Scaling Kubernetes Infrastructure with Global Services

Managing infrastructure across multiple Kubernetes clusters is a nightmare. You need the same core services everywhere (ingress controllers, DNS management, observability agents, security scanners), but keeping everything in sync across dozens or hundreds of clusters will drive you insane. 

Most teams start by copying configurations, updating values files, and deploying everything manually. This works fine when you have three clusters, but try scaling to fifty clusters and you'll spend your life chasing configuration drift and dealing with security gaps. 

Platform teams know this pain well. Every new cluster means hours of setup work. Every security update means touching every single environment. Miss one cluster and you've got an inconsistent mess that can cause issues later on. 

Global services solve this scaling challenge by automating the deployment and management of services across multiple clusters based on specific criteria. Rather than manually configuring each cluster, you define a service once and let the system handle the replication, customization, and ongoing maintenance across your entire infrastructure.

What Are Global Services?

A global service is a deployment pattern that automatically replicates a service across multiple Kubernetes clusters based on filtering criteria you define. Instead of deploying a service to just one cluster, global services let you specify which clusters should receive the deployment through tags, project associations, or cluster designations.

This approach differs from traditional single-cluster deployments where you manually configure and deploy services one cluster at a time. With global services, you declare the service template once, typically as a Helm chart or custom resource, and the system handles the distribution. When a cluster matches your targeting criteria, the service automatically appears there.

The entire system operates on GitOps principles. Each global service is defined as a custom resource that declares both the service template and the target criteria. Plural ensures these manifests are consistently deployed across all matching clusters, with the ability to customize configurations per cluster when needed. This creates a single source of truth for your infrastructure deployments while maintaining the flexibility to handle cluster-specific requirements.

Core Components and Targeting

Global services operate through a combination of targeting criteria (distribution tags, project associations, management cluster designations, etc.) and templating systems that determine where services deploy and how they're configured. For a cluster to receive a deployment, it must satisfy all specified criteria. For example, if you define both tags and project requirements, the cluster needs both the correct tags and project association.

The service template itself is typically defined as a Helm chart or custom Kubernetes resources. This template serves as the blueprint that gets replicated across all matching clusters. However, identical deployments across different environments rarely work in practice. Because clusters often need environment-specific configurations like different IAM roles, VPC settings, or resource limits, there's often some additional configuration required.

Global services tackle this problem through liquid templating and cluster metadata integration. You can associate cluster-specific variables through the cluster API, storing values like VPC IDs, role ARNs, or region-specific settings directly in the cluster metadata. These variables become available in your value files through liquid template syntax, allowing each cluster to render the same template with its own customized configuration.

The integration with Terraform creates a particularly powerful workflow. Since Terraform typically serves as the source of truth for infrastructure metadata, you can set cluster variables directly in your Terraform configurations where VPC IDs, security group references, and other infrastructure details are already defined. This eliminates the need to duplicate infrastructure information across multiple systems while ensuring your global services have access to the exact configuration data they need for each environment.

Common Use Cases for Global Services

A typical production setup includes ten or more foundational services: AWS Load Balancer Controller, NGINX ingress, external DNS, cert-manager, log aggregation agents, observability tools like Prometheus and Grafana, security scanners, policy engines, and various operators for storage and networking.

Consider the AWS Load Balancer Controller; it needs to run on every cluster, but each deployment requires a different IAM role that matches the cluster's specific AWS configuration. Without global services, you'd maintain separate Helm deployments for each cluster, manually updating role ARNs and VPC settings. With global services, you define the template once and let the liquid templating system pull the correct IAM role from each cluster's metadata.

The targeting system enables strategic deployment patterns beyond simple replication. You might deploy monitoring agents universally but restrict expensive security scanning tools to production environments only. Or you could roll out new ingress controllers to development clusters first, using tags to control the rollout progression. This selective deployment capability transforms global services from a simple replication tool into a sophisticated infrastructure orchestration system.

Advanced Workflows and Automation

Global services become even more powerful when integrated with pipelines and automated update systems. You can pipeline global services, creating a promotion workflow where development-level services get updated first, followed by production deployments after validation. When you make changes to a global service template, the pipeline automatically writes PRs to update the development global service. Once you merge and validate those changes, the pipeline enables promotion to production, writing additional PRs to update the production global service manifests.

The observer system takes automation a step further by monitoring compatibility matrices for new versions of your infrastructure components. These observers watch for updated versions that satisfy your current Kubernetes version constraints (e.g. looking for the latest Ingress NGINX version that supports both Kubernetes 1.32 and 1.33). When a compatible version is detected, the observer automatically triggers a pipeline run, creating PRs with the version updates across all relevant global services.

This creates a nearly autonomous workflow for maintaining your Kubernetes runtime. You configure your global services once, set up pipelines for safe promotion patterns, and establish observers to watch for updates. The system handles spawning PRs for version updates, and you simply review, merge, validate in your development environment, and promote to production. The entire process becomes deterministic and requires minimal ongoing engineering effort—a significant shift from manually tracking and updating dozens of infrastructure components across multiple clusters.

Compliance and Reporting Capabilities

For larger organizations, global services help you actually prove you've got the right security and operational components running consistently across every single cluster. Security protocols often mandate that specific tools, including policy enforcement engines, security scanners, and log aggregation agents, run on every cluster in your infrastructure. Without centralized visibility, validating this compliance becomes a manual audit process across potentially hundreds of environments.

Global services provide this visibility through API-driven compliance reporting. The system maintains complete state information about what's deployed where, including the exact Git repository, Helm chart version, and deployment configuration for every service across every cluster. You can generate compliance reports directly from the security tab in the interface, producing CSV exports that document your entire deployment landscape.

These reports contain the granular detail that compliance teams need: which clusters are running your database monitoring tools, where your policy enforcement engines are deployed, what version of each security scanner is active, and the Git commit hash that deployed each component. This level of documentation turns compliance audits from time-intensive manual processes into straightforward data exports.

Many organizations rely on these capabilities to satisfy their security and regulatory requirements. When auditors need proof that security controls are consistently applied across your infrastructure, you can provide comprehensive, real-time documentation of your deployment state rather than attempting to manually verify configurations across dozens or hundreds of clusters.

Conclusion

The traditional model of deploying Kubernetes services across clusters is unsustainable as organizations grow their Kubernetes footprint. What starts as a manageable process quickly becomes a bottleneck that limits both operational efficiency and security consistency.

Global services turn this on its head by making autonomous infrastructure management through sophisticated targeting, pipeline-driven workflows, and observer-based automation the norm.

For platform teams managing dozens or hundreds of clusters, this approach offers a clear path from manual deployment processes to systematic, auditable infrastructure that maintains itself while satisfying enterprise compliance requirements.