[{"data":1,"prerenderedAt":812},["ShallowReactive",2],{"/en-us/blog/a-guide-to-the-breaking-changes-in-gitlab-19-0":3,"navigation-en-us":33,"banner-en-us":443,"footer-en-us":453,"blog-post-authors-en-us-Martin Brümmer":695,"blog-related-posts-en-us-a-guide-to-the-breaking-changes-in-gitlab-19-0":710,"blog-promotions-en-us":749,"next-steps-en-us":802},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":24,"isFeatured":11,"meta":25,"navigation":26,"path":27,"publishedDate":15,"seo":28,"stem":30,"tagSlugs":31,"__hash__":32},"blogPosts/en-us/blog/a-guide-to-the-breaking-changes-in-gitlab-19-0.yml","A Guide To The Breaking Changes In Gitlab 19 0",[7],"martin-brmmer",null,"product",{"featured":11,"template":12,"slug":13},false,"BlogPost","a-guide-to-the-breaking-changes-in-gitlab-19-0",{"date":15,"body":16,"category":9,"tags":17,"authors":19,"title":21,"description":22,"heroImage":23},"2026-04-15","GitLab 17.0 shipped with 80 breaking changes. GitLab 18.0 had 27. The upcoming GitLab 19.0 release is projected to include 15.\n\nWe know that managing breaking changes across a major upgrade is time-consuming: It requires investigation and coordination across your organization. In response, we introduced a [breaking change approval requirement](https://docs.gitlab.com/development/deprecation_guidelines/#how-do-i-get-approval-to-move-forward-with-a-breaking-change) that mandates impact mitigation and leadership sign-off before any breaking change can proceed. That process is working, and we're committed to continuing to drive that number down.\n\nBelow you'll find every breaking change in GitLab 19.0, organized by deployment type and impact, alongside the mitigation steps you need to upgrade with confidence.\n\n## Deployment windows\n\nHere are the deployment windows you need to know.\n \n### GitLab.com\n \nBreaking changes for GitLab.com will be limited to these two windows:\n \n- **May 4–6, 2026** (09:00–22:00 UTC) — primary window\n- **May 11–13, 2026** (09:00–22:00 UTC) — contingency fallback\n \nMany other changes will continue to roll out throughout the month. You can learn more about the breaking changes occurring within each of these windows in the [breaking changes documentation](https://docs.gitlab.com/update/breaking_windows/).\n \n**Note:** Breaking changes may fall slightly outside of these windows in exceptional circumstances.\n \n### GitLab Self-Managed\n \nGitLab 19.0 will be available starting on May 21, 2026.\n\n> Learn more about the [release schedule](https://about.gitlab.com/releases/).\n \n### GitLab Dedicated\n \nThe upgrade to GitLab 19.0 will take place during your assigned maintenance window. You can learn more and find your assigned maintenance window in your Switchboard portal. GitLab Dedicated instances are kept on release N-1, so the upgrade to GitLab 19.0 will take place in the maintenance window during the week of June 22, 2026.\n\nVisit the [Deprecations page](https://docs.gitlab.com/update/deprecations/?removal_milestone=19.0&breaking_only=true) to see a full list of items scheduled for removal in GitLab 19.0. Read on to learn what's coming and how to prepare for this year's release based on your specific deployment.\n \n## Breaking changes\n\nHere are the breaking changes that are high impact.\n \n### High impact\n \n**1. Support for NGINX Ingress replaced by Gateway API with Envoy Gateway**\n \n_GitLab Self-Managed (Helm chart)_\n \nThe GitLab Helm chart has bundled NGINX Ingress as the default networking component. NGINX Ingress reached end-of-life in March 2026, and GitLab is now transitioning to Gateway API with Envoy Gateway as the new default.\n \nStarting with GitLab 19.0, Gateway API and the bundled Envoy Gateway become the default networking configuration. If migration to Envoy Gateway is not immediately feasible for your deployment, you can explicitly re-enable the bundled NGINX Ingress, which remains available until its planned removal in GitLab 20.0.\n \nThis change does not impact:\n- The NGINX used in the Linux package\n- GitLab Helm chart and GitLab Operator instances that use an externally managed Ingress or Gateway API controller\n \nGitLab will provide best-effort security maintenance for the forked NGINX Ingress chart and builds until full removal. To ensure a smooth transition, plan your migration to the provided Gateway API solution or an externally managed Ingress controller ahead of the 19.0 upgrade.