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gpu-cost-optimizer

Analyze and reduce GPU Kubernetes spend through allocation visibility, rightsizing, and spot capacity policies.

Updated Jul 16, 2026
Search Tags:#kubernetes#cost optimization#gpu#finops#kubecost#chargeback#opencost
One-click install
npx skills add https://github.com/Cloud-Byte-Consulting/plugins --skill gpu-cost-optimizer-cloud-byte-consulting
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gpu-cost-optimizer
Source: https://github.com/Cloud-Byte-Consulting/plugins/tree/main/gpu-research-platform/skills/gpu-cost-optimizer
Command: npx skills add https://github.com/Cloud-Byte-Consulting/plugins --skill gpu-cost-optimizer-cloud-byte-consulting

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? GPU cloud bills grow without visibility into which researcher, team, or project is consuming capacity, and idle or oversized GPU allocations silently burn budget. This Skill establishes per-namespace and per-label cost allocation, identifies waste, and drives a recurring cost-review workflow for research Kubernetes clusters. ## Core Features & Use Cases - Cost allocation and chargeback: Configure Kubecost or OpenCost with custom per-GPU-hour pricing and a label standard (team, project, owner, env) to produce per-project chargeback reports. - Waste detection and rightsizing: Find idle-allocated GPUs, oversized requests, abandoned workloads, and unclaimed volumes using DCGM utilization metrics and Goldilocks VPA recommendations. - Spot and scale-to-zero policies: Apply decision rules for spot/preemptible training with checkpointing, reserved capacity for known training windows, and scale-to-zero for idle notebooks and dev environments. - Use Case: A platform team facing a rising Lambda GPU bill installs OpenCost with custom pricing, enforces namespace labels, lists the top five idle GPU workloads, and publishes a monthly cost-review report with named owners for each savings action. ## Quick Start Use the gpu-cost-optimizer skill to analyze our cluster's GPU spend by namespace and produce a prioritized savings plan with a monthly cost-review report.

Frequently Asked Questions about gpu-cost-optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up GPU cost chargeback per team on Kubernetes?▼

Deploy Kubecost or OpenCost via Helm, then enforce a label standard (team, project, owner, env) on every namespace at creation. Allocation by label converts the cluster bill into per-project chargeback; unlabeled workloads are charged to the platform team as an incentive to fix hygiene.

Kubecost vs OpenCost for GPU cost visibility?▼

OpenCost is the open-source CNCF project providing allocation by namespace, label, and pod. Kubecost is commercial, built on OpenCost, and adds savings insights like rightsizing recommendations, abandoned workload detection, unclaimed PV identification, and deeper cloud-bill integration.

Should training jobs run on spot or preemptible GPU instances?▼

Spot suits training and fine-tuning jobs that checkpoint regularly, with the checkpoint interval sized so maximum lost work equals one interval and auto-resume on reschedule. Never use spot for workloads without a tested resume path or for online inference without on-demand fallback capacity.

How do I find idle GPUs wasting money in my cluster?▼

Compare Kubernetes allocation against DCGM telemetry: pods holding a GPU with DCGM_FI_DEV_GPU_UTIL near zero are idle-allocated. Also hunt for notebooks running overnight, abandoned workloads with no traffic, and dev environments alive on weekends, then apply scale-to-zero or culling policies.

Does Kubecost work with non-hyperscaler GPU clouds like Lambda?▼

Yes, but cloud-bill integrations like AWS CUR do not apply directly. Use custom pricing by feeding the provider's published per-GPU-hour rates into the OpenCost or Kubecost pricing config, and ask the provider whether a billing API or export exists for reconciliation.

Why is my GPU chargeback wrong after enabling time-slicing?▼

Under time-slicing, multiple virtual GPUs share one physical card, so charging full card price per virtual GPU overstates costs by the replica count. Update the pricing config to allocate by fraction of the physical device the same day sharing configuration changes.

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