GitOps at 15000+ Clusters with vCluster: What Large-Scale Testing Taught Us
Most Kubernetes platforms and GitOps tools are designed for organizations operating between 10 and 300 clusters.But a different category begins when organizations operate between 1,000 and 10,000 clusters across edge, retail, telecom, IoT, supermarkets, or franchise environments.
At this scale, many GitOps architectures and tools start to break down, even with HA setups and extensive tuning.Public scaling data at this level is limited because these environments are rare, expensive, and difficult to reproduce realistically. Reproducing thousands of Kubernetes clusters with dedicated infrastructure can quickly become prohibitively expensive. In our case, a traditional approach would likely have required an infrastructure budget in the range of €500,000 to €1,000,000.
In this webinar,
- You will learn how we approached predicting the operation of 15,000+ Kubernetes clusters based on a real-world customer requirement. Using large-scale GitOps load testing in a hub-and-spoke architecture, we explored scaling limits, bottlenecks, infrastructure costs, and operational trade-offs.
- How use of vCluster and vCluster Platform + Argo CD Integration to made this level of testing economically feasible. By using virtual Kubernetes clusters instead of fully dedicated physical or cloud-based clusters, we were able to model extreme-scale GitOps behavior within a budget of roughly €50,000 to €100,000, rather than spending several hundred thousand euros or more.
- The session covers what we learned from this large-scale simulation approach, where GitOps systems begin to hit practical limits, and what these findings may tell us about the future of GitOps at extreme scale with Argo CD and Sveltos.
- A practical demo showing how the setup can be reproduced.
You'll will leave with a better understanding of how to reason about GitOps scalability, how to test architectures that are too expensive to reproduce traditionally, and how tools like vCluster and vCluster Platform can help reduce cost while enabling realistic large-scale Kubernetes experimentation.
