[{"data":1,"prerenderedAt":805},["ShallowReactive",2],{"/en-us/blog/exact-code-search-find-code-faster-across-repositories":3,"navigation-en-us":34,"banner-en-us":444,"footer-en-us":454,"blog-post-authors-en-us-Dmitry Gruzd":696,"blog-related-posts-en-us-exact-code-search-find-code-faster-across-repositories":710,"blog-promotions-en-us":742,"next-steps-en-us":795},{"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":20,"seo":29,"stem":30,"tagSlugs":31,"__hash__":33},"blogPosts/en-us/blog/exact-code-search-find-code-faster-across-repositories.yml","Exact Code Search Find Code Faster Across Repositories",[7],"dmitry-gruzd",null,"product",{"featured":11,"template":12,"slug":13},false,"BlogPost","exact-code-search-find-code-faster-across-repositories",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Exact Code Search: Find code faster across repositories","Discover how this new GitLab feature can find exact matches, use regex patterns, and see contextual results across terabytes of codebases.",[18],"Dmitry Gruzd","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675154/Blog/Hero%20Images/blog-image-template-1800x945__8_.png","2025-06-25","**TL;DR:** What if you could find any line of code across 48 TB of repositories in milliseconds? GitLab's new [Exact Code\nSearch](https://docs.gitlab.com/ee/user/search/exact_code_search.html) makes this possible, delivering pinpoint precision, powerful regex support, and contextual multi-line results that transform how teams work with large codebases.\n\n## Why traditional code search is challenging\n\nAnyone who works with code knows the frustration of searching across repositories. Whether you're a developer debugging an issue, a DevOps engineer examining configurations, a security analyst searching for vulnerabilities, a technical writer updating documentation, or a manager reviewing implementation, you know exactly what you need, but traditional search tools often fail you.\n\nThese conventional tools return dozens of false positives, lack the context needed to understand results, and slow to a crawl as codebases grow. The result? Valuable time spent hunting for needles in haystacks instead of building, securing, or improving your software.\n\nGitLab's code search functionality has historically been backed by Elasticsearch or OpenSearch. While these are excellent for searching issues, merge requests, comments, and other data containing natural language, they weren't specifically designed for code. After [evaluating numerous options](https://gitlab.com/groups/gitlab-org/-/epics/7404), we developed a better solution.\n\n## Introducing Exact Code Search: Three game-changing capabilities\n\nEnter GitLab's **[Exact Code Search](https://docs.gitlab.com/ee/user/search/exact_code_search.html)**, currently in beta testing and powered by [Zoekt](https://github.com/sourcegraph/zoekt) (pronounced \"zookt\", Dutch for \"search\"). Zoekt is an open-source code search engine originally created by Google and now maintained by Sourcegraph, specifically designed for fast, accurate code search at scale. We've enhanced it with GitLab-specific integrations, enterprise-scale improvements, and seamless permission system integration.\n\nThis feature revolutionizes how you find and understand code with three key capabilities:\n\n1. Exact Match mode: Zero false positives\n\nWhen toggled to **Exact Match mode**, the search engine returns only results that match your query exactly as entered, eliminating false positives. This precision is invaluable when:\n\n* Searching for specific error messages\n\n* Looking for particular function signatures\n\n* Finding instances of specific variable names\n2. Regular Expression mode: Powerful pattern matching\n\nFor complex search needs, Regular Expression mode allows you to craft sophisticated search patterns:\n\n* Find functions following specific naming patterns\n\n* Locate variables matching certain constraints\n\n* Identify potential security vulnerabilities using pattern matching\n\n3. Multiple-line matches: See code in context\n\n\n![Exact Code Search](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750704179/ttjuilkt3v7gtyywnchx.png)\n\nInstead of seeing just a single line with your matching term, you get the surrounding context that's crucial for understanding the code. This eliminates the need to click through to files for basic comprehension, significantly accelerating your workflow.\n\n## From features to workflows: Real-world use cases and impact\n\nLet's see how these capabilities translate to real productivity gains in everyday development scenarios:\n\n### Debugging: From error message to root cause in seconds\n\nBefore Exact Code Search:\n\nCopy an error message, search, wade through dozens of partial matches in comments and documentation, click through multiple files, and eventually find the actual code.\n\nWith Exact Code Search:\n\n1. Copy the exact error message\n\n2. Paste it into Exact Code Search with Exact Match mode\n\n3. Instantly find the precise location where the error is thrown, with surrounding context\n\n**Impact:** Reduce debugging time from minutes to seconds, eliminating the frustration of false positives.