Choosing a GitOps Tool#

The term “GitOps” is applied to everything from a simple kubectl apply in a GitHub Actions workflow to a fully reconciled, pull-based deployment architecture with drift detection. These are fundamentally different approaches. Choosing between them depends on your team’s operational maturity, cluster count, and tolerance for running controllers in your cluster.

What GitOps Actually Means#

GitOps, as defined by the OpenGitOps principles (a CNCF sandbox project), has four requirements: declarative desired state, state versioned in git, changes applied automatically, and continuous reconciliation with drift detection. The last two are what separate true GitOps from “CI/CD that uses git.”

GitHub Actions Advanced Patterns#

Once you move past single-file workflows that run npm test on every push, GitHub Actions becomes a platform for building serious CI/CD infrastructure. The features covered here – reusable workflows, composite actions, matrix strategies, OIDC authentication, and caching – are what separate a working pipeline from a production-grade one.

Reusable Workflows#

A reusable workflow is a complete workflow file that other workflows can call like a function. Define it with the workflow_call trigger:

GitHub Actions on ARM64#

ARM64 is no longer optional infrastructure. AWS Graviton instances, Apple Silicon developer machines, and Ampere cloud hosts all run ARM64 natively. If your CI pipeline only builds and tests on x86, you are shipping untested binaries to a growing share of your deployment targets.

GitHub-Hosted ARM64 Runners#

GitHub offers native ARM64 runners. For public repositories, these have been available since late 2024. Private repositories gained access in 2025. Use them with:

Using Minikube for CI, Integration Testing, and Local Development Workflows#

Minikube gives you a real Kubernetes cluster wherever you need one – on a developer laptop, in a GitHub Actions runner, or in any CI environment that has Docker. The patterns differ between local development and CI, but the underlying approach is the same: stand up a cluster, deploy your workload and its dependencies, test against it, tear it down.