[{"data":1,"prerenderedAt":814},["ShallowReactive",2],{"/en-us/blog/why-we-are-not-leaving-the-cloud":3,"navigation-en-us":33,"banner-en-us":443,"footer-en-us":453,"blog-post-authors-en-us-Sean Packham":695,"blog-related-posts-en-us-why-we-are-not-leaving-the-cloud":709,"blog-promotions-en-us":751,"next-steps-en-us":804},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":22,"isFeatured":12,"meta":23,"navigation":24,"path":25,"publishedDate":20,"seo":26,"stem":30,"tagSlugs":31,"__hash__":32},"blogPosts/en-us/blog/why-we-are-not-leaving-the-cloud.yml","Why We Are Not Leaving The Cloud",[7],"sean-packham",null,"engineering",{"slug":11,"featured":12,"template":13},"why-we-are-not-leaving-the-cloud",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9},"Why we are not leaving the cloud","What we learned from our community vetting our proposal to leave the cloud.",[18],"Sean Packham","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663397/Blog/Hero%20Images/logoforblogpost.jpg","2017-03-02","\n\n\u003Cscript>\n  var disqus_identifier = '/blog/why-we-are-not-leaving-the-cloud/';\n\u003C/script>\n\nTowards the end of 2016 we said we were [leaving the cloud for bare metal](/blog/why-choose-bare-metal/) and shared our [hardware proposal](https://news.ycombinator.com/item?id=13153031). In December 2016, after receiving hundreds of comments and emails filled with advice and warnings, [Sid and the team decided](https://gitlab.com/gitlab-com/infrastructure/issues/727#note_20044060) to keep GitLab.com in the cloud. The rest of the post summarizes some of the great community support and feedback we received and ends with how we are committed to making GitLab.com fast and stable in the cloud. Our decision was based on  more than what is below but we wanted to give you a good summary of all the interesting things that were shared publicly.\n\n\u003C!-- more -->\n\n## Let's begin on the topic of cost\n\n> When I was at Koding we made a similar move from AWS to bare metal. The costs were amazing. Something like $20k a month for what in AWS would cost $200k. I have been saying for a very long time that once you hit a certain scale AWS no longer makes sense. *[Geraint - GitLab blog: Going bare metal](/blog/why-choose-bare-metal/#comment-2999631471)*\n\n> We had 140 servers hosted in New York City for 10 years or so, and hosting only was going up and up, and contracts didn't give us flexibility to add cabinets when we needed. We basically had to cancel the previous contract, make a new one, pay for the upgrade, pay for the cabinet setup, etc... At some point, when we had financial trouble paying $14K/month for hosting, we decided to move all our servers from NYC to Tallinn, Estonia, where we built our own a small scale datacenter. As a result, we were able to cut hosting fees x10. *[Dmitri - GitLab blog: Proposed server purchase](/blog/proposed-server-purchase-for-gitlab-com/#comment-3049071074)*\n\n\u003Cdiv style=\"font-size: 38px; line-height: 1.2; margin: 45px 0 55px; font-style: italic;\">\nIt's not just the cost of owning and renewing the hardware, it's everything else that comes with it – daenney\n\u003C/div>\n\n> It's not just the cost of owning and renewing the hardware, it's everything else that comes with it. Designing your network, performance tuning and debugging everything. Suddenly you have a capacity issue, now what b/c you're not likely to have a spare 100 servers racked and ready to go, or be able to spin them up in 2m? Autoscaling? *[daenney - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153296)*\n\n> Application Architecture is far more important than Cloud vs. Bare Metal. It is just easier and more cost effective to throw more bare metal hardware at the problem than it is cloud instances. For some this does make bare metal the better option. *[mohctp - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13162964)*\n\n> Moving to your own hardware will almost certainly improve performance, reduce incidental downtime, and cut costs substantially. Including hiring more engineers, you might expect total costs to be ~40-50% of what you would have spent on cloud-based services over the first 24 months. If your hardware lifecycle is 36-48 months, you will see large savings beyond 24 months. *[bobf - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153413)*\n\n> I think they are going to underestimate the cost to GitLab in the long run. When they need to pay for someone to be a 30 minute drive from their DC 24/7/365 after the first outage, when they realize how much spare hardware they are going to want around, etc. *[manacit - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13154057)*\n\n## What About Performance?\n\n> A cloud service providers' biggest responsibilities to its customers are security, durability, availability and performance -- in that order. You guys are vastly underestimating the complexity involved in getting first 3 right. *[mritun - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13155809)*\n\n> Very few teams at Google run on dedicated machines. Those that do are enormous, both in the scale of their infrastructure and in their team sizes. I'm not saying always go with a cloud provider, I'm reiterating that you'd better be certain you need to. *[boulos - Hacker News: Going bare metal](https://news.ycombinator.com/item?id=12941210)*\n\n\u003Cdiv style=\"font-size: 38px; line-height: 1.2; margin: 45px 0 55px; font-style: italic;\">\nA company rolling their own system doesn't have to share, and they can optimise specifically for their own requirements – taneq\n\u003C/div>\n\n> As a cloud provider, though, you're trying to provide shared resources to a group of clients. A company rolling their own system doesn't have to share, and they can optimise specifically for their own requirements. *[taneq - Hacker News: Going bare metal](https://news.ycombinator.com/item?id=12940925)*\n\n> My