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/work_items/590800)\n \n\n**2. Removal of bundled PostgreSQL, Redis, and MinIO from the GitLab Helm chart**\n \n_GitLab Self-Managed (Helm chart)_\n \nThe GitLab Helm chart has long bundled Bitnami PostgreSQL, Bitnami Redis, and a fork of the official MinIO chart to make setting up GitLab easier in proof-of-concept and test environments. Due to changes in licensing, project maintenance, and public image availability, these components will be removed from the GitLab Helm chart and GitLab Operator with no replacement.\n \nThese charts are explicitly documented as not recommended for production usage. Their sole purpose was to enable quick-start test environments.\n \nIf you are running an instance with the bundled PostgreSQL, Redis, or MinIO, follow the [migration guide](https://docs.gitlab.com/charts/installation/migration/bundled_chart_migration/) to configure external services before upgrading to GitLab 19.0. The Redis and PostgreSQL provided by the Linux package are not impacted by this change.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/work_items/590797)\n \n\n**3. Resource Owner Password Credentials (ROPC) OAuth grant removed**\n \n_GitLab.com | Self-Managed | Dedicated_\n \nSupport for the Resource Owner Password Credentials (ROPC) grant as an OAuth flow will be fully removed in GitLab 19.0. This aligns with the OAuth RFC Version 2.1 standard, which removes ROPC due to its inherent security limitations.\n \nGitLab has already required client authentication for ROPC on GitLab.com since April 8, 2025. An administrator setting was added in 18.0 to allow controlled opt-out ahead of the removal.\n \nAfter the 19.0 upgrade, ROPC cannot be used under any circumstances, even with client credentials. Any applications or integrations using this grant type must migrate to a supported OAuth flow — such as the Authorization Code flow — before upgrading.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/issues/457353)\n \n**4. PostgreSQL 16 no longer supported — PostgreSQL 17 is the new minimum**\n \n_GitLab Self-Managed_\n \nGitLab follows an [annual upgrade cadence for PostgreSQL](https://handbook.gitlab.com/handbook/engineering/infrastructure-platforms/data-access/database-framework/postgresql-upgrade-cadence/). In GitLab 19.0, PostgreSQL 17 becomes the minimum required version, and support for PostgreSQL 16 is removed.\n \nPostgreSQL 17 is available as of GitLab 18.9, so you can upgrade at any time before the 19.0 release.\n \nFor instances running a single PostgreSQL instance installed via the Linux package, an automatic upgrade to PostgreSQL 17 may be attempted during the 18.11 upgrade. Ensure you have sufficient disk space to accommodate the upgrade.\n \nFor instances using PostgreSQL Cluster, or those that opt out of the automated upgrade, a manual upgrade to PostgreSQL 17 is required before upgrading to GitLab 19.0.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/issues/589774) | [Upgrade guide](https://docs.gitlab.com/omnibus/settings/database/#upgrade-packaged-postgresql-server)\n \n\n \n### Medium impact\n\nHere are the breaking changes that are medium impact.\n \n**1. Linux package support for Ubuntu 20.04 discontinued**\n \n_GitLab Self-Managed_\n \nUbuntu standard support for Ubuntu 20.04 ended in May 2025. In accordance with GitLab's [Linux package supported platforms policy](https://docs.gitlab.com/install/package/#supported-platforms), packages are dropped once a vendor stops supporting the operating system.\n \nFrom GitLab 19.0, packages will no longer be provided for Ubuntu 20.04. GitLab 18.11 will be the last release with Linux packages for this distribution.\n \nIf you currently run GitLab on Ubuntu 20.04, you must upgrade to Ubuntu 22.04 or another [supported operating system](https://docs.gitlab.com/install/package/#supported-platforms) before upgrading to GitLab 19.0. Canonical provides an [upgrade guide](https://documentation.ubuntu.com/server/how-to/software/upgrade-your-release/) to help with the migration.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/omnibus-gitlab/-/issues/8915)\n \n\n**2. Support for Redis 6 removed**\n \n_GitLab Self-Managed_\n \nIn GitLab 19.0, support for Redis 6 is removed. Before upgrading, instances using an external Redis 6 deployment must migrate to either Redis 7.2 or Valkey 7.2, which is available in beta from GitLab 18.9 with general availability planned for GitLab 19.0.