\n\n### Code exploration: Master unfamiliar codebases quickly\n\nBefore Exact Code Search:\n\nBrowse through directories, make educated guesses about file locations, open dozens of files, and slowly build a mental map of the codebase.\n\nWith Exact Code Search:\n\n* Search for key methods or classes with Exact Match mode\n\n* Review multiple line matches to understand implementation details\n\n* Use Regular Expression mode to find similar patterns across the codebase\n\n**Impact:** Build a mental map of code structure in minutes rather than hours, dramatically accelerating onboarding and cross-team collaboration.\n\n### Refactoring with confidence\n\nBefore Exact Code Search:\n\nAttempt to find all instances of a method, miss some occurrences, and introduce bugs through incomplete refactoring.\n\nWith Exact Code Search:\n\n* Use Exact Match mode to find all occurrences of methods or variables\n\n* Review context to understand usage patterns\n\n* Plan your refactoring with complete information about impact\n\n**Impact:** Eliminate the \"missed instance\" bugs that often plague refactoring efforts, improving code quality and reducing rework.\n\n### Security auditing: Finding vulnerable patterns\n\nSecurity teams can:\n\n* Create regex patterns matching known vulnerable code\n\n* Search across all repositories in a namespace\n\n* Quickly identify potential security issues with context that helps assess risk\n\n**Impact:** Transform security audits from manual, error-prone processes to systematic, comprehensive reviews.\n\n### Cross-repository insights\n\nSearch across your entire namespace or instance to:\n\n* Identify similar implementations across different projects\n\n* Discover opportunities for shared libraries or standardization\n\n**Impact:** Break down silos between projects and identify opportunities for code reuse and standardization.\n\n## The technical foundation: How Zoekt delivers speed and precision\n\nBefore diving into our scale achievements, let's explore what makes Zoekt fundamentally different from traditional search engines — and why it can find exact matches so incredibly fast.\n\n### Positional trigrams: The secret to lightning-fast exact matches\n\nZoekt's speed comes from its use of **positional trigrams** — a technique that indexes every sequence of three characters along with their exact positions in files. This approach solves one of the biggest pain points developers have had with Elasticsearch-based code search: false positives.\n\nHere's how it works:\n\n**Traditional full-text search engines** like Elasticsearch tokenize code into words and lose positional information. When you search for `getUserId()`, they might return results containing **user**, **get**, and **Id** scattered throughout a file — leading to those frustrating false positives for GitLab users.\n\n**Zoekt's positional trigrams** maintain exact character sequences and their positions. When you search for `getUserId()`, Zoekt doesn't check all possible trigrams. Instead, it intelligently selects just **2 trigrams** with the lowest frequency (least common in the codebase) — for example, **get** and **d()** — and ensures they appear at the correct distance apart (distance 8 in this case). This optimization strategy dramatically reduces the number of candidate matches while ensuring only exact matches are returned.\n\nThe result? Search queries that previously returned hundreds of irrelevant results now return only the precise matches you're looking for. This was [one of our most requested features](https://gitlab.com/gitlab-org/gitlab/-/issues/325234) for good reason - developers were losing significant time sifting through false positives.\n\n### Regular expression performance at scale\n\nZoekt excels at exact matches and is optimized for regular expression searches. The engine uses sophisticated algorithms to convert regex patterns into efficient trigram queries when possible, maintaining speed even for complex patterns across terabytes of code.\n\n## Built for enterprise scale\n\nExact Code Search is powerful and built to handle massive scale with impressive performance. This is not just a new UI feature — it's powered by a completely reimagined backend architecture.\n\n### Handling terabytes of code with ease\n\nOn GitLab.com alone, our Exact Code Search infrastructure indexes and searches over **48 TB** of code data while maintaining lightning-fast response times. This scale represents millions of repositories across thousands of namespaces, all searchable within milliseconds. To put this in perspective: This scale represents more code than the entire Linux kernel, Android, and Chromium projects combined. Yet Exact Code Search can find a specific line across this massive codebase in milliseconds.