Kubernetes Integration
Orchestrate test workloads across distributed Kubernetes clusters. Auto-scale execution, manage resources dynamically, and run thousands of tests in parallel.
Overview
As test suites grow, single-machine execution becomes a bottleneck. Kubernetes integration lets WalnutAI distribute test workloads across a cluster of machines, running hundreds of tests simultaneously while managing infrastructure automatically. The result is dramatically faster feedback loops without manual capacity planning.
WalnutAI deploys ephemeral test runner pods that pull test suites from a queue, execute them in isolation, report results, and terminate. Auto-scaling policies ensure the cluster expands when demand peaks, such as after a major merge, and contracts during quiet periods to minimize cost. Each pod runs in a dedicated namespace with strict resource quotas, so test execution never impacts production workloads sharing the same cluster.
For enterprises with multi-cloud or multi-region requirements, WalnutAI supports routing tests to different clusters based on geographic proximity, cloud provider, or available capacity. Infrastructure is managed declaratively via Helm charts or a Kubernetes Operator, fitting naturally into GitOps workflows and existing platform engineering practices.
Setup in 4 Steps
Connect Your Cluster
Provide WalnutAI with kubeconfig access to your Kubernetes cluster. Supports EKS, GKE, AKS, and self-managed clusters. Role-based access ensures WalnutAI only operates within designated namespaces.
Define Test Namespaces
Create dedicated namespaces for test workloads. WalnutAI provisions resources within these namespaces, isolating test execution from production and development workloads.
Configure Auto-Scaling Rules
Set scaling policies that control how WalnutAI expands and contracts test pods. Define minimum and maximum replicas, resource requests, and scaling triggers based on queue depth.
Deploy Test Runners
WalnutAI deploys lightweight test runner pods to your cluster. These pods pull test suites, execute them, report results, and terminate automatically when the run completes.
Key Capabilities
Dynamic Auto-Scaling
Test workloads scale up and down automatically based on demand. When a large test suite is queued, WalnutAI spins up additional pods. When tests complete, resources are released back to the cluster.
Distributed Test Execution
Split test suites across dozens or hundreds of pods running in parallel. WalnutAI coordinates execution, collects results from all pods, and assembles a unified report.
Namespace Isolation
Test workloads run in dedicated namespaces with strict resource quotas and network policies. This prevents test execution from impacting production services sharing the same cluster.
Multi-Cluster Support
Distribute tests across multiple Kubernetes clusters in different regions or cloud providers. WalnutAI routes test workloads to the optimal cluster based on availability and latency.
Helm Chart & Operator
Deploy WalnutAI test infrastructure using a Helm chart or Kubernetes Operator. Manage configuration declaratively, integrate with GitOps workflows, and track deployments via standard Kubernetes tooling.
Ready to integrate Kubernetes?
Scale your test execution across clusters. Our team will help you configure namespaces, auto-scaling, and distributed test runners.