thinking is that elasticity and recovery from hardware failure, and migration and multi-data center high availability will become concerns. Moving from the cloud to bare metal gives you performance and simplicity, but doesn't give you as many ways of recovering from network interruptions, and hardware failures. *[wpostma - the GitLab blog: Going bare metal](/blog/why-choose-bare-metal/#comment-3001348957)*\n\n> It sounds like they didn't design for the cloud and are now experiencing the consequences. The cloud has different tradeoffs and performance characteristics from a datacenter. If you plan for that, it's great. Your software will be robust as a result. If you assume the characteristics of a data center, you're likely to run into problems. *[wandernotlost - Hacker News: Going bare metal](https://news.ycombinator.com/item?id=12940082)*\n\n\u003Cdiv style=\"font-size: 38px; line-height: 1.2; margin: 45px 0 55px; font-style: italic;\">\nIt makes sense to keep GitLab.com as an eat-your-own-dog-food-at-scale environment – jtwaleson\n\u003C/div>\n\n> It makes sense to keep GitLab.com as an eat-your-own-dog-food-at-scale environment.  If one of their customers that run on-premise has performance issues they can't just say: GitLab.com uses a totally different architecture so you're on your own. They need GitLab.com to be as close as possible to the standard product. *[twaleson on Hacker News: Going bare metal](https://news.ycombinator.com/item?id=12940462)*\n\n> They are moving from cloud to bare metal because of performance while using a bunch of software that are notoriously slow and wasteful. I would optimise the hell out of my stack before commit to a change like this. Building your own racks does not deliver business value and it is extremely error prone process (been there, done that). *[StreamBright - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153866)*\n\n## Advice on our storage proposals\n\n> __Don't f*ck with storage.__ 32 file servers for 96TB? Same question as with networking re:ceph. What are your failure domains? How much does it cost to maintain the FTEs who can run this thing? *[Spooky23 - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153860)* - *Spooky23 did warn us \"I'm a cranky old person now\".*\n\n> I think there might be a pretty big IOPS drop when you switch over to this hardware. You're looking at having approximately 60 7200 RPM drives in this CephFS cluster. Doing the math, if you assume each of those drives can do 100 read and 100 write IOPS, and that you are doing 3x replication on write (plus journal writes), you're not going to get anywhere near the numbers that you want. *[Nicholas - the GitLab blog: Proposed server purchase](/blog/proposed-server-purchase-for-gitlab-com/#comment-3047537669)*\n\n>I would think that GitLab's workload is mostly random, which would pose a problem for larger drives. The SSDs are a great idea, but I've only seen 8TB drives used when there are 2 to 3 tiers; with 8TB drives being all the way on the bottom. I'm not sure how effective having a single SSD as a cache drive for 24TBs of 8TB disks will be. *[lykron - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153333)*\n\n## and our choice of 8TB drives\n\n> If you are looking for performance, do not get the 8TB drives. In my experience, drives above 5TB do not have good response times. I don't have hard numbers, but I built a 10 disk RAID6 array with 5TB disks and 2TB disks and the 2TB disks were a lot more responsive. *[lykron - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153196)*\n\n> Just a few quick notes. I've experience running ~300TB of usable Ceph storage. Stay away from the 8TB drives. Why are you using fat twins? Honestly, what does that buy you? You need more spindles, and fewer cores and memory. With your current configuration, what are you getting per rack unit? *[halbritt - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153786)*\n\n##  Feedback on our network proposals\n\n>__Don't f*ck with networking.__ Do you have experience operating same or similar workloads on your super micro SDN? Will the CEO of your super micro VAR pickup his phone at 2AM when you call? *[Spooky23 - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153860)*\n\n> I would not use 10GBase-T since it's designed for desktop use. I suggest ideally 25G SFP28 (AOC-MH25G-m2S2TM) but 10G SFP+ (AOC-MTG-i4S) is OK. The speed and type of the switch needs to match the NIC (you linked to an SFP+ switch that isn't compatible with your proposed 10GBase-T NICs). *[wmf - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153678)*\n\n> I didn't see it mentioned but what are your plans for the network strategy. Are you planning to run dual-stack IPv4/IPv6 ? IPv4 only? Internal IPv6 only with NAT64 to the public stuff? Hopefully IPv6 shows up somewhere in the stack. It's sad to see big players not using it yet. *[tomschlick - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153922)*\n\n> Don't fall into the trap of extending VLANs everywhere. You should definitely be routing (not switching) between different routers.\n>\n> \"Should we have a separate network for Ceph traffic?\" Yes, if you want your Ceph cluster to remain usable during rebuilds. Ceph will peg the internal network during any sort of rebuild event. *[devicenull - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153339)*\n\n## What did the community have to say about Ceph?