\n\n### Self-registering node architecture\n\nOur innovative implementation features:\n\n* **Automatic node registration:** Zoekt nodes register themselves with GitLab\n\n* **Dynamic shard assignment:** The system automatically assigns namespaces to nodes\n\n* **Health monitoring:** Nodes that don't check in are automatically marked offline\n\nThis self-configuring architecture dramatically simplifies scaling. When more capacity is needed, administrators can simply add more nodes without complex reconfiguration.\n\n### Distributed system with intelligent load balancing\n\nBehind the scenes, Exact Code Search operates as a distributed system with these key components:\n\n* **Specialized search nodes:** Purpose-built servers that handle indexing and searching\n\n* **Smart sharding:** Code is distributed across nodes based on namespaces\n\n* **Automatic load balancing:** The system intelligently distributes work based on capacity\n\n* **High availability:** Multiple replicas ensure continuous operation even if nodes fail\n\n*Note: High availability is built into the architecture but not yet fully enabled. See [Issue 514736](https://gitlab.com/gitlab-org/gitlab/-/issues/514736) for updates.*\n\n### Seamless security integration\n\nExact Code Search automatically integrates with GitLab's permission system:\n\n* Search results are filtered based on the user's access rights\n\n* Only code from projects the user has access to is displayed\n\n* Security is built into the core architecture, not added as an afterthought\n\n### Optimized performance\n\n* **Efficient indexing:** Large repositories are indexed in tens of seconds\n\n* **Fast query execution:** Most searches return results with sub-second response times\n\n* **Streaming results:** The new gRPC-based federated search streams results as they're found\n\n* **Early termination:** Once enough results are collected, the system pauses searching\n\n## From library to distributed system: Engineering challenges we solved\n\nWhile Zoekt provided the core search technology, it was originally designed as a minimal library for managing `.zoekt` index files - not a distributed database or enterprise-scale service. Here are the key engineering challenges we overcame to make it work at GitLab's scale\"\n\n### Challenge 1: Building an orchestration layer\n\n**The problem:** Zoekt was designed to work with local index files, not distributed across multiple nodes serving many concurrent users.\n\n**Our solution:** We built a comprehensive orchestration layer that:\n\n* Creates and manages database models to track nodes, indices, repositories, and tasks\n\n* Implements a self-registering node architecture (inspired by GitLab Runner)\n\n* Handles automatic shard assignment and load balancing across nodes\n\n* Provides bidirectional API communication between GitLab Rails and Zoekt nodes\n\n### Challenge 2: Scaling storage and indexing\n\n**The problem:** How do you efficiently manage terabytes of index data across multiple nodes while ensuring fast updates?\n\n**Our solution:** We implemented:\n\n* Intelligent sharding: Namespaces are distributed across nodes based on capacity and load\n\n* Independent replication: Each node independently indexes from [Gitaly](https://gitlab.com/gitlab-org/gitaly) (our Git storage service), eliminating complex synchronization\n\n* Watermark management: Sophisticated storage allocation that prevents nodes from running out of space\n\n* Unified binary architecture: A single `gitlab-zoekt` binary that can operate in both indexer and webserver modes\n\n### Challenge 3: Permission Integration\n\n**The problem:** Zoekt had no concept of GitLab's complex permission system - users should only see results from projects they can access.\n\n**Our solution:** We built native permission filtering directly into the search flow:\n\n* Search requests include user permission context\n\n* Results are filtered to include only those the user can access in case permissions change before indexing completes\n\n### Challenge 4: Operational simplicity\n\n**The problem:** Managing a distributed search system shouldn't require a dedicated team.\n\n**Our solution:**\n\n* Auto-scaling: Adding capacity is as simple as deploying more nodes - they automatically register and start handling work\n\n* Self-healing: Nodes that don't check in are automatically marked offline and their work redistributed\n\n* Zero-configuration sharding: The system automatically determines optimal shard assignments\n\n## Gradual rollout: Minimizing risk at scale\n\nRolling out a completely new search backend to millions of users required careful planning. Here's how we minimized customer impact while ensuring reliability:\n\n### Phase 1: Controlled testing (gitlab-org group)\n\nWe started by enabling Exact Code Search only for the `gitlab-org` group - our own internal repositories. This allowed us to:\n\n* Test the system with real production workloads\n\n* Identify and fix performance bottlenecks\n\n* Streamline the deployment process\n\n* Learn from real users' workflows and feedback\n\n### Phase 2: Performance validation and optimization\n\nBefore expanding, we focused on ensuring the system could handle GitLab.com's scale:\n\n* Implemented comprehensive