In this self-managed GitLab CI setup, ordinary Java build, test, and artifact-publishing jobs run on Amazon ECS Fargate; jobs that build container images run on Amazon EC2. The split follows what each job needs: Java processes do not require privileged container access, while the team’s selected image-build workflow expects a Docker daemon and reusable image-layer storage. It is a workload-specific design, not a claim that every way of building an image requires EC2.
Why the team self-hosts its GitLab runners
Vivek Itp’s account of the setup says self-hosting began with an artifact-signing requirement, not a goal of reducing cloud costs. The publish step signs JAR artifacts using an AWS Key Management Service (KMS) key, and deployment rejects artifacts that do not verify. In the described design, signing permission belongs to restricted runner identities; project pipeline YAML does not, by itself, determine who can use the key. This is the author’s security rationale and design description, not an independent audit of the system.
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The runner split is by workload rather than by team or environment. As Itp puts it, “The split is not by team or by environment. It is by what the job actually does.” The author’s account describes restricted runners that can access signing credentials and production-facing artifact paths, alongside unrestricted runners that cannot. For a small set of critical projects, it describes guarded CI with fixed runner tags and signing and verification steps.
Why Java jobs fit Fargate, while this Docker workflow does not
Java build, test, and publish jobs
Maven or Gradle builds, tests, and artifact publishing can run as ordinary processes inside a task. In this setup, they do not need privileged container operations or access to the host’s container runtime. Fargate’s managed task boundary therefore suits these jobs: each task receives isolated infrastructure, and the customer has less responsibility for securing the underlying compute. AWS still assigns customers responsibility for areas including network configuration and storage encryption; see its shared responsibility guidance for Amazon ECS.
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Image-building jobs
The selected image-building workflow expects a Docker daemon and reusable layer storage. AWS states that privileged containers or access are unavailable on Fargate and identifies Docker-in-Docker as an affected use case. Fargate containers also cannot access the underlying host or container runtime, which is why a workflow dependent on those capabilities does not fit the team’s Fargate tasks. AWS’s Fargate security considerations describe the restriction.
This limitation should not be stretched into a claim that Fargate has no disk or that no image-building method can run there. The author says alternatives were considered but does not identify or evaluate them, so the account does not establish which other builders would or would not work.
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Fargate storage is not the same as a reusable EC2 layer cache
Fargate does provide task-scoped ephemeral storage. AWS documents a default minimum of 20 GiB for Linux tasks using platform version 1.4.0 or later, configurable up to 200 GiB. That storage serves task image and writable-data needs; it is not the persistent, host-level Docker layer cache the author describes on EC2 workers. See AWS’s Fargate task storage documentation for the platform and version scope.
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How to decide which runner a job needs
The practical question for developers was, “which runner should my job use?” The author’s routine rule is straightforward:
- Jobs that build container images use the EC2 runner tag.
- Other jobs, including Java builds, tests, and artifact publishing, use the Fargate runner tag.
The goal is for developers not to have to choose infrastructure labels for ordinary jobs. In the author’s words, “So Java builds go to Fargate, Docker builds go to EC2, and the developer writes neither of those words.” A job that both builds Java artifacts and builds an image is the boundary case. It may be a sign that the job combines responsibilities that would be clearer as separate jobs, rather than a reason to force every job into one runner category.
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What the split costs operationally
Using both Fargate and EC2 creates two execution paths to maintain. The author lists EC2 host patching and rotation, keeping runner versions aligned with GitLab, monitoring autoscaling, and distinguishing infrastructure failures from project failures. No staff-hour estimate is given.
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Concurrency also needs attention. A high limit can create resource contention; a low limit can leave jobs queued while capacity is idle. The reported mitigation is to make queue status visible to developers, not to prescribe a universal concurrency value. Each installation must tune its limits to its workload and capacity.
What this case study does—and does not—show
This is one team’s workload-based runner design. It explains why the team places ordinary Java work on Fargate and its Docker-daemon-dependent image builds on EC2, but it does not establish that the arrangement is cheapest, fastest, or right for every GitLab installation. The author reports no dollar savings, build-time comparison, quantified cache improvement, or test results comparing alternative image builders. The case is useful as a decision framework: match the runner to a job’s actual privilege, runtime, storage, isolation, and operations requirements.
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