\n\n> I lead a technical operations team that moved our infrastructure from public cloud (~400 instances) to private cloud (~55 physical servers) and finally, to Kubernetes (6 physical servers). We actually run a mix of Kubernetes and OpenStack, putting apps and services in Kubernetes and all data storage in OpenStack. I've done extensive testing with Ceph and while it adds flexibility, you're not going to be able to touch the I/O performance of bare metal local disks for database use. For storage, I like to keep it simple. I rely on the Linux OS running on standard tried-and-true filesystems (ext4 and ZFS) and build redundancy at the software layer. *[Chris - GitLab blog: Proposed server purchase](/blog/proposed-server-purchase-for-gitlab-com/#comment-3047381500)*\n\n> We had disastrous experiences with Ceph and Gluster on bare metal. I think this says more about the immaturity (and difficulty) of distributed file systems than the cloud per se. *__[codinghorror - Hacker News: Going bare metal](https://news.ycombinator.com/item?id=12940042)__*\n\n> You need to make sure that there is not an architecture that you can build that absolves you of having to run a CephFS cluster. CephFS is cool, but it is a single point of failure right now, and comes with a ton of caveats. Performance and stability will be much improved if you remove the layer of abstraction it creates and write your app to handle some sort of distributed storage system. *[Nicholas - GitLab blog: Proposed server purchase](/blog/proposed-server-purchase-for-gitlab-com/#comment-3047478761)*\n\n\u003Cdiv style=\"font-size: 38px; line-height: 1.2; margin: 45px 0 55px; font-style: italic;\">\nBe very very careful about Ceph hype – late2part\n\u003C/div>\n\n> Be very very careful about Ceph hype. Ceph is good at redundancy and throughput, but not at IOPS, and Rados IOPS are poor. We couldn't get over 60k random RW IOPS across a 120 OSD cluster with 120 SSDs. *[late2part - GitLab blog: Proposed server purchase](https://news.ycombinator.com/item?id=13154620)*\n\n> If you're using CephFS and everyone else wants to be using other Cloud storage solutions, that would actually put you at a disconnect with your users and leave room for a competitor with the tools and experience to scale out on Cloud storage to come in offering support. *[Rapzid - Hacker News: Going bare metal](https://news.ycombinator.com/item?id=12946174)*\n\n## How would moving to metal affect the GitLab team?\n\n> Your core competency is code, not infrastructure, so striking out to build all of these new capabilities in your team and organization will come at a cost that you can not predict. Looking at total cost of ownership of cloud vs steel isn't as simple as comparing the hosting costs, hardware and facilities. *[ninjakeyboard - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153779)*\n\n\u003Cdiv style=\"font-size: 38px; line-height: 1.2; margin: 45px 0 55px; font-style: italic;\">\nYour core competency is code, not infrastructure – ninjakeyboard\n\u003C/div>\n\n> Another problem I would say to move to metal is that you lose support. Cloud vendors have entire teams, network, systems, datacenters etc. at your disposal, this is included in the price you are paying. Are you sure you are ready to debug networking issues, systems problems at the level as the cloud vendors? It is a tough job. *[l1x - GitLab blog: Proposed server purchase](/blog/proposed-server-purchase-for-gitlab-com/#comment-3047353138)*\n\n> I think you're under estimating the number of people required to run your own infrastructure. You need people who can configure networking gear, people swapping out failed NICs/Drives at the datacenter, someone managing vendor relationships, and people doing capacity planning. *[thebyrd-on Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13153644)*\n\n## Let’s just abandon x86 altogether\n\n\u003Cdiv style=\"font-size: 38px; line-height: 1.2; margin: 45px 0 55px; font-style: italic;\">\nWhy bind yourself to Intel servers? – MBH\n\u003C/div>\n\n> Why bind yourself to Intel servers? The max CPU-to-Memory bandwidth is 68 GB/s. That's horrible for crunching data fast. IBM's POWER8 systems have servers with 230 GB/s CPU-to-Memory bandwidth, and others with 320 GB/s...\n>\n> ...POWER8 CPUs have a different architecture than Intel: PPC64, so you may need to recompile some things, or have some Intel systems for workloads that can only run on x86_64. *[MBH - GitLab blog: Proposed server purchase](/blog/proposed-server-purchase-for-gitlab-com/#comment-3053432409)*\n\n## We all have an opinion\n\n> I've only ever built desktop machines, and this top comment drew a surprising parallel to most help me with my desktop build type posts. Granted, I'm sure as you dig deeper, the reasoning may be much different, but myself being ignorant about a proper server build, it was somehow reassuring to see power and cooling at the top! *[davidbrent - Hacker News: Proposed server purchase](https://news.ycombinator.com/item?id=13154202)*\n\n## We are taking a step back and using a boring solution\n\nWe want to scale intelligently and build great software; we don’t want to be an infrastructure company. We are embracing and are excited about solving the challenge of scaling GitLab.com on the cloud, because solving it for us also solved it for the largest enterprises in the world using GitLab on premise.\n\nMost of the scaling headaches have occurred because Git is read-heavy: looking at our Git Read/Write performance chart below, you can see that for about every 300 reads we get 10 writes. We tried to solve this by running CephFS in the cloud but it goes against our value of using the simplest, most  [boring solution](https://handbook.gitlab.com/handbook/#values) for a problem.\n\n![An average of 300 Reads to 10 writes](https://about.gitlab.com/images/blogimages/why-we-are-not-leaving-the-cloud-chart.png)\n\n## How are we going to get back to basics?\n\n1. We spread all our storage into [multiple NFS shards](https://gitlab.com/gitlab-com/infrastructure/issues/711) and [dropped CephFS](https://gitlab.com/gitlab-com/infrastructure/issues/817) from our stack.\n2. We created [Gitaly](https://gitlab.com/gitlab-org/gitaly) so that we can stop relying on NFS for horizontal scaling and speed up Git access through caching.