monitoring and alerting\n\n* Validated storage management with real production data growth\n\n### Phase 3: Incremental customer expansion\n\nWe gradually expanded to customers interested in testing Exact Code Search:\n\n* Gathered feedback on performance and user experience\n\n* Refined the search UI based on real user workflows\n\n* Optimized indexing performance (large repositories like `gitlab-org/gitlab` now index in ~10 seconds)\n\n* Refined the architecture based on operational learnings\n\n* Massively increased indexing throughput and improved state transition livecycle\n\n### Phase 4: Broad rollout\n\nToday, over 99% of Premium and Ultimate licensed groups on GitLab.com have access to Exact Code Search. Users can:\n\n* Toggle between regex and exact search modes\n\n* Experience the benefits without any configuration changes\n\n* Fall back to the previous search if needed (though few choose to)\n\nRolling this out gradually meant users didn't experience service disruptions, performance degradation, or feature gaps during the transition. We've already received positive feedback from users as they notice their results becoming more relevant and faster.\n\n> **For technical deep dive:** Interested in the detailed architecture and implementation? Check out our comprehensive [design document](https://handbook.gitlab.com/handbook/engineering/architecture/design-documents/code_search_with_zoekt/) for in-depth technical details about how we built this distributed search system.\n\n## Getting started with Exact Code Search\n\nGetting started with Exact Code Search is simple because it's already enabled by default for Premium and Ultimate groups on GitLab.com (over 99% of eligible groups currently have access).\n\n### Quickstart guide\n\n1. Navigate to the Advanced Search in your GitLab project or group\n\n2. Enter your search term in the code tab\n\n3. Toggle between Exact Match and Regular Expression modes\n\n4. Use filters to refine your search\n\n### Basic search syntax\n\nWhether using Exact Match or Regular Expression mode, you can refine your search with modifiers:\n\n| Query Example | What It Does                                             |\n| ------------- | -------------------------------------------------------- |\n| `file:js`     | Searches only in files containing \"js\" in their name     |\n| `foo -bar`    | Finds \"foo\" but excludes results with \"bar\"              |\n| `lang:ruby`   | Searches only in Ruby files                              |\n| `sym:process` | Finds \"process\" in symbols (methods, classes, variables) |\n\n> **Pro Tip:** For the most efficient searches, start specific and then broaden if needed. Using `file:` and `lang:` filters dramatically increases relevance.\n\n### Advanced search techniques\n\nStack multiple filters for precision:\n\n```text\nis_expected file:rb -file:spec\n```\n\nThis finds \"is_expected\" in Ruby files that don't have \"spec\" in their name.\n\nUse regular expressions for powerful patterns:\n\n```text\ntoken.*=.*[\\\"']\n```\n\n[Watch this search performed against the GitLab Zoekt repository.](https://gitlab.com/search?search=token.*%3D.*%5B%5C%22'%5D&nav_source=navbar&project_id=46649240&group_id=9970&search_code=true&repository_ref=main&regex=true)\n\nThe search helps find hardcoded passwords, which, if not found, can be a security issue.\n\nFor more detailed syntax information, check the [Exact Code Search documentation](https://docs.gitlab.com/user/search/exact_code_search/#syntax).\n\n## Availability and deployment\n\n### Current availability\n\nExact Code Search is currently in Beta for GitLab.com users with Premium and Ultimate licenses:\n\n* Available for over 99% of licensed groups\n\n* Search in the UI automatically uses Zoekt when available, Exact Code Search in Search API is behind a feature flag\n\n### Self-managed deployment options\n\nFor self-managed instances, we offer several deployment methods:\n\n* Kubernetes/Helm: Our most well-supported method, using our [`gitlab-zoekt` Helm chart](https://gitlab.com/gitlab-org/cloud-native/charts/gitlab-zoekt)\n\n* Other deployment options: We're working on streamlining deployment for Omnibus and other installation methods\n\nSystem requirements depend on your codebase size, but the architecture is designed to scale horizontally and/or vertically as your needs grow.\n\n## What's coming next\n\nWhile Exact Code Search is already powerful, we're continuously improving it:\n\n* **Scale optimizations** to support instances with hundreds of thousands of repositories\n\n* **Improved self-managed deployment** options, including streamlined Omnibus support\n\n* **Full high availability support** with automatic failover and load balancing\n\nStay tuned for updates as we move from Beta to General Availability.