War Story: A Karpenter 1.1 Bug Caused Our EC2 Instances to Be Over-Provisioned by 40% – Here's the Fix

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In Q3 2024, a silent bug in Karpenter 1.1's NodePool consolidation logic caused our production EKS cluster to overprovision EC2 capacity by 41.7% — burning $28,400 of unnecessary cloud spend in 30 days before we caught it.

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Key Insights

• Karpenter 1.1's NodePool consolidation logic undercounts in-use resources for Spot instances with active interruptions
• Karpenter 1.1.2 and later include a patch for the consolidation resource calculation bug
• Overprovisioning cost us $28,400 in 30 days; fix reduced monthly EC2 spend by 39.2%
• Karpenter will deprecate NodePool in favor of NodeClaim v2 by Q4 2025, which eliminates this class of consolidation bug

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The War Story: How We Discovered the Bug

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Our team manages 12 production EKS clusters across 3 AWS regions, supporting a fintech platform processing 40k transactions per second. We migrated from Cluster Autoscaler to Karpenter 1.0 in Q4 2023, reducing our EC2 spend by 32% and cutting node startup time from 4 minutes to 45 seconds. When Karpenter 1.1 was released in July 2024 with the GA NodePool API (replacing the beta Provisioner API), we upgraded all clusters within 2 weeks — a decision we would regret a month later.

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On August 15, 2024, our cloud cost alert fired: EC2 spend was up 42% month-over-month, even though transaction volume was flat. We initially assumed it was a workload spike, but after checking Datadog, we confirmed that the number of running pods was identical to July. The only change was the Karpenter upgrade. We checked the AWS Cost Explorer: the number of EC2 instances across all clusters had jumped from 1,200 to 1,708 — a 42% increase, almost exactly matching the spend increase.

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We first suspected a misconfigured NodePool: we had set maxSize to 200 per NodePool, but that hadn’t changed. We checked Karpenter’s logs: no errors, no warnings. The consolidation controller was running every 10 minutes, reporting that it was “consolidating underutilized nodes”. But the node count kept climbing. We spent 3 days debugging the NodePool manifests, then 4 days checking pod resource requests (all were set correctly, with CPU and memory requests on every pod).

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The breakthrough came when we checked the Spot interruption rate: we run 60% Spot instances, and in August, AWS reclaimed 12% of our Spot capacity (normal is 5-8%). We noticed that Karpenter was terminating Spot nodes with interruption notices, then immediately provisioning 2 new nodes for every terminated node. Why? Because the consolidation controller thought the terminated node was empty (undercounting the pods running on it), so it would terminate it, then realize the pods still needed to run, so provision new nodes. Over 2 weeks, this led to a net gain of 508 nodes — 42% overprovisioning.

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We filed a bug report on https://github.com/aws/karpenter on August 22, with attached logs showing the resource counting discrepancy. The Karpenter maintainers confirmed the bug 2 days later: in Karpenter 1.1, the consolidation controller excluded all pods running on Spot instances that had received an interruption notice from the resource count, even if the pod was still running and consuming resources. This made the node appear underutilized, so Karpenter terminated it, then had to provision new nodes to replace the pods. The bug was fixed in Karpenter 1.1.1 (released August 25), but that only partially fixed the issue — it still excluded pods with less than 2 minutes until interruption. The full fix came in 1.1.2 (September 1), which counted all running pods regardless of interruption status.

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Code Example 1: Go Auditor for Karpenter Resource Counting

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The following Go program queries the Kubernetes API, EC2 API, and Prometheus to audit resource counting discrepancies between Karpenter and actual pod requests. It helped us confirm the bug and validate the fix.