\n \nThe bundled Redis included with the Linux package has used Redis 7 since GitLab 16.2 and is not affected. Only instances using an external Redis 6 deployment must act.\n \nMigration resources are available for common platforms:\n \n- **AWS ElastiCache:** Upgrade to [Redis 7.2 or Valkey 7.2](https://docs.aws.amazon.com/AmazonElastiCache/latest/dg/supported-engine-versions.html)\n- **GCP Memorystore:** Upgrade to [Redis 7.2 or Valkey 7.2](https://cloud.google.com/memorystore/docs/redis/supported-versions)\n- **Azure Cache for Redis:** Managed Redis 7.2 or Valkey 7.2 is not yet available on Azure. You can self-host on Azure VMs or AKS, or use the Linux package installation, which will support Valkey 7.2 with GitLab 19.0 GA.\n- **Self-hosted:** Upgrade your Redis 6 instance to Redis 7.2 or Valkey 7.2.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/work_items/585839) | [Requirements documentation](https://docs.gitlab.com/install/requirements/)\n \n\n \n**3. `heroku/builder:22` image replaced by `heroku/builder:24`**\n \n_GitLab.com | Self-Managed | Dedicated_\n \nThe cloud-native buildpack (CNB) builder image used in Auto DevOps has been updated to `heroku/builder:24`. This affects pipelines that use the [`auto-build-image`](https://gitlab.com/gitlab-org/cluster-integration/auto-build-image) provided by the [Auto Build stage of Auto DevOps](https://docs.gitlab.com/topics/autodevops/stages/#auto-build).\n \nWhile most workloads will be unaffected, this may be a breaking change for some users. Before upgrading, review the [Heroku-24 stack release notes](https://devcenter.heroku.com/articles/heroku-24-stack#what-s-new) and [upgrade notes](https://devcenter.heroku.com/articles/heroku-24-stack#upgrade-notes) to assess your impact.\n \nIf you need to continue using `heroku/builder:22` after GitLab 19.0, set the CI/CD variable `AUTO_DEVOPS_BUILD_IMAGE_CNB_BUILDER` to `heroku/builder:22`.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/cluster-integration/auto-build-image/-/issues/79)\n\n\n**4. Mattermost removed from the Linux package**\n \n_GitLab Self-Managed_\n \nIn GitLab 19.0, bundled Mattermost is removed from the Linux package. Mattermost was first bundled with GitLab in 2015, but has since matured its own standalone deployment options. Additionally, with Mattermost v11, [GitLab SSO was deprecated from their free offering](https://forum.mattermost.com/t/mattermost-v11-changes-in-free-offerings/25126), reducing the value of the bundled integration.\n \nCustomers not using the bundled Mattermost will not be impacted. If you currently use it, refer to [Migrating from GitLab Omnibus to Mattermost Standalone](https://docs.mattermost.com/administration-guide/onboard/migrate-gitlab-omnibus.html) in the Mattermost documentation for migration instructions.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/work_items/590798)\n \n\n \n**5. Linux package support for SUSE distributions discontinued**\n \n_GitLab Self-Managed_\n \nIn GitLab 19.0, Linux package support for SUSE distributions ends. This affects:\n \n- openSUSE Leap 15.6\n- SUSE Linux Enterprise Server 12.5\n- SUSE Linux Enterprise Server 15.6\n \nGitLab 18.11 will be the last version with Linux packages for these distributions. The recommended path forward is to migrate to a [Docker deployment of GitLab](https://docs.gitlab.com/install/docker/installation/) on your existing distribution, avoiding the need to change your underlying operating system to continue receiving upgrades.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/work_items/590801)\n \n\n \n### Low impact\n\nHere are the breaking changes that are low impact.\n \n**1. Spamcheck removed from Linux package and GitLab Helm chart**\n \n_GitLab Self-Managed_\n \nIn GitLab 19.0, [Spamcheck](https://docs.gitlab.com/administration/reporting/spamcheck/) is removed from the Linux package and GitLab Helm chart. It is primarily relevant to large public instances, which is an edge case in GitLab's customer base. The removal reduces package size and dependency footprint for the majority of customers.\n \nCustomers not currently using Spamcheck will not be impacted. If you currently use the bundled Spamcheck, you can deploy it separately using [Docker](https://gitlab.com/gitlab-org/gl-security/security-engineering/security-automation/spam/spamcheck). No data migration is required.