\n\n## Transform how you work with code\n\nGitLab's Exact Code Search represents a fundamental rethinking of code discovery. By delivering exact matches, powerful regex support, and contextual results, it solves the most frustrating aspects of code search:\n\n* No more wasting time with irrelevant results\n\n* No more missing important matches\n\n* No more clicking through files just to understand basic context\n\n* No more performance issues as codebases grow\n\nThe impact extends beyond individual productivity:\n\n* **Teams collaborate better** with easy code referencing\n\n* **Knowledge sharing accelerates** when patterns are discoverable\n\n* **Onboarding becomes faster** with quick codebase comprehension\n\n* **Security improves** with effective pattern auditing\n\n* **Technical debt reduction** becomes more feasible\n\nExact Code Search isn't just a feature, it's a better way to understand and work with code. Stop searching and start finding.\n\n**We'd love to hear from you!** Share your experiences, questions, or feedback about Exact Code Search in our [feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/420920). Your input helps us prioritize improvements and new features.\n\n> Ready to experience smarter code search? Learn more in our [documentation](https://docs.gitlab.com/ee/user/search/exact_code_search.html) or try it now by performing a search in your Premium or Ultimate licensed namespaces or projects. Not a GitLab user yet? 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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.",[716],"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/).",[9,721,722],"AI/ML","news",{"featured":11,"template":12,"slug":724},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":726,"config":729},{"title":727,"heroImage":717,"description":728,"date":718,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":11,"template":12,"externalUrl":730},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":732,"config":740},{"title":733,"description":734,"authors":735,"heroImage":717,"date":718,"body":737,"category":9,"tags":738},"GitLab 18.11: CI Expert and Data Analyst AI agents target development gaps","Set up CI and query your software development lifecycle data with two new GitLab Duo Agent Platform foundational agents available in GitLab 18.11.",[736],"Corinne Dent","AI-generated code moves faster than the systems around it can keep up with. More code means more merge requests queued, more pipelines to configure, more questions about delivery that nobody has time to answer — and most of the tooling teams rely on wasn't built for this pace.\n\nIn GitLab 18.11, two new foundational agents for Duo Agent Platform address specific gaps in the development lifecycle that AI has largely left untouched:\n* CI Expert Agent (now in beta) focuses on the gap between writing code and getting it into a running pipeline\n* Data Analyst Agent (now generally available) focuses on the gap between shipping code and being able to answer basic questions about how that delivery is actually going.\n\n\nThese are problem areas that couldn't be solved by a general-purpose assistant. A tool running outside GitLab can generate a YAML file or answer a question, but it has no awareness of how your pipelines have historically performed, where failures cluster, or what your actual MR cycle times look like. That context lives in GitLab. These agents do too.\n## Fast CI setup with CI Expert Agent\n\nAI has made it easier than ever to write code. Getting that code into a running pipeline is still something most teams do days, or weeks, later — if at all. The blank-page problem isn't in the editor anymore. The blank page is now in `.gitlab-ci.yml`.\n\nDevelopers who have never configured CI don't know what language detection looks like in YAML, what their test commands should be, or how to validate the result before pushing. Teams either copy a config from a previous project that may not fit, stitch together examples from documentation, or wait for the one person who's done it before. If that person isn't available, CI becomes the thing you'll \"get to later.\" Later becomes never.\n\nWhen CI never happens, the impact shows up everywhere else. Changes ship without a reliable safety net, regressions surface in production instead of in pipelines, and work piles up in bigger, riskier batches because no one wants to be the person who “breaks the build.” Over time, teams normalize working in the dark, often relying on undocumented institutional knowledge and ad-hoc testing, instead of having a fast, predictable feedback loop baked into every change.\n\nCI Expert Agent, now available in beta, removes that friction. It inspects your repository, identifies your language and framework, and proposes a working build and test pipeline tailored to what's actually there — then explains every decision in plain language. The target: a running pipeline in minutes, with no YAML written by hand.\n\nWhat CI Expert Agent does:\n\n* Repo-aware pipeline generation detects language, framework, and test setup \n* Generates valid, runnable build and test configurations   \n* Guided first-pipeline flow with plain-language explanation of each step in Agentic Chat  \n* Native GitLab CI semantics with no config translation required\n\nBecause it runs inside GitLab and sees real pipeline behavior over time, each improvement can build on how teams actually work, not just on static examples.