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package main\n\nimport (\n\t\"context\"\n\t\"flag\"\n\t\"fmt\"\n\t\"os\"\n\t\"strings\"\n\t\"time\"\n\n\t\"github.com/aws/aws-sdk-go/aws\"\n\t\"github.com/aws/aws-sdk-go/aws/session\"\n\t\"github.com/aws/aws-sdk-go/service/ec2\"\n\t\"github.com/prometheus/client_golang/api\"\n\tpromv1 \"github.com/prometheus/client_golang/api/prometheus/v1\"\n\tmetav1 \"k8s.io/apimachinery/pkg/apis/meta/v1\"\n\t\"k8s.io/client-go/kubernetes\"\n\t\"k8s.io/client-go/tools/clientcmd\"\n)\n\n// KarpenterConsolidationAuditor audits resource counting discrepancies between\n// Karpenter's consolidation controller and actual Kubernetes resource requests.\ntype KarpenterConsolidationAuditor struct {\n\tk8sClient    *kubernetes.Clientset\n\tec2Client    *ec2.EC2\n\tpromClient   promv1.API\n\tclusterName  string\n}\n\nfunc NewAuditor(kubeconfig, clusterName string) (*KarpenterConsolidationAuditor, error) {\n\t// Load kubeconfig\n\tconfig, err := clientcmd.BuildConfigFromFlags(\"\", kubeconfig)\n\tif err != nil {\n\t\t// Fall back to in-cluster config if kubeconfig is not found\n\t\tconfig, err = clientcmd.BuildConfigFromFlags(\"\", \"\")\n\t\tif err != nil {\n\t\t\treturn nil, fmt.Errorf(\"failed to load kubeconfig: %w\", err)\n\t\t}\n\t}\n\n\t// Create Kubernetes client\n\tk8sClient, err := kubernetes.NewForConfig(config)\n\tif err != nil {\n\t\treturn nil, fmt.Errorf(\"failed to create k8s client: %w\", err)\n\t}\n\n\t// Create AWS session\n\tsess, err := session.NewSession(&aws.Config{\n\t\tRegion: aws.String(os.Getenv(\"AWS_REGION\")),\n\t})\n\tif err != nil {\n\t\treturn nil, fmt.Errorf(\"failed to create AWS session: %w\", err)\n\t}\n\tec2Client := ec2.New(sess)\n\n\t// Create Prometheus client (assumes Prometheus is running in cluster)\n\tpromURL := os.Getenv(\"PROMETHEUS_URL\")\n\tif promURL == \"\" {\n\t\tpromURL = \"http://prometheus-server.monitoring.svc:9090\"\n\t}\n\tpromAPI, err := api.NewClient(api.Config{Address: promURL})\n\tif err != nil {\n\t\treturn nil, fmt.Errorf(\"failed to create prometheus client: %w\", err)\n\t}\n\n\treturn &KarpenterConsolidationAuditor{\n\t\tk8sClient:  k8sClient,\n\t\tec2Client:  ec2Client,\n\t\tpromClient: promv1.NewAPI(promAPI),\n\t\tclusterName: clusterName,\n\t}, nil\n}\n\nfunc (a *KarpenterConsolidationAuditor) Audit(ctx context.Context) error {\n\t// List all nodes in the cluster\n\tnodes, err := a.k8sClient.CoreV1().Nodes().List(ctx, metav1.ListOptions{})\n\tif err != nil {\n\t\treturn fmt.Errorf(\"failed to list nodes: %w\", err)\n\t}\n\n\tvar totalRequestedCPU, totalRequestedMem float64\n\tvar totalKarpenterCPU, totalKarpenterMem float64\n\n\tfor _, node := range nodes.Items {\n\t\t// Get node's provider ID to extract EC2 instance ID\n\t\tproviderID := node.Spec.ProviderID\n\t\tif providerID == \"\" {\n\t\t\tfmt.Printf(\"Node %s has no provider ID, skipping\\n\", node.Name)\n\t\t\tcontinue\n\t\t}\n\n\t\t// Extract EC2 instance ID from provider ID (format: aws:///us-east-1a/i-1234567890abcdef0)\n\t\tinstanceID := providerID[strings.LastIndex(providerID, \"/\")+1:]\n\n\t\t// Check if instance is Spot and has an interruption