\n\n[Gitaly](https://gitlab.com/gitlab-org/gitaly) will serve as the single interface for all our Git access throughout our stack. With Gitaly the gitrpc travels over the network and the disk is accessed locally. Instead of all the disk access going over the network. It will also be used to improve our monitoring of Git resource usage to make better decisions; currently we are only sampling processes.\n\nWe would love if the community would challenge our use of Gitaly with the same passion they challenged us before. What do you think of the software architecture? Can a caching layer like this scale? What alarm bells are set off? We can’t wait to hear your feedback!\n\nWe would like to thank our community, customers, team and board for all their great support – you all make GitLab an incredible product.\n","yml",{},true,"/en-us/blog/why-we-are-not-leaving-the-cloud",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":27,"ogSiteName":28,"ogType":29,"canonicalUrls":27},"https://about.gitlab.com/blog/why-we-are-not-leaving-the-cloud","https://about.gitlab.com","article","en-us/blog/why-we-are-not-leaving-the-cloud",[],"h0TKTcUVF4sTVXsLMyP8cu-sL0B90AjCEH7b0f00zPY",{"data":34},{"logo":35,"freeTrial":40,"sales":45,"login":50,"items":55,"search":363,"minimal":394,"duo":413,"switchNav":422,"pricingDeployment":433},{"config":36},{"href":37,"dataGaName":38,"dataGaLocation":39},"/","gitlab logo","header",{"text":41,"config":42},"Get free 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CI/CD tools can run a build and ship a deployment. Where they diverge is what happens when your delivery needs get real: a monorepo with a dozen services, microservices spread across multiple repositories, deployments to dozens of environments, or a platform team trying to enforce standards without becoming a bottleneck.\n  \nGitLab's pipeline execution model was designed for that complexity. Parent-child pipelines, DAG execution, dynamic pipeline generation, multi-project triggers, merge request pipelines with merged results, and CI/CD Components each solve a distinct class of problems. Because they compose, understanding the full model unlocks something more than a faster pipeline. In this article, you'll learn about the five patterns where that model stands out, each mapped to a real engineering scenario with the configuration to match.\n  \nThe configs below are illustrative. The scripts use echo commands to keep the signal-to-noise ratio low. Swap them out for your actual build, test, and deploy steps and they are ready to use.\n\n\n## 1. Monorepos: Parent-child pipelines + DAG execution\n\n\nThe problem: Your monorepo has a frontend, a backend, and a docs site. Every commit triggers a full rebuild of everything, even when only a README changed.\n\n\nGitLab solves this with two complementary features: [parent-child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#parent-child-pipelines) (which let a top-level pipeline spawn isolated sub-pipelines) and [DAG execution via `needs`](https://docs.gitlab.com/ci/yaml/#needs) (which breaks rigid stage-by-stage ordering and lets jobs start the moment their dependencies finish).\n\n\nA parent pipeline detects what changed and triggers only the relevant child pipelines:\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - trigger\n\ntrigger-services:\n  stage: trigger\n  trigger:\n    include:\n      - local: '.gitlab/ci/api-service.yml'\n      - local: '.gitlab/ci/web-service.yml'\n      - local: '.gitlab/ci/worker-service.yml'\n    strategy: depend\n```\n\n\nEach child pipeline is a fully independent pipeline with its own stages, jobs, and artifacts. The parent waits for all of them via [strategy: depend](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#wait-for-downstream-pipeline-to-complete) so you get a single green/red signal at the top level, with full drill-down into each service's pipeline. This organizational separation is the bigger win for large teams: each service owns its pipeline config, changes in one cannot break another, and the complexity stays manageable as the repo grows.\n\n\nOne thing worth knowing: when you pass [multiple files to a single `trigger: include:`](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#combine-multiple-child-pipeline-configuration-files), GitLab merges them into a single child pipeline configuration. This means jobs defined across those files share the same pipeline context and can reference each other with `needs:`, which is what makes the DAG optimization possible. If you split them into separate trigger jobs instead, each would be its own isolated pipeline and cross-file `needs:` references would not work.\n\n\nCombine this with `needs:` inside each child pipeline and you get DAG execution. Your integration tests can start the moment the build finishes, without waiting for other jobs in the same stage.\n\n```yaml\n# .gitlab/ci/api-service.yml\nstages:\n  - build\n  - test\n\nbuild-api:\n  stage: build\n  script:\n    - echo \"Building API service\"\n\ntest-api:\n  stage: test\n  needs: [build-api]\n  script:\n    - echo \"Running API tests\"\n```\n\n\nWhy it matters: Teams with large monorepos typically report significant reductions in pipeline runtime after switching to DAG execution, since jobs no longer wait on unrelated work in the same stage. Parent-child pipelines add the organizational layer that keeps the configuration maintainable as the repo and team grow.\n\n![Local downstream pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738759/Blog/Imported/hackathon-fake-blog-post-s/image3_vwj3rz.png \"Local downstream pipelines\")\n\n## 2. Microservices: Cross-repo, multi-project pipelines\n\n\nThe problem: Your frontend lives in one repo, your backend in another. When the frontend team ships a change, they have no visibility into whether it broke the backend integration and vice versa.