GitLab Runner Case Study

Executive Summary

This GitLab Runner Case Study explores how KineticSkunk rebuilt its CI/CD systems by deploying a bespoke fleet of custom GitLab Runners. The result? More than 70% faster validation, real-time feedback, hardened security, and a self-healing build pipeline. This GitLab Runner Case Study outlines how custom runners improved performance metrics, making it a framework for future CI/CD implementations.

Background and Objectives – GitLab Runner Case Study

As a consultancy operating across AWS, Azure, and Red Hat OpenShift, KineticSkunk needed to evolve beyond legacy Jenkins and static agents. The main goals included:

• Accelerate feedback loops on feature branches
• Secure builds using vault-managed credentials
• Scale automatically to handle test surges
• Standardize job templates across services

These objectives required a scalable, high-availability solution without compromising compliance or development velocity.

Key Challenges – GitLab Runner Case Study

The team faced several sticky points that made rapid delivery difficult:

• Agent Pool Saturation: Shared runners could not handle spikes from heavy test jobs, especially Selenium and GenAI suites.
• Configuration Drift: Tooling inconsistencies led to brittle pipelines.
• Secrets Management: Needed secure, one-time secret injection during builds.
• Availability Risks: Single-AZ agents posed operational exposure.

GitLab Auto-scaling Runner Solution Architecture

To tackle these problems, KineticSkunk adopted a robust architecture optimized for automation, consistency, and fault tolerance.

Gitlab Dynamic Agent Provisioning

We used Kubernetes and cloud VMs to start a runner for each job, then shut it down after the job finished to keep things flexible.

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Gitlab Immutable Configuration Templates

All job definitions were consolidated into centrally managed YAML templates, preventing drift and promoting reusability.

Gitlab Secure Secret Injection

Integrated with HashiCorp Vault, pipelines retrieved time-limited tokens without storing sensitive data locally.
Learn more about HashiCorp Vault’s secrets engine and how it integrates with CI/CD systems.

Gitlab Multi-Zone Resilience

Runners spanned multiple availability zones, equipped with health checks and auto-recovery via a monitoring system to prevent outages.

Implementation Components

COMPONENT	APPROACH
Agent Pools	EKS pods and cloud VMs with cleanup hooks
Job Dispatch Logic	Pipeline tagging and workload prioritization
Configuration Store	Git-backed YAML templates and approval workflows
Observability	Real-time metrics, queue monitoring, and auto-scaling triggers

Results & Metrics

• Queue Reduction: 75% less wait time during traffic surges
• Runtime Efficiency: 60% faster execution across most jobs
• High Availability: >99.9% uptime, with no CI-induced outages
• Security Compliance: No persistent secrets, full audit trail verified

CI/CD Infrastructure Case Study Lessons Learned

• Template Governance: Enforcing shared YAML patterns removed 90% of configuration-related errors
• Autoscaling Lag: Reduced startup delay with leaner runner images
• Infrastructure Redundancy: Moving to multi-zone saved hours of downtime during an unplanned event

Conclusion

This GitLab Runner case study shows how KineticSkunk improved its CI/CD with custom runners that scale up and down automatically. By fixing slow builds, safer secrets handling, and system reliability, the team got clear wins—job times fell by 60% and uptime stayed above 99.9%. They used on-demand runners, a secure vault to add credentials when needed, and a central setup to manage configs. As teams push for faster, safer releases, this proves the value of tailored GitLab Runner solutions.

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Explore Our DevOps & DevSecOps Solutions

Looking to optimize your CI/CD pipelines or enhance cloud security? Discover how our tailored solutions can drive efficiency, resilience, and compliance:

DevOps Solutions

• CI/CD pipeline design and automation
• Infrastructure as Code (IaC) using Terraform, Pulumi, or Ansible
• Cloud-native deployments on AWS, Azure, GCP
• Kubernetes orchestration and scaling
• Monitoring, logging, and observability systems

DevSecOps Solutions

• Secure pipeline design with integrated security checks
• Secrets management with Vault, AWS Secrets Manager, etc.
• Static and dynamic security testing (SAST/DAST)
• Compliance automation and audit readiness
• Container image scanning and policy enforcement

Learn more:
DevOps Solutions | DevSecOps Solutions