Your organization has 200+ developers and pipeline jobs are queuing for 10+ minutes. You currently use shared GitLab.com runners. Design a runner infrastructure strategy using specific runners on Kubernetes with autoscaling, and explain the trade-offs between shared, group, and project-specific runners.
Quick Answer
Deploy the GitLab Runner Operator on Kubernetes with autoscaling executors, use group runners for team-level shared infrastructure, project-specific runners for security-sensitive workloads, and implement runner tags for workload routing. Size based on job concurrency and resource profiles.
Detailed Answer
Runner Types and Scope
Shared runners serve all projects in a GitLab instance—convenient but create noisy neighbor problems and security concerns (a compromised job could access another project's secrets). Group runners serve all projects within a group—ideal for team-level shared infrastructure. Project-specific runners are dedicated to a single project—required for workloads processing sensitive data or requiring specialized hardware (GPU, ARM). In a 200-developer organization, use group runners as the default with project-specific runners for security-critical deployments.
Kubernetes Executor Architecture
The GitLab Runner Kubernetes executor creates a new pod for each CI job. The pod contains at minimum a build container (runs your script), a helper container (handles git clone and artifacts), and optionally service containers (postgres, redis for integration tests). Pods are created on-demand and destroyed after job completion, providing clean isolation and automatic scaling. Configure resource requests/limits per job using runner config or Kubernetes limit ranges.
Autoscaling Strategy
Use the GitLab Runner Helm chart with HPA (Horizontal Pod Autoscaler) based on the number of running jobs. The runner manager pod handles job acquisition; the Kubernetes executor handles pod creation. Set concurrent to control maximum parallel jobs per runner manager. Deploy multiple runner managers (3-5) for HA with anti-affinity rules. Use Kubernetes Cluster Autoscaler to add nodes when pod scheduling pressure increases—configure with appropriate scale-down delay to avoid thrashing.
Performance Optimization
The biggest performance bottleneck is dependency download. Solve with: distributed caching using S3/GCS-backed runner cache, Docker layer caching via a registry mirror or BuildKit cache mounts, and large PVC-backed workspace volumes for builds that reuse data. Use node pools with local SSDs for I/O-intensive builds. Pre-pull common base images using DaemonSets to eliminate image pull latency for frequent job types.
Security Isolation
Runner tags control which runners execute which jobs. Tag runners with their security level (e.g., production-deploy, untrusted-mr). Protected runners only execute jobs from protected branches/tags—essential for deployment jobs. Use Kubernetes namespaces with NetworkPolicies to isolate job pods from each other and from cluster services. For maximum isolation, use Kata Containers or gVisor runtime classes for untrusted workloads.