\n\n\nGitLab's [multi-project pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#multi-project-pipelines) let one project trigger a pipeline in a completely separate project and wait for the result. The triggering project gets a linked downstream pipeline right in its own pipeline view.\n\n\nThe frontend pipeline builds an API contract artifact and publishes it, then triggers the backend pipeline. The backend fetches that artifact directly using the [Jobs API](https://docs.gitlab.com/ee/api/jobs.html#download-a-single-artifact-file-from-specific-tag-or-branch) and validates it before allowing anything to proceed. If a breaking change is detected, the backend pipeline fails and the frontend pipeline fails with it.\n\n```yaml\n# frontend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n  - trigger-backend\n\nbuild-frontend:\n  stage: build\n  script:\n    - echo \"Building frontend and generating API contract...\"\n    - mkdir -p dist\n    - |\n      echo '{\n        \"api_version\": \"v2\",\n        \"breaking_changes\": false\n      }' > dist/api-contract.json\n    - cat dist/api-contract.json\n  artifacts:\n    paths:\n      - dist/api-contract.json\n    expire_in: 1 hour\n\ntest-frontend:\n  stage: test\n  script:\n    - echo \"All frontend tests passed!\"\n\ntrigger-backend-pipeline:\n  stage: trigger-backend\n  trigger:\n    project: my-org/backend-service\n    branch: main\n    strategy: depend\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n```\n\n```yaml\n# backend repo: .gitlab-ci.yml\nstages:\n  - build\n  - test\n\nbuild-backend:\n  stage: build\n  script:\n    - echo \"All backend tests passed!\"\n\nintegration-test:\n  stage: test\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"pipeline\"\n  script:\n    - echo \"Fetching API contract from frontend...\"\n    - |\n      curl --silent --fail \\\n        --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n        --output api-contract.json \\\n        \"${CI_API_V4_URL}/projects/${FRONTEND_PROJECT_ID}/jobs/artifacts/main/raw/dist/api-contract.json?job=build-frontend\"\n    - cat api-contract.json\n    - |\n      if grep -q '\"breaking_changes\": true' api-contract.json; then\n        echo \"FAIL: Breaking API changes detected - backend integration blocked!\"\n        exit 1\n      fi\n      echo \"PASS: API contract is compatible!\"\n```\n\n\nA few things worth noting in this config. The `integration-test` job uses `$CI_PIPELINE_SOURCE == \"pipeline\"` to ensure it only runs when triggered by an upstream pipeline, not on a standalone push to the backend repo. The frontend project ID is referenced via `$FRONTEND_PROJECT_ID`, which should be set as a [CI/CD variable](https://docs.gitlab.com/ci/variables/) in the backend project settings to avoid hardcoding it.\n\n\nWhy it matters: Cross-service breakage that previously surfaced in production gets caught in the pipeline instead. The dependency between services stops being invisible and becomes something teams can see, track, and act on.\n\n\n![Cross-project pipelines](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738762/Blog/Imported/hackathon-fake-blog-post-s/image4_h6mfsb.png \"Cross-project pipelines\")\n\n\n## 3. Multi-tenant / matrix deployments: Dynamic child pipelines\n\n\nThe problem: You deploy the same application to 15 customer environments, or three cloud regions, or dev/staging/prod. Updating a deploy stage across all of them one by one is the kind of work that leads to configuration drift. Writing a separate pipeline for each environment is unmaintainable from day one.\n\n\nGitLab's [dynamic child pipelines](https://docs.gitlab.com/ci/pipelines/downstream_pipelines/#dynamic-child-pipelines) let you generate a pipeline at runtime. A job runs a script that produces a YAML file, and that YAML becomes the pipeline for the next stage. The pipeline structure itself becomes data.\n\n\n```yaml\n# .gitlab-ci.yml\nstages:\n  - generate\n  - trigger-environments\n\ngenerate-config:\n  stage: generate\n  script:\n    - |\n      # ENVIRONMENTS can be passed as a CI variable or read from a config file.\n      # Default to dev, staging, prod if not set.\n      ENVIRONMENTS=${ENVIRONMENTS:-\"dev staging prod\"}\n      for ENV in $ENVIRONMENTS; do\n        cat > ${ENV}-pipeline.yml \u003C\u003C EOF\n      stages:\n        - deploy\n        - verify\n      deploy-${ENV}:\n        stage: deploy\n        script:\n          - echo \"Deploying to ${ENV} environment\"\n      verify-${ENV}:\n        stage: verify\n        script:\n          - echo \"Running smoke tests on ${ENV}\"\n      EOF\n      done\n  artifacts:\n    paths:\n      - \"*.yml\"\n    exclude:\n      - \".gitlab-ci.yml\"\n\n.trigger-template:\n  stage: trigger-environments\n  trigger:\n    strategy: depend\n\ntrigger-dev:\n  extends: .trigger-template\n  trigger:\n    include:\n      - artifact: dev-pipeline.yml\n        job: generate-config\n\ntrigger-staging:\n  extends: .trigger-template\n  needs: [trigger-dev]\n  trigger:\n    include:\n      - artifact: staging-pipeline.yml\n        job: generate-config\n\ntrigger-prod:\n  extends: .trigger-template\n  needs: [trigger-staging]\n  trigger:\n    include:\n      - artifact: prod-pipeline.yml\n        job: generate-config\n  when: manual\n```\n\n\nThe generation script loops over an `ENVIRONMENTS` variable rather than hardcoding each environment separately. Pass in a different list via a CI variable or read it from a config file and the pipeline adapts without touching the YAML. The trigger jobs use [extends:](https://docs.gitlab.com/ci/yaml/#extends) to inherit shared configuration from `.trigger-template`, so `strategy: depend` is defined once rather than repeated on every trigger job. Add a new environment by updating the variable, not by duplicating pipeline config. Add [when: manual](https://docs.gitlab.com/ci/yaml/#when) to the production trigger and you get a promotion gate baked right into the pipeline graph.