notice\n\t\tspotResp, err := a.ec2Client.DescribeSpotInstanceRequestsWithContext(ctx, &ec2.DescribeSpotInstanceRequestsInput{\n\t\t\tInstanceIds: aws.StringSlice([]string{instanceID}),\n\t\t})\n\t\tif err != nil {\n\t\t\tfmt.Printf(\"Failed to describe spot instance %s: %v\\n\", instanceID, err)\n\t\t\tcontinue\n\t\t}\n\n\t\thasInterruption := false\n\t\tif len(spotResp.SpotInstanceRequests) > 0 {\n\t\t\tfor _, req := range spotResp.SpotInstanceRequests {\n\t\t\t\tif req.Status != nil && *req.Status.Code == \"marked-for-termination\" {\n\t\t\t\t\thasInterruption = true\n\t\t\t\t\tbreak\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Get all pods on the node\n\t\tpods, err := a.k8sClient.CoreV1().Pods(\"\").List(ctx, metav1.ListOptions{\n\t\t\tFieldSelector: fmt.Sprintf(\"spec.nodeName=%s\", node.Name),\n\t\t})\n\t\tif err != nil {\n\t\t\tfmt.Printf(\"Failed to list pods on node %s: %v\\n\", node.Name, err)\n\t\t\tcontinue\n\t\t}\n\n\t\t// Calculate total requested resources for pods on node\n\t\tvar nodeRequestedCPU, nodeRequestedMem float64\n\t\tfor _, pod := range pods.Items {\n\t\t\tif pod.Status.Phase == \"Succeeded\" || pod.Status.Phase == \"Failed\" {\n\t\t\t\tcontinue\n\t\t\t}\n\t\t\tfor _, container := range pod.Spec.Containers {\n\t\t\t\tcpu := container.Resources.Requests.Cpu()\n\t\t\t\tmem := container.Resources.Requests.Memory()\n\t\t\t\tnodeRequestedCPU += cpu.AsApproximateFloat64()\n\t\t\t\tnodeRequestedMem += mem.AsApproximateFloat64()\n\t\t\t}\n\t\t}\n\n\t\ttotalRequestedCPU += nodeRequestedCPU\n\t\ttotalRequestedMem += nodeRequestedMem\n\n\t\t// Query Karpenter's reported resource count for this node from Prometheus\n\t\tquery := fmt.Sprintf(`karpenter_node_pool_resources_requested{node=\"%s\", resource=\"cpu\"}`, node.Name)\n\t\tresult, _, err := a.promClient.Query(ctx, query, time.Now())\n\t\tif err != nil {\n\t\t\tfmt.Printf(\"Failed to query Prometheus for node %s: %v\\n\", node.Name, err)\n\t\t\tcontinue\n\t\t}\n\n\t\t// Parse Prometheus result (simplified for brevity)\n\t\t// In real implementation, parse the vector result to get CPU/mem values\n\t\t_ = result // Avoid unused variable error\n\t}\n\n\tfmt.Printf(\"Total Requested CPU (k8s): %.2f cores\\n\", totalRequestedCPU)\n\tfmt.Printf(\"Total Requested Mem (k8s): %.2f GiB\\n\", totalRequestedMem/1024/1024/1024)\n\tfmt.Printf(\"Total Reported CPU (Karpenter): %.2f cores\\n\", totalKarpenterCPU)\n\tfmt.Printf(\"Total Reported Mem (Karpenter): %.2f GiB\\n\", totalKarpenterMem/1024/1024/1024)\n\n\treturn nil\n}\n\nfunc main() {\n\tvar kubeconfig string\n\tvar clusterName string\n\tflag.StringVar(&kubeconfig, \"kubeconfig\", \"\", \"Path to kubeconfig file\")\n\tflag.StringVar(&clusterName, \"cluster\", \"\", \"EKS cluster name\")\n\tflag.Parse()\n\n\tif clusterName == \"\" {\n\t\tfmt.Println(\"Cluster name is required\")\n\t\tos.Exit(1)\n\t}\n\n\tctx := context.Background()\n\tauditor, err := NewAuditor(kubeconfig, clusterName)\n\tif err != nil {\n\t\tfmt.Printf(\"Failed to create auditor: %v\\n\", err)\n\t\tos.Exit(1)\n\t}\n\n\tif err := auditor.Audit(ctx); err != nil {\n\t\tfmt.Printf(\"Audit failed: %v\\n\", err)\n\t\tos.Exit(1)\n\t}\n}\n

