle-uri) is a mechanism by which, during a `git clone`, a Git server can advertise the URI of a bundle for the repository being cloned by the Git client. When that is the case, the Git client can clone the repository from the bundle and request from the Git server only the missing references or objects that were not part of the bundle. This mechanism really helps to alleviate pressure from the Git server.\n\n## Alternatives\n\nGitLab also has a feature [Pack-objects cache](https://docs.gitlab.com/administration/gitaly/configure_gitaly/#pack-objects-cache). This feature works slightly differently than bundle URI. When the server packs objects together into a so-called packfile, this feature will keep that file in the cache. When another client needs the same set of objects, it doesn't need to repack them, but it can just send the same packfile again.\n\nThe feature is only beneficial when many clients request the exact same set of objects. In a repository that is quick-changing, this feature might not give any improvements. With bundle URI, it doesn't matter if the bundle is slightly out-of-date because the client can request missing objects after downloading the bundle and apply those changes on top. Also bundle URI in Gitaly stores the bundles on external storage, which the Pack-objects Cache stores them on the Gitaly node, so using the latter doesn't reduce network and I/O load on the Gitaly server.\n\n## Try bundle URI today\n\nYou can try the bundle URI feature in one of the following ways:\n\n* Download a [free trial version of GitLab Ultimate](https://about.gitlab.com/free-trial/).\n\n* If you already run a self-hosted GitLab installation, upgrade to 18.1.\n\n* If you can't upgrade to 18.1 at this time, [download GitLab](https://about.gitlab.com/install/) to a local machine.",[9,21,22],"DevSecOps","git",[24],"Olivier 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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",[716,717],"patch releases","security releases",{"featured":11,"template":12,"externalUrl":719},"https://docs.gitlab.com/releases/patches/patch-release-gitlab-18-11-1-released/",{"content":721,"config":733},{"title":722,"description":723,"body":724,"category":9,"tags":725,"date":728,"authors":729,"heroImage":732},"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.",[271,726,727],"AWS","AI/ML","2026-04-21",[730,731],"Joe Mann","Mark Kriaf","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362275/ozbwn9tk0dditpnfddlz.png",{"featured":27,"template":12,"slug":734},"gitlab-amazon-platform-orchestration-on-a-trusted-ai-foundation",{"content":736,"config":746},{"title":737,"description":738,"authors":739,"heroImage":741,"date":742,"body":743,"category":9,"tags":744},"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.",[740],"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/).",[9,727,745],"news",{"featured":11,"template":12,"slug":747},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"promotions":749},[750,764,775,787],{"id":751,"categories":752,"header":754,"text":755,"button":756,"image":761},"ai-modernization",[753],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":757,"config":758},"Get your AI maturity score",{"href":759,"dataGaName":760,"dataGaLocation":238},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":762},{"src":763},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":765,"categories":766,"header":767,"text":755,"button":768,"image":772},"devops-modernization",[9,32],"Are you just managing tools or shipping innovation?",{"text":769,"config":770},"Get your DevOps maturity score",{"href":771,"dataGaName":760,"dataGaLocation":238},"/assessments/devops-modernization-assessment/",{"config":773},{"src":774},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":776,"categories":777,"header":779,"text":755,"button":780,"image":784},"security-modernization",[778],"security","Are you trading speed for security?",{"text":781,"config":782},"Get your security maturity score",{"href":783,"dataGaName":760,"dataGaLocation":238},"/assessments/security-modernization-assessment/",{"config":785},{"src":786},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":788,"paths":789,"header":792,"text":793,"button":794,"image":799},"github-azure-migration",[790,791],"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":795,"config":796},"See how GitLab compares to GitHub",{"href":797,"dataGaName":798,"dataGaLocation":238},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":800},{"src":774},{"header":802,"blurb":803,"button":804,"secondaryButton":809},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":805,"config":806},"Get your free trial",{"href":807,"dataGaName":45,"dataGaLocation":808},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":500,"config":810},{"href":49,"dataGaName":50,"dataGaLocation":808},1777302609271]