\n\n\nWhy it matters: SaaS companies and platform teams use this pattern to manage dozens of environments without duplicating pipeline logic. The pipeline structure itself stays lean as the deployment matrix grows.\n\n\n![Dynamic pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738765/Blog/Imported/hackathon-fake-blog-post-s/image7_wr0kx2.png \"Dynamic pipeline\")\n\n\n## 4. MR-first delivery: Merge request pipelines, merged results, and workflow routing\n\n\nThe problem: Your pipeline runs on every push to every branch. Expensive tests run on feature branches that will never merge. Meanwhile, you have no guarantee that what you tested is actually what will land on `main` after a merge.\n\n\nGitLab has three interlocking features that solve this together:\n\n\n*   [Merge request pipelines](https://docs.gitlab.com/ci/pipelines/merge_request_pipelines/) run only when a merge request exists, not on every branch push. This alone eliminates a significant amount of wasted compute.\n\n*   [Merged results pipelines](https://docs.gitlab.com/ci/pipelines/merged_results_pipelines/) go further. GitLab creates a temporary merge commit (your branch plus the current target branch) and runs the pipeline against that. You are testing what will actually exist after the merge, not just your branch in isolation.\n\n*   [Workflow rules](https://docs.gitlab.com/ci/yaml/workflow/) let you define exactly which pipeline type runs under which conditions and suppress everything else. The `$CI_OPEN_MERGE_REQUESTS` guard below prevents duplicate pipelines firing for both a branch and its open MR simultaneously.\n\n\nWith those three working together, here is what a tiered pipeline looks like:\n\n```yaml\n# .gitlab-ci.yml\nworkflow:\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH && $CI_OPEN_MERGE_REQUESTS\n      when: never\n    - if: $CI_COMMIT_BRANCH\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\nstages:\n  - fast-checks\n  - expensive-tests\n  - deploy\n\nlint-code:\n  stage: fast-checks\n  script:\n    - echo \"Running linter\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nunit-tests:\n  stage: fast-checks\n  script:\n    - echo \"Running unit tests\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"push\"\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nintegration-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running integration tests (15 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\ne2e-tests:\n  stage: expensive-tests\n  script:\n    - echo \"Running E2E tests (30 min)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"merge_request_event\"\n    - if: $CI_COMMIT_BRANCH == \"main\"\n\nnightly-comprehensive-scan:\n  stage: expensive-tests\n  script:\n    - echo \"Running full nightly suite (2 hours)\"\n  rules:\n    - if: $CI_PIPELINE_SOURCE == \"schedule\"\n\ndeploy-production:\n  stage: deploy\n  script:\n    - echo \"Deploying to production\"\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n      when: manual\n```\n\nWith this setup, the pipeline behaves differently depending on context. A push to a feature branch with no open MR runs lint and unit tests only. Once an MR is opened, the workflow rules switch from a branch pipeline to an MR pipeline, and the full integration and E2E suite runs against the merged result. Merging to `main` queues a manual production deployment. A nightly schedule runs the comprehensive scan once, not on every commit.\n\n\nWhy it matters: Teams routinely cut CI costs significantly with this pattern, not by running fewer tests, but by running the right tests at the right time. Merged results pipelines catch the class of bugs that only appear after a merge, before they ever reach `main`.\n\n\n![Conditional pipelines (within a branch with no MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738768/Blog/Imported/hackathon-fake-blog-post-s/image6_dnfcny.png \"Conditional pipelines (within a branch with no MR)\")\n\n\n\n![Conditional pipelines (within an MR)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738772/Blog/Imported/hackathon-fake-blog-post-s/image1_wyiafu.png \"Conditional pipelines (within an MR)\")\n\n\n\n![Conditional pipelines (on the main branch)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738774/Blog/Imported/hackathon-fake-blog-post-s/image5_r6lkfd.png \"Conditional pipelines (on the main branch)\")\n\n## 5. Governed pipelines: CI/CD Components\n\n\nThe problem: Your platform team has defined the right way to build, test, and deploy. But every team has their own `.gitlab-ci.yml` with subtle variations. Security scanning gets skipped. Deployment standards drift. Audits are painful.\n\n\nGitLab [CI/CD Components](https://docs.gitlab.com/ci/components/) let platform teams publish versioned, reusable pipeline building blocks. Application teams consume them with a single `include:` line and optional inputs — no copy-paste, no drift. Components are discoverable through the [CI/CD Catalog](https://docs.gitlab.com/ci/components/#cicd-catalog), which means teams can find and adopt approved building blocks without needing to go through the platform team directly.\n\n\nHere is a component definition from a shared library:\n\n```yaml\n# templates/deploy.yml\nspec:\n  inputs:\n    stage:\n      default: deploy\n    environment:\n      default: production\n---\ndeploy-job:\n  stage: $[[ inputs.stage ]]\n  script:\n    - echo \"Deploying $APP_NAME to $[[ inputs.environment ]]\"\n    - echo \"Deploy URL: $DEPLOY_URL\"\n  environment:\n    name: $[[ inputs.environment ]]\n```\nAnd here is how an application team consumes it:\n\n```yaml\n# Application repo: .gitlab-ci.yml\nvariables:\n  APP_NAME: \"my-awesome-app\"\n  DEPLOY_URL: \"https://api.example.com\"\n\ninclude:\n  - component: gitlab.com/my-org/component-library/build@v1.0.6\n  - component: gitlab.com/my-org/component-library/test@v1.0.6\n  - component: gitlab.com/my-org/component-library/deploy@v1.0.6\n    inputs:\n      environment: staging\n\nstages:\n  - build\n  - test\n  - deploy\n```\n\nThree lines of `include:` replace hundreds of lines of duplicated YAML. The platform team can push a security fix to `v1.0.7` and teams opt in on their own schedule — or the platform team can pin everyone to a minimum version. Either way, one change propagates everywhere instead of needing to be applied repo by repo.