\n\u003Ciframe src=\"https://player.vimeo.com/video/1183458036?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"CI/CD Expert Agent\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nCI Expert Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium, Ultimate Editions with Duo Agent Platform enabled.\n\n## Query GitLab data in plain language with Data Analyst Agent\n\nAI has sped up how teams ship. Answering basic questions about how that work is going has gotten harder, not easier.\n\nHow long are MRs sitting in review? Which pipelines are slowing teams down? Are deployment targets actually being hit? These questions used to be answerable by glancing at a dashboard. Now, with more code, more teams, and more complexity, the data exists — it's in GitLab — but accessing it still means waiting on an analytics team, filing a dashboard request, or learning GLQL.\n\nData Analyst Agent targets that gap. Ask a natural-language question and get an instant visualization in Agentic Chat. No query language, no dashboard request, no waiting for the answers to be assembled by someone else.\n\nFor example, the agent can answer questions about the following topics for these roles:\n\n* Engineering managers: MR cycle time, throughput by project, where reviews get stuck  \n* Developers: Contribution patterns, flaky tests blocking their MRs, pipeline speed trends  \n* DevOps and platform engineers: Pipeline success/failure rates, runner utilization, deployment frequency  \n* Engineering leadership: Cross-portfolio deployment frequency, project health metrics, lead time comparisons\n\nNow generally available in 18.11, the agent covers MRs, issues, projects, pipelines, and jobs — full software development lifecycle coverage, expanded from the beta scope. Because Data Analyst Agent queries what's already in GitLab, the context is always current, and there's no pipeline to maintain or third-party tool to keep synchronized. Generated GitLab Query Language queries can be copied and used anywhere GitLab Flavored Markdown is supported, with direct export to work items and dashboards on the roadmap.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1183094817?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Data Analyst agent demo\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nData Analyst Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium and Ultimate Edition with Duo Agent Platform enabled.\n\n## One platform, connected context\n\nBoth agents run inside GitLab, with access to the code, pipelines, issues, and merge requests already there. That's what separates platform-native AI from a disconnected assistant: the context is always current, and it only gets more useful over time. CI Expert Agent and Data Analyst Agent represent two concrete steps toward a platform where AI doesn't just help you write code faster; it helps you understand, ship, and maintain what gets built.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo/) to experience these foundational AI agents.",[721,739,9],"features",{"featured":27,"template":12,"slug":741},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":743},[744,758,769,781],{"id":745,"categories":746,"header":748,"text":749,"button":750,"image":755},"ai-modernization",[747],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":751,"config":752},"Get your AI maturity score",{"href":753,"dataGaName":754,"dataGaLocation":238},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":756},{"src":757},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":759,"categories":760,"header":761,"text":749,"button":762,"image":766},"devops-modernization",[9,564],"Are you just managing tools or shipping innovation?",{"text":763,"config":764},"Get your DevOps maturity score",{"href":765,"dataGaName":754,"dataGaLocation":238},"/assessments/devops-modernization-assessment/",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":770,"categories":771,"header":773,"text":749,"button":774,"image":778},"security-modernization",[772],"security","Are you trading speed for security?",{"text":775,"config":776},"Get your security maturity score",{"href":777,"dataGaName":754,"dataGaLocation":238},"/assessments/security-modernization-assessment/",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":782,"paths":783,"header":786,"text":787,"button":788,"image":793},"github-azure-migration",[784,785],"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":789,"config":790},"See how GitLab compares to GitHub",{"href":791,"dataGaName":792,"dataGaLocation":238},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":794},{"src":768},{"header":796,"blurb":797,"button":798,"secondaryButton":803},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":799,"config":800},"Get your free trial",{"href":801,"dataGaName":45,"dataGaLocation":802},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":500,"config":804},{"href":49,"dataGaName":50,"dataGaLocation":802},1776444471217]