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/work_items/590796)\n \n\n**2. Slack slash commands integration removed**\n \n_GitLab Self-Managed | Dedicated_\n \nThe [Slack slash commands integration](https://docs.gitlab.com/user/project/integrations/slack_slash_commands/) is deprecated in favor of the [GitLab for Slack app](https://docs.gitlab.com/user/project/integrations/gitlab_slack_application/), which provides a more secure integration with the same capabilities.\n \nFrom GitLab 19.0, users will no longer be able to configure or use Slack slash commands. This integration only exists on GitLab Self-Managed and GitLab Dedicated — GitLab.com users are not affected.\n \nTo check if your instance is impacted, see the [impact check guidance](https://gitlab.com/gitlab-org/gitlab/-/work_items/569345#am-i-impacted).\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/work_items/569345)\n \n\n**3. Bitbucket Cloud import via API no longer supports app passwords**\n \n_GitLab.com | Self-Managed | Dedicated_\n \nAtlassian has deprecated app passwords (username and password authentication) for Bitbucket Cloud and has announced that this authentication method will stop working on June 9, 2026.\n \nFrom GitLab 19.0, importing repositories from Bitbucket Cloud through the GitLab API requires [user API tokens](https://support.atlassian.com/organization-administration/docs/understand-user-api-tokens/) instead of app passwords. Users importing from Bitbucket Server, or from Bitbucket Cloud through the GitLab UI, are not affected.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/work_items/588961) | [Impact check](https://gitlab.com/gitlab-org/gitlab/-/work_items/588961#am-i-impacted)\n \n**4. Trending tab removed from Explore projects page**\n \n_GitLab.com | Self-Managed | Dedicated_\n \nThe **Trending** tab in **Explore > Projects** and its associated GraphQL arguments are removed in GitLab 19.0. The trending algorithm only considers public projects, making it ineffective for Self-Managed instances that primarily use internal or private project visibility.\n \nIn the month before the GitLab 19.0 release, the **Trending** tab on GitLab.com will redirect to the **Active** tab sorted by stars in descending order.\n \nAlso removed: the `trending` argument in the `Query.adminProjects`, `Query.projects`, and `Organization.projects` GraphQL types.\n \n[Deprecation notice](https://gitlab.com/groups/gitlab-org/-/work_items/18493)\n \n\n**5. Container registry storage driver updates**\n \n_GitLab Self-Managed_\n \nTwo legacy container registry storage drivers are being replaced in GitLab 19.0:\n \n- **Azure storage driver:** The legacy `azure` driver becomes an alias for the new `azure_v2` driver. No manual action is required, but proactive migration is recommended for improved reliability and performance. See the [object storage documentation](https://docs.gitlab.com/administration/packages/container_registry/#use-object-storage) for migration steps. [Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/issues/523096)\n \n- **S3 storage driver (AWS SDK v1):** The legacy `s3` driver becomes an alias for the new `s3_v2` driver. The `s3_v2` driver does not support Signature Version 2 — any `v4auth: false` configuration will be transparently ignored. Migrate to Signature Version 4 before upgrading. [Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/issues/523095)\n \n\n**6. `ciJobTokenScopeAddProject` GraphQL mutation removed**\n \n_GitLab.com | Self-Managed | Dedicated_\n \nThe `ciJobTokenScopeAddProject` GraphQL mutation is deprecated in favor of `ciJobTokenScopeAddGroupOrProject`, introduced alongside the CI/CD job token scope changes in GitLab 18.0. Update any automation or tooling using the deprecated mutation before upgrading.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/issues/474175)\n\n \n**7. `ci_job_token_scope_enabled` projects API attribute removed**\n \n_GitLab.com | Self-Managed | Dedicated_\n \nThe `ci_job_token_scope_enabled` attribute in the [Projects REST API](https://docs.gitlab.com/api/projects/) is removed in GitLab 19.0. This attribute was deprecated in GitLab 18.0 when the underlying setting was removed, and has since always returned `false`.\n \nTo control CI/CD job token access, use the [CI/CD job token project settings](https://docs.gitlab.com/ci/jobs/ci_job_token/#control-job-token-access-to-your-project).