\n\n\nPair this with [resource groups](https://docs.gitlab.com/ci/resource_groups/) to prevent concurrent deployments to the same environment, and [protected environments](https://docs.gitlab.com/ci/environments/protected_environments/) to enforce approval gates - and you have a governed delivery platform where compliance is the default, not the exception.\n\n\nWhy it matters: This is the pattern that makes GitLab CI/CD scale across hundreds of teams. Platform engineering teams enforce compliance without becoming a bottleneck. Application teams get a fast path to a working pipeline without reinventing the wheel.\n\n\n![Component pipeline (imported jobs)](https://res.cloudinary.com/about-gitlab-com/image/upload/v1775738776/Blog/Imported/hackathon-fake-blog-post-s/image2_pizuxd.png \"Component pipeline (imported jobs)\")\n\n## Putting it all together\n\nNone of these features exist in isolation. The reason GitLab's pipeline model is worth understanding deeply is that these primitives compose:\n\n*   A monorepo uses parent-child pipelines, and each child uses DAG execution\n\n*   A microservices platform uses multi-project pipelines, and each project uses MR pipelines with merged results\n\n*   A governed platform uses CI/CD components to standardize the patterns above across every team\n\n\nMost teams discover one of these features when they hit a specific pain point. The ones who invest in understanding the full model end up with a delivery system that actually reflects how their engineering organization works, not a pipeline that fights it.\n\n## Other patterns worth exploring\n\n\nThe five patterns above cover the most common structural pain points, but GitLab's pipeline model goes further. A few others worth looking into as your needs grow:\n\n\n*   [Review apps with dynamic environments](https://docs.gitlab.com/ci/environments/) let you spin up a live preview for every feature branch and tear it down automatically when the MR closes. Useful for teams doing frontend work or API changes that need stakeholder sign-off before merging.\n\n*   [Caching and artifact strategies](https://docs.gitlab.com/ci/caching/) are often the fastest way to cut pipeline runtime after the structural work is done. Structuring `cache:` keys around dependency lockfiles and being deliberate about what gets passed between jobs with [artifacts:](https://docs.gitlab.com/ci/yaml/#artifacts) can make a significant difference without changing your pipeline shape at all.\n\n*   [Scheduled and API-triggered pipelines](https://docs.gitlab.com/ci/pipelines/schedules/) are worth knowing about because not everything should run on a code push. Nightly security scans, compliance reports, and release automation are better modeled as scheduled or [API-triggered](https://docs.gitlab.com/ci/triggers/) pipelines with `$CI_PIPELINE_SOURCE` routing the right jobs for each context.\n\n## How to get started\n\nModern software delivery is complex. Teams are managing monorepos with dozens of services, coordinating across multiple repositories, deploying to many environments at once, and trying to keep standards consistent as organizations grow. GitLab's pipeline model was built with all of that in mind.\n\nWhat makes it worth investing time in is how well the pieces fit together. Parent-child pipelines bring structure to large codebases. Multi-project pipelines make cross-team dependencies visible and testable. Dynamic pipelines turn environment management into something that scales gracefully. MR-first delivery with merged results ensures confidence at every step of the review process. And CI/CD Components give platform teams a way to share best practices across an entire organization without becoming a bottleneck.\n\nEach of these features is powerful on its own, and even more so when combined. GitLab gives you the building blocks to design a delivery system that fits how your team actually works, and grows with you as your needs evolve.\n\n> [Start a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/) to use pipeline logic today.\n\n## Read more\n\n*   [Variable and artifact sharing in GitLab parent-child pipelines](https://about.gitlab.com/blog/variable-and-artifact-sharing-in-gitlab-parent-child-pipelines/)\n*   [CI/CD inputs: Secure and preferred method to pass parameters to a pipeline](https://about.gitlab.com/blog/ci-cd-inputs-secure-and-preferred-method-to-pass-parameters-to-a-pipeline/)\n*   [Tutorial: How to set up your first GitLab CI/CD component](https://about.gitlab.com/blog/tutorial-how-to-set-up-your-first-gitlab-ci-cd-component/)\n*   [How to include file references in your CI/CD components](https://about.gitlab.com/blog/how-to-include-file-references-in-your-ci-cd-components/)\n*   [FAQ: GitLab CI/CD Catalog](https://about.gitlab.com/blog/faq-gitlab-ci-cd-catalog/)\n*   [Building a GitLab CI/CD pipeline for a monorepo the easy way](https://about.gitlab.com/blog/building-a-gitlab-ci-cd-pipeline-for-a-monorepo-the-easy-way/)\n*   [A CI/CD component builder's journey](https://about.gitlab.com/blog/a-ci-component-builders-journey/)\n*   [CI/CD Catalog goes GA: No more building pipelines from scratch](https://about.gitlab.com/blog/ci-cd-catalog-goes-ga-no-more-building-pipelines-from-scratch/)","5 ways GitLab pipeline logic solves real engineering problems","Learn how to scale CI/CD with composable patterns for monorepos, microservices, environments, and governance.",[716],"Omid