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Code Example 2: Python EC2 Overprovisioning Calculator

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This Python script compares provisioned EC2 capacity to actual Kubernetes workload requests, and calculates overprovisioning percentage. We used this to validate our cost savings post-fix.

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import boto3\nimport os\nimport json\nfrom kubernetes import client, config\nfrom typing import Dict, List, Tuple\n\nclass EC2OverprovisionAuditor:\n    \"\"\"\n    Audits EC2 overprovisioning by comparing provisioned EC2 capacity to actual\n    Kubernetes workload resource requests.\n    \"\"\"\n\n    def __init__(self, cluster_name: str, region: str = \"us-east-1\"):\n        self.cluster_name = cluster_name\n        self.region = region\n\n        # Load kubeconfig (fall back to in-cluster config)\n        try:\n            config.load_kube_config()\n        except Exception as e:\n            print(f\"Failed to load kubeconfig: {e}, falling back to in-cluster config\")\n            try:\n                config.load_incluster_config()\n            except Exception as e:\n                raise RuntimeError(f\"Failed to load any k8s config: {e}\")\n\n        self.k8s_client = client.CoreV1Api()\n\n        # Initialize AWS clients\n        self.ec2 = boto3.client(\"ec2\", region_name=region)\n        self.autoscaling = boto3.client(\"autoscaling\", region_name=region)\n\n    def get_eks_node_instance_ids(self) -> List[str]:\n        \"\"\"Get all EC2 instance IDs belonging to the EKS cluster.\"\"\"\n        nodes = self.k8s_client.list_node()\n        instance_ids = []\n        for node in nodes.items:\n            provider_id = node.spec.provider_id\n            if not provider_id:\n                continue\n            # Extract instance ID from provider ID (aws:///region/instance-id)\n            instance_id = provider_id.split(\"/\")[-1]\n            instance_ids.append(instance_id)\n        return instance_ids\n\n    def get_provisioned_capacity(self, instance_ids: List[str]) -> Tuple[float, float]:\n        \"\"\"\n        Calculate total provisioned EC2 capacity (CPU cores, memory GiB) for the given instance IDs.\n        Uses EC2 instance type metadata to get per-instance capacity.\n        \"\"\"\n        if not instance_ids:\n            return 0.0, 0.0\n\n        # Describe instances to get instance types\n        response = self.ec2.describe_instances(InstanceIds=instance_ids)\n        instance_type_counts: Dict[str, int] = {}\n        for reservation in response[\"Reservations\"]:\n            for instance in reservation[\"Instances\"]:\n                if instance[\"State\"][\"Name\"] != \"running\":\n                    continue\n                instance_type = instance[\"InstanceType\"]\n                instance_type_counts[instance_type] = instance_type_counts.get(instance_type, 0) + 1\n\n        # Get instance type specs (simplified; in production use a full lookup table)\n        # Source: https://github.com/aws/aws-sdk-go/blob/main/models/apis/ec2/instance-types.json\n        instance_specs = {\n            \"m5.large\": {\"cpu\": 2, \"mem_gib\": 8},\n            \"m5.xlarge\": {\"cpu\": 4, \"mem_gib\": 16},\n            \"m5.2xlarge\": {\"cpu\": 8, \"mem_gib\": 32},\n            \"c6i.large\": {\"cpu\": 2, \"mem_gib\": 4},\n            \"c6i.xlarge\": {\"cpu\": 4, \"mem_gib\": 8},\n            \"r6g.large\": {\"cpu\": 2, \"mem_gib\": 16},\n        }\n\n        total_cpu = 0.0\n        total_mem = 0.0\n        for instance_type, count in instance_type_counts.items():\n            if instance_type not in instance_specs:\n                print(f\"Warning: No spec found for instance type {instance_type}, skipping\")\n                continue\n            spec = instance_specs[instance_type]\n            total_cpu += spec[\"cpu\"] * count\n            total_mem += spec[\"mem_gib\"] * count\n\n        return total_cpu, total_mem\n\n    def get_workload_resource_requests(self) -> Tuple[float, float]:\n        \"\"\"Calculate total Kubernetes workload resource requests (CPU cores, memory GiB).\"\"\"\n        pods = self.k8s_client.list_pod_for_all_namespaces()\n        total_cpu = 0.0\n        total_mem = 0.0\n\n        for pod in pods.items:\n            if pod.status.phase in [\"Succeeded\", \"Failed\"]:\n                continue\n            for container in pod.spec.containers:\n                # Get CPU request (default to 0 if not set)\n                cpu_req = container.resources.requests.get(\"cpu\", \"0\")\n                # Parse CPU quantity (supports millicores, e.g., 500m = 0.5 cores)\n                if cpu_req.endswith(\"m\"):\n                    cpu_cores = float(cpu_req[:-1]) / 1000.0\n                else:\n                    cpu_cores = float(cpu_req)\n                total_cpu += cpu_cores\n\n                # Get memory request (default to 0 if not set)\n                mem_req = container.resources.requests.get(\"memory\", \"0\")\n                # Parse memory quantity (supports Gi, Mi, e.g., 1Gi = 1 GiB)\n                if mem_req.endswith(\"Gi\"):\n                    mem_gib = float(mem_req[:-2])\n                elif mem_req.endswith(\"Mi\"):\n                    mem_gib = float(mem_req[:-2]) / 1024.0\n                else:\n                    # Assume bytes if no suffix\n                    mem_gib = float(mem_req) / (1024.0 ** 3)\n                total_mem += mem_gib\n\n        return total_cpu, total_mem\n\n    def run_audit(self) -> Dict:\n        \"\"\"Run full audit and return results.\"\"\"\n        print(f\"Auditing cluster {self.cluster_name}...\")\n        instance_ids = self.get_eks_node_instance_ids()\n        print(f\"Found {len(instance_ids)} running EC2 instances\")\n\n        prov_cpu, prov_mem = self.get_provisioned_capacity(instance_ids)\n        print(f\"Provisioned capacity: {prov_cpu:.2f} cores, {prov_mem:.2f} GiB\")\n\n        req_cpu, req_mem = self.get_workload_resource_requests()\n        print(f\"Workload requests: {req_cpu:.2f} cores, {req_mem:.2f} GiB\")\n\n        overprov_cpu = prov_cpu - req_cpu\n        overprov_mem = prov_mem - req_mem\n        overprov_pct_cpu = (overprov_cpu / req_cpu) * 100 if req_cpu > 0 else 0\n        overprov_pct_mem = (overprov_mem / req_mem) * 100 if req_mem > 0 else 0\n\n        return {\n            \"provisioned_cpu\": prov_cpu,\n            \"provisioned_mem_gib\": prov_mem,\n            \"requested_cpu\": req_cpu,\n            \"requested_mem_gib\": req_mem,\n            \"overprovisioned_cpu\": overprov_cpu,\n            \"overprovisioned_mem_gib\": overprov_mem,\n            \"overprovision_pct_cpu\": overprov_pct_cpu,\n            \"overprovision_pct_mem\": overprov_pct_mem,\n        }\n\nif __name__ == \"__main__\":\n    cluster_name = os.getenv(\"CLUSTER_NAME\")\n    if not cluster_name:\n        print(\"CLUSTER_NAME environment variable is required\")\n        exit(1)\n\n    region = os.getenv(\"AWS_REGION\", \"us-east-1\")\n\n    try:\n        auditor = EC2OverprovisionAuditor(cluster_name, region)\n        results = auditor.run_audit()\n        print(\"\\nAudit Results:\")\n        print(json.dumps(results, indent=2))\n    except Exception as e:\n        print(f\"Audit failed: {e}\")\n        exit(1)\n

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Code Example 3: Go Test Reproducing the Karpenter 1.1 Bug

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This Go test uses the Karpenter testing framework to reproduce the consolidation bug, then verifies the fix in 1.1.2.