\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/issues/423091)\n \n\n \n**8. Unauthenticated Projects API pagination limit enforced on GitLab.com**\n \n_GitLab.com_\n \nTo maintain platform stability and ensure consistent performance, a maximum offset limit of 50,000 will be enforced for all unauthenticated requests to the Projects List REST API on GitLab.com. For example, the `page` parameter will be limited to 2,500 pages when retrieving 20 results per page.\n \nWorkflows requiring access to more data must use keyset-based pagination parameters. This limit applies only to GitLab.com. On GitLab Self-Managed and GitLab Dedicated, the offset limit will be disabled by default behind a feature flag.\n \n[Deprecation notice](https://gitlab.com/gitlab-org/gitlab/-/work_items/585176)\n \n## Resources to manage your impact\n \nWe've developed specific tooling to help customers understand how these planned changes impact their GitLab instance(s). Once you've assessed your impact, we recommend reviewing the mitigation steps provided in the documentation relevant to each change to ensure a smooth transition to GitLab 19.0.\n \n**[GitLab Detective](https://gitlab.com/gitlab-com/support/toolbox/gitlab-detective) (Self-Managed only):** This experimental tool automatically checks a GitLab installation for known issues by looking at config files and database values. Note: it must run directly on your GitLab nodes.\n \nIf you have a paid plan and have questions or require assistance with these changes, please open a support ticket on the [GitLab Support Portal](https://support.gitlab.com/).\n \nIf you are a free GitLab.com user, you can access additional support through community sources such as [GitLab Documentation](https://docs.gitlab.com/), the [GitLab Community Forum](https://forum.gitlab.com/), and [Stack Overflow](https://stackoverflow.com/questions/tagged/gitlab).\n",[9,18],"news",[20],"Martin Brümmer","A guide to the breaking changes in GitLab 19.0","GitLab 19.0 removes several deprecated features. 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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.",[270,728,729],"AWS","AI/ML","2026-04-21",[732,733],"Joe Mann","Mark Kriaf","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776362275/ozbwn9tk0dditpnfddlz.png",{"featured":26,"template":12,"slug":736},"gitlab-amazon-platform-orchestration-on-a-trusted-ai-foundation",{"content":738,"config":747},{"title":739,"description":740,"authors":741,"heroImage":743,"date":744,"body":745,"category":9,"tags":746},"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.",[742],"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/).",[9,729,18],{"featured":11,"template":12,"slug":748},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"promotions":750},[751,765,776,788],{"id":752,"categories":753,"header":755,"text":756,"button":757,"image":762},"ai-modernization",[754],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":758,"config":759},"Get your AI maturity score",{"href":760,"dataGaName":761,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":763},{"src":764},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":766,"categories":767,"header":768,"text":756,"button":769,"image":773},"devops-modernization",[9,563],"Are you just managing tools or shipping innovation?",{"text":770,"config":771},"Get your DevOps maturity score",{"href":772,"dataGaName":761,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":774},{"src":775},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":777,"categories":778,"header":780,"text":756,"button":781,"image":785},"security-modernization",[779],"security","Are you trading speed for security?",{"text":782,"config":783},"Get your security maturity score",{"href":784,"dataGaName":761,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":786},{"src":787},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":789,"paths":790,"header":793,"text":794,"button":795,"image":800},"github-azure-migration",[791,792],"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":796,"config":797},"See how GitLab compares to GitHub",{"href":798,"dataGaName":799,"dataGaLocation":237},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":801},{"src":775},{"header":803,"blurb":804,"button":805,"secondaryButton":810},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":806,"config":807},"Get your free trial",{"href":808,"dataGaName":44,"dataGaLocation":809},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":499,"config":811},{"href":48,"dataGaName":49,"dataGaLocation":809},1777302618164]