Khan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772721753/frfsm1qfscwrmsyzj1qn.png","2026-04-09",[102,720,721,722],"DevOps platform","tutorial","features",{"featured":24,"template":13,"slug":724},"5-ways-gitlab-pipeline-logic-solves-real-engineering-problems",{"content":726,"config":736},{"title":727,"description":728,"authors":729,"heroImage":731,"date":732,"body":733,"category":9,"tags":734},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[730],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n  \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n  gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n  1. Do I have this cached? → Return it\n  2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│                    CI/CD Pipeline                       │\n│                          │                              │\n│                          ▼                              │\n│   gitlab.com/virtual_registries/container/\u003Cid>/image   │\n└─────────────────────────────────────────────────────────┘\n                           │\n                           ▼\n┌─────────────────────────────────────────────────────────┐\n│            Container Virtual Registry                   │\n│                                                         │\n│  Upstream 1: Docker Hub ────────────────┐               │\n│  Upstream 2: dhi.io (Hardened) ────────┐│               │\n│  Upstream 3: MCR ─────────────────────┐││               │\n│  Upstream 4: Quay.io ────────────────┐│││               │\n│                                      ││││               │\n│                    ┌─────────────────┴┴┴┴──┐            │\n│                    │        Cache          │            │\n│                    │  (manifests + layers) │            │\n│                    └───────────────────────┘            │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficult to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n    group_id=\"785414\",  # Your top-level group ID\n    name=\"platform-images\",\n    description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://registry-1.docker.io\",\n    name=\"Docker Hub\",\n    cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-docker-username\",\n    password=\"your-docker-access-token\",\n    cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://mcr.microsoft.com\",\n    name=\"Microsoft Container Registry\",\n    cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://quay.io\",\n    name=\"Quay.io\",\n    cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n  VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n  \nbuild:\n  image: docker:24\n  services:\n    - docker:24-dind\n  before_script:\n    # Authenticate to GitLab (which handles upstream auth for you)\n    - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n  script:\n    # All of these go through your single virtual registry\n    \n    # Official Docker Hub images (use library/ prefix)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n    \n    # Docker Hardened Images from dhi.io (no prefix needed)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n    \n    # .NET from MCR\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n    entries = client.list_cache_entries(upstream['id'])\n    print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-username\",\n    password=\"your-token\",\n    cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[721,735,722],"product",{"featured":12,"template":13,"slug":737},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":739,"config":749},{"title":740,"description":741,"authors":742,"heroImage":744,"date":745,"category":9,"tags":746,"body":748},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[743],"Nick Veenhof","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","2026-01-08",[255,617,747],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo-agent-platform/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":750,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"promotions":752},[753,767,778,790],{"id":754,"categories":755,"header":757,"text":758,"button":759,"image":764},"ai-modernization",[756],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":760,"config":761},"Get your AI maturity score",{"href":762,"dataGaName":763,"dataGaLocation":237},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":765},{"src":766},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":768,"categories":769,"header":770,"text":758,"button":771,"image":775},"devops-modernization",[735,563],"Are you just managing tools or shipping innovation?",{"text":772,"config":773},"Get your DevOps maturity score",{"href":774,"dataGaName":763,"dataGaLocation":237},"/assessments/devops-modernization-assessment/",{"config":776},{"src":777},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":779,"categories":780,"header":782,"text":758,"button":783,"image":787},"security-modernization",[781],"security","Are you trading speed for security?",{"text":784,"config":785},"Get your security maturity score",{"href":786,"dataGaName":763,"dataGaLocation":237},"/assessments/security-modernization-assessment/",{"config":788},{"src":789},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":791,"paths":792,"header":795,"text":796,"button":797,"image":802},"github-azure-migration",[793,794],"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":798,"config":799},"See how GitLab compares to GitHub",{"href":800,"dataGaName":801,"dataGaLocation":237},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":803},{"src":777},{"header":805,"blurb":806,"button":807,"secondaryButton":812},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":808,"config":809},"Get your free trial",{"href":810,"dataGaName":44,"dataGaLocation":811},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":499,"config":813},{"href":48,"dataGaName":49,"dataGaLocation":811},1776454409398]