{
  "id": 4260372,
  "title": "Unlocking workload rightsizing visibility on GKE: How VPA decision logs bring observability to autoscaling",
  "url": "https://urgent.news/2026/08/29/unlocking-workload-rightsizing-visibility-on-gke-how-vpa-decision",
  "topic": "tech",
  "section": "Tech",
  "published": "2026-08-29T18:57:42.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/googlecloud/unlocking-workload-rightsizing-visibility-on-gke-how-vpa-decision-logs-bring-observability-to-17md"
  },
  "original_language": "en",
  "account": "In the article \"Unlocking workload rightsizing visibility on GKE: How VPA decision logs bring observability to autoscaling,\" the GKE team introduces a new feature called VerticalPodAutoscaler (VPA) Logs. This feature aims to provide better visibility into the resource management decisions made by the VPA in Kubernetes clusters.\n\nTraditionally, VPA decisions have been opaque, making it hard for platform engineers to understand why certain Pods were evicted or resized. This lack of transparency often led to unexpected restarts and performance issues. The new VPA Logs solution addresses this issue by streaming structured VPA decision events directly into Cloud Logging.\n\nVPA Logs capture detailed information about each decision made by the VPA controller. Each log entry is a structured JSON payload containing metadata about the targeted workload, the evaluation state, and the calculated resource bounds. The controller categorizes decision logs into four main operations: Update recommendation, Evict Pod, Apply recommendation on eviction, and Apply recommendation in place.\n\nBy enabling VPA decision logs, GKE users can now gain complete visibility across horizontal and vertical scaling dimensions. This enables better troubleshooting of scaling actions and helps build more reliable autonomous workload management. The logs also provide valuable insights into the confidence level of the recommendation engine, with a confidence field indicating whether the recommender has processed fewer than 10 metric samples (LOW) or 10 or more metric samples (HIGH).",
  "summary": "Automating resource management in Kubernetes has always required a high degree of trust. When you hand over CPU and memory sizing to the Vertical Pod Autoscaler (VPA), you expect it to rightsize containers efficiently without introducing unexpected restarts or performance regressions. Yet for many platform engineers, running VPA in production has felt like operating a black box. Until recently,…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}