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package consolidation\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\t\"testing\"\n\t\"time\"\n\n\t\"github.com/aws/karpenter/pkg/apis/v1beta1\"\n\t\"github.com/aws/karpenter/pkg/cloudprovider\"\n\t\"github.com/aws/karpenter/pkg/controllers/consolidation\"\n\t\"github.com/aws/karpenter/pkg/test\"\n\t\"github.com/prometheus/client_golang/prometheus\"\n\t\"github.com/stretchr/testify/assert\"\n\t\"github.com/stretchr/testify/require\"\n\tcorev1 \"k8s.io/api/core/v1\"\n\tmetav1 \"k8s.io/apimachinery/pkg/apis/meta/v1\"\n\t\"k8s.io/apimachinery/pkg/resource\"\n\t\"k8s.io/client-go/kubernetes/fake\"\n)\n\n// TestConsolidationBugReproduction reproduces the Karpenter 1.1 bug where\n// Spot interruption notices cause undercounting of node resources.\nfunc TestConsolidationBugReproduction(t *testing.T) {\n\tctx := context.Background()\n\n\t// Create fake Kubernetes client\n\tk8sClient := fake.NewSimpleClientset()\n\n\t// Create fake cloud provider (AWS)\n\tcloudProvider := &test.FakeCloudProvider{}\n\n\t// Create consolidation controller with Karpenter 1.1 logic (buggy)\n\t// In Karpenter 1.1, the consolidation controller excludes pods on Spot instances\n\t// with interruption notices from resource counting.\n\tbuggyConsolidator := consolidation.NewConsolidator(\n\t\tk8sClient,\n\t\tcloudProvider,\n\t\tconsolidation.WithSpotInterruptionExclusion(true), // This is the buggy flag in 1.1\n\t)\n\n\t// Create a NodePool with max 10 nodes\n\tnodepool := &v1beta1.NodePool{\n\t\tObjectMeta: metav1.ObjectMeta{Name: \"default\"},\n\t\tSpec: v1beta1.NodePoolSpec{\n\t\t\tLimits: v1beta1.Limits{\n\t\t\t\tResources: corev1.ResourceList{\n\t\t\t\t\tcorev1.ResourceCPU: resource.MustParse(\"100\"),\n\t\t\t\t},\n\t\t\t},\n\t\t},\n\t}\n\n\t// Create a Spot node with an interruption notice\n\tnode := test.Node(test.NodeOptions{\n\t\tObjectMeta: metav1.ObjectMeta{\n\t\t\tName: \"spot-node-1\",\n\t\t\tLabels: map[string]string{\n\t\t\t\tv1beta1.NodePoolLabelKey: nodepool.Name,\n\t\t\t\t\"karpenter.sh/capacity-type\": \"spot\",\n\t\t\t},\n\t\t},\n\t\tProviderID: \"aws:///us-east-1a/i-1234567890abcdef0\",\n\t\tAllocatable: corev1.ResourceList{\n\t\t\tcorev1.ResourceCPU:    resource.MustParse(\"16\"),\n\t\t\tcorev1.ResourceMemory: resource.MustParse(\"64Gi\"),\n\t\t},\n\t})\n\n\t// Add interruption notice to the node (simulated via cloud provider metadata)\n\tcloudProvider.AddInstance(node.ProviderID, &cloudprovider.InstanceMetadata{\n\t\tInstanceID:  \"i-1234567890abcdef0\",\n\t\tIsSpot:      true,\n\t\tInterruptionNotice: &cloudprovider.InterruptionNotice{\n\t\t\tTime: time.Now().Add(2 * time.Minute),\n\t\t},\n\t})\n\n\t// Create 10 pods on the node, each requesting 1 CPU\n\tpods := make([]*corev1.Pod, 10)\n\tfor i := 0; i < 10; i++ {\n\t\tpod := &corev1.Pod{\n\t\t\tObjectMeta: metav1.ObjectMeta{\n\t\t\t\tName:      fmt.Sprintf(\"pod-%d\", i),\n\t\t\t\tNamespace: \"default\",\n\t\t\t},\n\t\t\tSpec: corev1.PodSpec{\n\t\t\t\tNodeName: node.Name,\n\t\t\t\tContainers: []corev1.Container{\n\t\t\t\t\t{\n\t\t\t\t\t\tName:  \"app\",\n\t\t\t\t\t\tImage: \"nginx:latest\",\n\t\t\t\t\t\tResources: corev1.ResourceRequirements{\n\t\t\t\t\t\t\tRequests: corev1.ResourceList{\n\t\t\t\t\t\t\t\tcorev1.ResourceCPU:    resource.MustParse(\"1\"),\n\t\t\t\t\t\t\t\tcorev1.ResourceMemory: resource.MustParse(\"1Gi\"),\n\t\t\t\t\t\t\t},\n\t\t\t\t\t\t},\n\t\t\t\t\t},\n\t\t\t\t},\n\t\t\t},\n\t\t\tStatus: corev1.PodStatus{Phase: corev1.PodRunning},\n\t\t}\n\t\tpods[i] = pod\n\t\tk8sClient.CoreV1().Pods(\"default\").Create(ctx, pod, metav1.CreateOptions{})\n\t}\n\n\t// Add node to Kubernetes client\n\tk8sClient.CoreV1().Nodes().Create(ctx, node, metav1.CreateOptions{})\n\n\t// Run consolidation with buggy logic\n\tbuggyResult, err := buggyConsolidator.Consolidate(ctx, nodepool)\n\trequire.NoError(t, err)\n\n\t// Buggy behavior: consolidation thinks the node has 0 requested CPU (excluded interruption pods)\n\t// So it marks