CI/CD optimization starts with visibility. Building a successful DevOps platform at enterprise scale should include understanding pipeline performance, job execution patterns, and quantifiable operational insights — especially for organizations running GitLab self-managed instances.To help GitLab customers maximize their platform investments, we developed the GitLab CI/CD Observability solution as part of our Platform Excellence program, which transforms raw pipeline metrics into actionable operational insights.A leading financial services organization partnered with GitLab's customer success architect to gain visibility into their GitLab self-managed deployment. Together, we implemented a containerized observability solution combining the open-source gitlab-ci-pipelines-exporter with enterprise-grade Prometheus and Grafana infrastructure.In this article, you'll learn the challenges they faced managing pipelines at scale and how GitLab CI/CD Observability addressed them with a practical, end-to-end implementation.The challenge: Measuring CI/CD performanceBefore implementing any observability solution, define your measurement landscape:What metrics matter? Pipeline duration, job success rates, queue times, runner utilizationWho needs visibility? Developers, DevOps engineers, platform teams, leadershipWhat decisions will this drive? Infrastructure investment, bottleneck remediation, capacity planningSolution architecture: A full set of dashboards for observabilityOnce deployed, the observability stack provides a set of Grafana dashboards that give real-time and historical visibility into your CI/CD platform. A typical deployment includes:Pipeline Overview Dashboard: A top-level view showing total pipeline runs, success/failure rates over time (as stacked bar or time-series charts), and average pipeline duration trends. Panels use color-coded status indicators (green for success, red for failure, amber for cancelled) so platform teams can spot degradation at a glance.Job Performance Dashboard: Drill-down panels showing individual job duration distributions (histogram), the top 10 slowest jobs by average duration, and job failure heatmaps by project and stage. This is where teams identify specific bottleneck jobs worth optimizing.Runner & Infrastructure Dashboard: Combines Node Exporter host metrics (CPU, memory, disk) with pipeline queue-time data to correlate infrastructure saturation with pipeline wait times. Useful for capacity planning decisions such as scaling runner pools or upgrading instance sizes.Deployment Frequency Dashboard: Tracks deployment count and deployment duration over time per environment, aligned with DORA metrics. Helps engineering leadership assess delivery throughput and environment drift (commits behind main).Each dashboard is provisioned automatically via Grafana's file-based provisioning, so it deploys consistently across environments. The dashboards can be further customized with Grafana variables to filter by project, ref/branch, or time range.The solution requires two exporters:Pipeline Exporter: Collects CI/CD metrics via GitLab API (pipeline duration, job status, deployments)Node Exporter: Collects host-level metrics (CPU, memory, disk) for infrastructure correlationPrerequisites:GitLab Self-Managed Version 18.1+Container orchestration platform: A Kubernetes cluster (recommended for enterprise deployments) or a container runtime such as Docker/Podman for smaller scale or proof-of-concept environments. The primary deployment guide below targets Kubernetes; a Docker Compose alternative is provided in the appendix for local testing and evaluationGitLab Personal Access Token (read_api scope)Kubernetes deployment (recommended)For enterprise environments, deploy each component as a separate Deployment within a dedicated namespace. This approach integrates with existing cluster infrastructure, secrets management, and network policies.1. Create namespace and secret kubectl create namespace gitlab-observability