How to scale GitLab runners in Kubernetes under high demand
Learn how to configure automatic scaling for GitLab CI/CD pipelines in Kubernetes clusters using ephemeral containers to optimize costs and performance.
Summary
- Ephemeral runners create a clean, isolated environment for every CI/CD job without accumulating digital waste.
- Automatic scaling adjusts the number of active servers dynamically based on the pending task queue.
- Disposable containers prevent security vulnerabilities in one build from affecting future runs on the same node.
- Efficient resource utilization in Kubernetes reduces idle infrastructure costs during peak and off-peak hours.
- Proper configuration of CPU and memory limits prevents heavy builds from crashing the entire cluster.
Why use ephemeral runners in Kubernetes for CI/CD?
As engineering teams grow, the volume of automated software tests and builds skyrockets. If the infrastructure running these tasks cannot keep up, the waiting queue grows and the delivery time for new features plummets. In practice, this means developers spend precious hours staring at a loading screen waiting for code to compile.
To solve this productivity bottleneck, the most efficient modern approach involves utilizing an ephemeral Kubernetes runners architecture for continuous integration builds. A runner is the agent that executes commands defined in automation routines, while Kubernetes acts as the master orchestrator managing where and when these agents run. Ephemeral means every task gets an exclusive container that is born from scratch, executes the work, and disappears immediately afterward without leaving a trace.
How automatic scaling works under high demand
Imagine a shopping mall parking lot with only five fixed spaces, but facing hundreds of cars on holiday eves. If the space does not expand, street traffic jams up. With Kubernetes and GitLab, operators define an intelligent mechanism called autoscaling, which constantly monitors the size of the pending automation task queue.
When the queue grows, the system launches new containers within seconds to absorb the workload, distributing processes across multiple cluster nodes. The main advantage of this strategy lies in the fact that as soon as the last test finishes, the extra containers shut down, returning cloud power consumption or spending back to the baseline. This elasticity ensures budget predictability and avoids unpleasant surprises on the end-of-month invoice.
Configuring the GitLab executor in the cluster
To set this machinery in motion, the first step involves deploying the official GitLab Runner operator inside your cluster. This operator reads configuration instructions and communicates directly with the platform to fetch new build jobs as soon as developers trigger them.
Below is a basic YAML configuration file example, instructing the system to use the Kubernetes executor by creating isolated containers for each step of the process:
concurrent: 10
check_interval: 3
runners:
- name: "kubernetes-ephemeral-runner"
url: "https://gitlab.com"
token: "GLRT-EXAMPLE-TOKEN"
executor: "kubernetes"
kubernetes:
namespace: "gitlab-runners"
image: "ubuntu:22.04"
privileged: false
poll_interval: 5
poll_timeout: 300
In this file, the concurrent parameter determines how many tasks can run simultaneously, while the executor explicitly points to the container environment managed by Kubernetes. Keeping the privileged option set to false is a golden rule for security, preventing malicious code executed during tests from gaining full control over the main server infrastructure.
Setting resource limits to prevent bottlenecks
A common mistake when configuring high-demand environments is forgetting to enforce strict memory and processing consumption limits for each build container. If a single test process starts consuming all available RAM due to a code leak, it can crash other critical services running on the same physical server.
To shield the system against this type of failure, configure rigid resource request and limit parameters in your deployment file. This way, if a process exceeds the healthy limit established by engineering, Kubernetes itself steps in and terminates that specific task, preserving the stability of the rest of the operation and ensuring other developers' workflows are not interrupted.
Final Considerations
Scaling CI/CD runners using ephemeral containers in Kubernetes clusters radically transforms the operational efficiency of a technology company. By eliminating the idle waste of traditional servers and ensuring a clean environment for every test cycle, teams gain speed, security, and financial predictability. The initial learning curve investment is quickly offset by stability and a drastic reduction in software delivery time.