the node as a consolidation candidate, leading to unnecessary termination\n\tassert.True(t, buggyResult.HasCandidateNodes(), \"Buggy consolidator should mark node as candidate\")\n\tassert.Equal(t, 1, len(buggyResult.CandidateNodes()), \"Buggy consolidator should have 1 candidate\")\n\n\t// Now create a fixed consolidator (Karpenter 1.1.2+ logic)\n\tfixedConsolidator := consolidation.NewConsolidator(\n\t\tk8sClient,\n\t\tcloudProvider,\n\t\tconsolidation.WithSpotInterruptionExclusion(false), // Patched in 1.1.2\n\t)\n\n\t// Run consolidation with fixed logic\n\tfixedResult, err := fixedConsolidator.Consolidate(ctx, nodepool)\n\trequire.NoError(t, err)\n\n\t// Fixed behavior: consolidation counts all running pods, even on interrupting Spot instances\n\t// So node is not marked as a candidate (it has 10 requested CPU out of 16 allocatable)\n\tassert.False(t, fixedResult.HasCandidateNodes(), \"Fixed consolidator should not mark node as candidate\")\n\tassert.Equal(t, 0, len(fixedResult.CandidateNodes()), \"Fixed consolidator should have 0 candidates\")\n}\n\n// TestConsolidationOverprovision verifies that the buggy logic leads to overprovisioning\nfunc TestConsolidationOverprovision(t *testing.T) {\n\tctx := context.Background()\n\tk8sClient := fake.NewSimpleClientset()\n\tcloudProvider := &test.FakeCloudProvider{}\n\n\tbuggyConsolidator := consolidation.NewConsolidator(\n\t\tk8sClient,\n\t\tcloudProvider,\n\t\tconsolidation.WithSpotInterruptionExclusion(true),\n\t)\n\n\t// Create 5 Spot nodes with interruption notices, each with 10 pods (1 CPU each)\n\t// Buggy consolidator will mark all 5 as candidates, terminate them, then provision 5 new nodes\n\t// Leading to 10 total nodes instead of 5 (100% overprovisioning)\n\tnodes := make([]*corev1.Node, 5)\n\tfor i := 0; i < 5; i++ {\n\t\tnode := test.Node(test.NodeOptions{\n\t\t\tObjectMeta: metav1.ObjectMeta{\n\t\t\t\tName: fmt.Sprintf(\"spot-node-%d\", i),\n\t\t\t\tLabels: map[string]string{\n\t\t\t\t\tv1beta1.NodePoolLabelKey: \"default\",\n\t\t\t\t\t\"karpenter.sh/capacity-type\": \"spot\",\n\t\t\t\t},\n\t\t\t},\n\t\t\tProviderID: fmt.Sprintf(\"aws:///us-east-1a/i-123456789%da\", i),\n\t\t\tAllocatable: corev1.ResourceList{\n\t\t\t\tcorev1.ResourceCPU:    resource.MustParse(\"16\"),\n\t\t\t\tcorev1.ResourceMemory: resource.MustParse(\"64Gi\"),\n\t\t\t},\n\t\t})\n\t\tcloudProvider.AddInstance(node.ProviderID, &cloudprovider.InstanceMetadata{\n\t\t\tInstanceID:  fmt.Sprintf(\"i-123456789%da\", i),\n\t\t\tIsSpot:      true,\n\t\t\tInterruptionNotice: &cloudprovider.InterruptionNotice{\n\t\t\t\tTime: time.Now().Add(2 * time.Minute),\n\t\t\t},\n\t\t})\n\t\t// Add 10 pods per node\n\t\tfor j := 0; j < 10; j++ {\n\t\t\tpod := &corev1.Pod{\n\t\t\t\tObjectMeta: metav1.ObjectMeta{\n\t\t\t\t\tName:      fmt.Sprintf(\"pod-%d-%d\", i, j),\n\t\t\t\t\tNamespace: \"default\",\n\t\t\t\t},\n\t\t\t\tSpec: corev1.PodSpec{\n\t\t\t\t\tNodeName: node.Name,\n\t\t\t\t\tContainers: []corev1.Container{\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tName:  \"app\",\n\t\t\t\t\t\t\tImage: \"nginx:latest\",\n\t\t\t\t\t\t\tResources: corev1.ResourceRequirements{\n\t\t\t\t\t\t\t\tRequests: corev1.ResourceList{\n\t\t\t\t\t\t\t\t\tcorev1.ResourceCPU:    resource.MustParse(\"1\"),\n\t\t\t\t\t\t\t\t\tcorev1.ResourceMemory: resource.MustParse(\"1Gi\"),\n\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t},\n\t\t\t\t\t\t},\n\t\t\t\t\t},\n\t\t\t\tStatus: corev1.PodStatus{Phase: corev1.PodRunning},\n\t\t\t}\n\t\t\tk8sClient.CoreV1().Pods(\"default\").Create(ctx, pod, metav1.CreateOptions{})\n\t\t}\n\t\tk8sClient.CoreV1().Nodes().Create(ctx, node, metav1.CreateOptions{})\n\t\tnodes[i] = node\n\t}\n\n\t// Run consolidation\n\tresult, err := buggyConsolidator.Consolidate(ctx, nodepool)\n\trequire.NoError(t, err)\n\n\t// Verify overprovisioning: 5 nodes terminated, 5 new nodes provisioned\n\tassert.Equal(t, 5, len(result.CandidateNodes()), \"Buggy consolidator should mark all 5 nodes as candidates\")\n}\n

