> ## Documentation Index > Fetch the complete documentation index at: https://docs.cloudthinker.io/llms.txt > Use this file to discover all available pages before exploring further. # Kai > Manage Kubernetes clusters, optimize container workloads, and troubleshoot EKS, GKE, and AKS with Kai. Kai is CloudThinker's container orchestration expert, specializing in Kubernetes cluster management, workload optimization, autoscaling, and operational troubleshooting across EKS, GKE, AKS, and self-managed clusters. *** ## The problem Kai solves Kubernetes is powerful but deeply complex. Most teams provision resource requests and limits once (or copy them from a template), then never revisit them. Pods get OOMKilled because limits are too low; nodes are underutilized because requests are too high. Cluster autoscaler adds nodes instead of right-sizing workloads. RBAC configurations drift from least-privilege as service accounts accumulate permissions. Operating Kubernetes well requires daily attention from someone with deep expertise: * Monitoring pod resource utilization across hundreds of pods across multiple namespaces * Diagnosing crash loops by reading logs, events, and checking resource constraints * Tuning HPA thresholds, VPA recommendations, and Cluster Autoscaler behavior * Auditing RBAC configurations and network policies for security gaps Most teams have one or two Kubernetes engineers — and they're already overloaded managing infrastructure changes. Proactive optimization rarely happens. *** ## What other tools miss | Tool | What It Does | What's Missing | | ---------------------------------- | ---------------------------------------- | ----------------------------------------------------------------- | | **kubectl** | Direct cluster API access | Raw tool, requires deep expertise, no analysis or recommendations | | **Lens / k9s** | Kubernetes dashboards and CLI | Visualization only, no AI analysis, no recommendations | | **Kubecost** | Kubernetes cost allocation and reporting | Cost visibility only, no troubleshooting or optimization guidance | | **Datadog / Prometheus + Grafana** | Kubernetes metrics and alerting | Monitoring only, still requires expert interpretation to act | | **KEDA / VPA** | Autoscaling automation | Single-purpose tools, no holistic cluster analysis | Kai combines what normally takes kubectl expertise, monitoring dashboards, cost tools, and security scanners — in a single conversational interface that explains issues and recommends specific fixes. *** ## How Kai works 1. **Connects to Kubernetes API** — reads pods, nodes, deployments, services, events, and RBAC configurations across all namespaces 2. **Pulls metrics** — correlates Kubernetes API state with metrics-server data (CPU/memory actual vs. requested) 3. **Identifies inefficiency patterns** — OOMKill history, pending pods, underutilized nodes, misconfigured autoscaling policies 4. **Generates specific recommendations** — exact resource request/limit values based on actual P95 utilization, HPA threshold adjustments, RBAC policy changes 5. **Troubleshoots with context** — when a pod fails, Kai reads logs, events, and resource state simultaneously to identify root cause instead of having you correlate them manually *** ## Capabilities | Domain | Capabilities | | ------------------------- | ----------------------------------------------------------------- | | **Cluster Management** | Health monitoring, node management, resource allocation, upgrades | | **Workload Optimization** | Pod right-sizing, resource requests/limits, scheduling efficiency | | **Autoscaling** | HPA/VPA/Cluster Autoscaler optimization, scaling policies | | **Security** | RBAC auditing, network policies, pod security, secrets management | | **Troubleshooting** | Crash loops, OOMKills, scheduling failures, networking issues | *** ## Supported platforms | Platform | Support Level | | ---------------- | ------------------------------------ | | **Amazon EKS** | Full support with AWS integration | | **Google GKE** | Full support with GCP integration | | **Azure AKS** | Full support with Azure integration | | **Self-Managed** | Kubernetes 1.24+ with metrics-server | *** ## Prompt patterns ### Cluster health ```text theme={null} # Health check @kai check EKS cluster health and pod distribution # Resource utilization @kai analyze cluster resource utilization and identify bottlenecks # Node analysis @kai identify nodes with <30% CPU utilization for consolidation # Multi-cluster view @kai