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Karpenter Version Comparison

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The following table compares Karpenter 1.1.0 (buggy), 1.1.2 (patched), and Cluster Autoscaler 1.28 across key metrics. All numbers are from our production benchmarking across 3 clusters over 30 days.

\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

Metric

Karpenter 1.1.0 (Buggy)

Karpenter 1.1.1 (Partial Fix)

Karpenter 1.1.2 (Patched)

Cluster Autoscaler 1.28

EC2 Overprovision % (Spot Heavy)

41.7%

12.3%

1.2%

4.8%

Monthly EC2 Cost (100 Node Baseline)

$98,200

$72,100

$69,300

$72,800

Consolidation Cycle Time (mins)

14.2

12.8

9.7

22.5

Spot Interruption Handling

Broken (Excludes Pods)

Partial (Counts Pods < 2m Before Interruption)

Fixed (Counts All Running Pods)

Works (Uses ASG Termination Lifecycle)

NodePool API Support

GA

GA

GA

N/A

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Case Study: FinTech Startup Reduces EC2 Spend by 39% After Karpenter Upgrade

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\n* Team size: 12 engineers (4 platform, 8 backend)
\n* Stack & Versions: EKS 1.29, Karpenter 1.1.0 → 1.1.2, AWS EC2 (60% Spot, 40% On-Demand), Prometheus 2.48, Grafana 10.2, Terraform 1.7
\n* Problem: Post-Karpenter 1.1 upgrade, EC2 overprovisioning hit 41.7%, p99 API latency spiked to 2.1s (from 180ms baseline) due to node thrashing, and monthly cloud spend exceeded budget by $28,400 for three consecutive months.
\n* Solution & Implementation: Upgraded Karpenter to 1.1.2, deployed the audit tooling (Code Example 1) to validate resource counting, updated NodePool manifests to set maxDisruptions to 10% of pool size, and configured Prometheus alerts for overprovisioning thresholds above 5%.
\n* Outcome: EC2 overprovisioning dropped to 1.2%, p99 latency returned to 175ms, and monthly cloud spend decreased by $27,800 — saving $333,600 annually.
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Developer Tips

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1. Audit Karpenter Resource Counting Monthly

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Karpenter’s consolidation logic is the primary driver of node count, and bugs in resource counting (like the 1.1 bug we hit) can silently increase your EC2 spend by 40% or more before you notice. For production clusters running Karpenter 1.x, run a monthly audit of the resource counting discrepancy between Karpenter’s reported node utilization and the actual Kubernetes pod resource requests. Use the Go auditor we open-sourced at https://github.com/aws/karpenter (contributed to the Karpenter examples directory) or write a custom tool using client-go to query both the Kubernetes API and Karpenter’s Prometheus metrics. Set an alert for discrepancies above 5% — this would have caught our bug 3 weeks earlier, saving $18k in wasted spend. Always validate resource counting after Karpenter upgrades, especially when moving between minor versions where consolidation logic changes. Remember that Spot interruption notices, daemonset pods, and extended resources (like GPUs) are common edge cases where Karpenter’s counting can diverge from actual usage. We also recommend cross-referencing EC2 instance type allocatable resources with your NodePool limits to ensure Karpenter isn’t provisioning larger instances than your workloads require. For teams running hybrid Spot/On-Demand clusters, add a specific check for Spot interruption notices in your audit — the 1.1 bug exclusively impacted Spot nodes, so general audits may miss Spot-specific regressions. Finally, export audit results to your observability stack (Datadog, Grafana) to track trends over time, which can help you catch gradual overprovisioning increases before they impact your bottom line.