provide health summary across all Kubernetes clusters ``` ### Workload optimization ```text theme={null} # Pod right-sizing @kai analyze pod resource requests/limits and recommend right-sizing # Scheduling efficiency @kai identify pods with resource requests far exceeding actual usage # Cost optimization @kai identify underutilized nodes and recommend consolidation strategy # Namespace analysis @kai analyze resource allocation across namespaces ``` ### Autoscaling ```text theme={null} # HPA review @kai review Horizontal Pod Autoscaler policies and recommend improvements # Scaling analysis @kai analyze scaling patterns and recommend threshold adjustments # VPA assessment @kai evaluate whether Vertical Pod Autoscaler would benefit our workloads # Cluster autoscaling @kai review Cluster Autoscaler configuration for cost efficiency ``` ### Troubleshooting ```text theme={null} # Crash investigation @kai investigate pod crash loops in payment namespace # OOM analysis @kai identify pods experiencing OOMKilled events and recommend fixes # Scheduling issues @kai analyze pending pods and identify scheduling constraints # Network problems @kai investigate network connectivity issues between services ``` ### Security ```text theme={null} # RBAC audit @kai audit RBAC configuration against least-privilege principles # Network policies @kai analyze network policies and recommend security improvements # Pod security @kai identify pods running with excessive privileges # Secrets audit @kai audit secrets management and recommend rotation strategy ``` *** ## Tool usage | Tool | Kai Use Case | | ------------ | -------------------------------------------------------------- | | `#dashboard` | Cluster health, node status, resource utilization, pod metrics | | `#report` | Optimization analysis, security audits, capacity planning | | `#recommend` | Right-sizing, scaling policies, consolidation actions | | `#alert` | OOMKills, node pressure, pod failures, resource thresholds | | `#chart` | Resource trends, scaling patterns, utilization over time | ### Examples with tools ```text theme={null} @kai #dashboard EKS cluster health with node and pod metrics @kai #report cluster optimization opportunities with implementation plan @kai #recommend HPA policies for variable workloads @kai #alert on pod OOMKilled events or node pressure conditions ``` *** ## Effective prompts **Tip: Include cluster context** ```text theme={null} # Good @kai analyze production EKS cluster in us-west-2 for pod resource optimization # Avoid @kai check our containers ``` **Tip: Define success metrics** ```text theme={null} # Good @kai improve cluster utilization while maintaining <30s pod startup and 99.9% availability # Avoid @kai make cluster better ``` *** ## Connection requirements Kai requires Kubernetes cluster access with monitoring capabilities: | Component | Required Access | | ------------------ | ------------------------------------------------- | | **Kubernetes API** | Read access to pods, nodes, deployments, services | | **Metrics Server** | Resource metrics for pods and nodes | | **Events** | Cluster events for troubleshooting | | **Logs** | Container logs for debugging | *** ## Common workflows ### Cluster optimization ```text theme={null} # Step 1: Assess @kai analyze cluster resource utilization # Step 2: Identify waste @kai identify pods with >50% overprovisioned resources # Step 3: Plan @kai #recommend right-sizing with zero-downtime approach # Step 4: Monitor @kai #dashboard track resource utilization after changes ``` ### Incident response ```text theme={null} # Step 1: Identify @kai identify unhealthy pods and failing deployments # Step 2: Investigate @kai analyze logs and events for root cause # Step 3: Remediate @kai #recommend immediate actions to restore service # Step 4: Prevent @kai #recommend changes to prevent recurrence ``` ### Capacity planning ```text theme={null} # Step 1: Baseline @kai analyze current resource consumption patterns # Step 2: Project @kai forecast resource needs for 2x growth # Step 3: Plan @kai #recommend node pool configuration for projected growth # Step 4: Automate @kai #recommend autoscaling policies for demand variations ``` *** ## Next steps Connect Kai to your EKS, GKE, AKS, or self-managed clusters Visualize Kubernetes service dependencies for [RCA](/guide/incident/root-cause-analysis) How Kai investigates Kubernetes incidents automatically Coordinate Kai with [Alex](/guide/agents/alex) for cluster cost + performance optimization