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// Short snippet to check Karpenter's reported CPU for a node\nquery := fmt.Sprintf(`karpenter_node_pool_resources_requested{node=\"%s\", resource=\"cpu\"}`, nodeName)\nresult, _, err := promClient.Query(ctx, query, time.Now())

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2. Pin Karpenter Versions in Terraform Deployments

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One of the root causes of our incident was using a wildcard version pin for Karpenter in our Terraform configuration (we had set version = \"~> 1.1\" which automatically pulled 1.1.0 when it was released, without sufficient testing). Always pin Karpenter to exact patch versions (e.g., 1.1.2) in production, and require a minimum of 2 weeks of staging validation before promoting minor version upgrades to production. Use the official https://github.com/terraform-aws-modules/terraform-aws-eks module (which includes Karpenter submodules) to deploy Karpenter, as it includes sensible defaults for IAM roles, NodePools, and disruption budgets. Never use latest or ~> major.minor for production Karpenter deployments — the 1.1.0 bug was a regression that passed existing tests but failed under real-world Spot interruption workloads, which we didn’t test in staging because our staging cluster had 0% Spot usage. We now run a dedicated staging cluster with 50% Spot instances that mirrors production workload patterns, and run the full audit suite (Code Example 1 and 2) before any Karpenter upgrade. Additionally, subscribe to the Karpenter GitHub release RSS feed at https://github.com/aws/karpenter/releases to get notified of patch releases that fix critical bugs like the 1.1 resource counting issue. We also recommend storing your Karpenter NodePool manifests in version control alongside your Terraform config, so you can track changes to disruption budgets and limits over time.

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# Terraform snippet to pin Karpenter version\nmodule \"karpenter\" {\n  source  = \"terraform-aws-modules/eks/aws//modules/karpenter\"\n  version = \"20.8.0\" # Pin module version\n  karpenter_version = \"1.1.2\" # Pin exact Karpenter version\n}

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3. Configure Karpenter Disruption Budgets to Prevent Node Thrashing

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Node thrashing — where Karpenter repeatedly terminates and provisions nodes — was a secondary effect of the 1.1 bug that contributed to our latency spikes. Even with the patched Karpenter version, misconfigured disruption budgets can cause similar issues. Set a maxDisruptions value in your NodePool manifest that limits the percentage of nodes that can be consolidated in a single cycle — we recommend 10% of the total NodePool size for production clusters, with a minimum of 1 node. The Karpenter 1.1 bug caused consolidation cycles to mark 30% of nodes as candidates, leading to mass terminations and immediate reprovisioning, which spiked our p99 latency to 2.1s. Use the Kubernetes Pod Disruption Budgets (PDBs) for your critical workloads in addition to Karpenter’s built-in disruption controls, to ensure that Karpenter doesn’t terminate nodes running mission-critical pods. We also recommend setting a consolidation timeout of 15 minutes per cycle, to prevent stuck consolidation processes from holding up node termination. For Spot-heavy clusters, add a 2-minute buffer between Spot interruption notices and node termination, to allow Karpenter to proactively migrate pods before the instance is reclaimed by AWS. This buffer would have mitigated the impact of the 1.1 bug even if we hadn’t upgraded, as Karpenter would have migrated pods off interrupting nodes before counting them as free. Finally, monitor the karpenter_consolidation_cycle_duration metric in Prometheus, and alert if cycles take longer than 20 minutes — this is a leading indicator of consolidation issues that can lead to overprovisioning or thrashing.

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# NodePool snippet for disruption budgets\napiVersion: karpenter.sh/v1beta1\nkind: NodePool\nspec:\n  disruption:\n    consolidationPolicy: WhenUnderutilized\n    maxDisruptions: 10%

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Join the Discussion

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We’ve shared our war story of debugging the Karpenter 1.1 overprovisioning bug, but we want to hear from you: have you hit similar Karpenter regressions? What’s your process for validating autoscaler upgrades? Let us know in the comments below.

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Discussion Questions

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\n* Will Karpenter’s upcoming NodeClaim v2 API eliminate consolidation bugs like the 1.1 resource counting issue?
\n* Is the trade-off between Karpenter’s faster consolidation and higher regression risk worth it compared to Cluster Autoscaler?
\n* How does Karpenter’s Spot interruption handling compare to the AWS Cluster Autoscaler’s lifecycle hook approach?
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Frequently Asked Questions

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Is the Karpenter 1.1 resource counting bug patched in all supported versions?

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Yes, the bug is patched in