Managing Kubecost

Core Helper Functions

#!/bin/bash

kubecost_api() {
    local endpoint="$1"
    curl -s "http://${KUBECOST_HOST:-localhost:9090}/model/${endpoint}"
}

kubecost_allocation() {
    local window="${1:-1d}"
    local aggregate="${2:-namespace}"
    kubecost_api "allocation?window=${window}&aggregate=${aggregate}"
}

kubecost_savings() {
    kubecost_api "savings"
}

Output Rules

• TOKEN EFFICIENCY: Target ≤50 lines per output
• Use jq filtering on API responses to extract key cost fields
• Round cost values to 2 decimal places for readability

Common Operations

Namespace Cost Breakdown

#!/bin/bash
WINDOW="${1:-7d}"

echo "=== Namespace Costs (window: $WINDOW) ==="
kubecost_api "allocation?window=${WINDOW}&aggregate=namespace" | jq -r '
    .data[0] | to_entries[] |
    "\(.key)\t$\(.value.totalCost | . * 100 | round / 100)\tCPU: \(.value.cpuCoreRequestAverage | . * 100 | round / 100) cores\tRAM: \(.value.ramByteRequestAverage / 1073741824 | . * 100 | round / 100) GiB"
' | sort -t'$' -k2 -rn | column -t | head -20

echo ""
echo "=== Total Cluster Cost ==="
kubecost_api "allocation?window=${WINDOW}&aggregate=cluster" | jq -r '
    .data[0] | to_entries[] |
    "\(.key)\tTotal: $\(.value.totalCost | . * 100 | round / 100)"
' | column -t

Workload Efficiency Scoring

#!/bin/bash
NAMESPACE="${1:-}"
WINDOW="${2:-2d}"

FILTER=""
[ -n "$NAMESPACE" ] && FILTER="&filterNamespaces=${NAMESPACE}"

echo "=== Workload Efficiency (window: $WINDOW) ==="
kubecost_api "allocation?window=${WINDOW}&aggregate=controller${FILTER}" | jq -r '
    .data[0] | to_entries[] |
    select(.value.totalCost > 0.01) |
    "\(.key)\tCost: $\(.value.totalCost | . * 100 | round / 100)\tCPU Eff: \((.value.cpuEfficiency // 0) * 100 | round)%\tRAM Eff: \((.value.ramEfficiency // 0) * 100 | round)%\tTotal Eff: \((.value.totalEfficiency // 0) * 100 | round)%"
' | sort -t'$' -k2 -rn | column -t | head -20

Savings Recommendations

#!/bin/bash
echo "=== Right-Sizing Recommendations ==="
kubecost_api "savings/requestSizing?window=48h&targetCPUUtilization=0.65&targetRAMUtilization=0.70" | jq -r '
    .[] | select(.annualSavings > 10) |
    "\(.namespace)/\(.controllerName)\tSavings: $\(.annualSavings | round)/yr\tCPU: \(.currentCPURequest)→\(.recommendedCPURequest)\tRAM: \(.currentRAMRequest)→\(.recommendedRAMRequest)"
' | sort -t'$' -k2 -rn | head -15

echo ""
echo "=== Abandoned Workloads ==="
kubecost_api "savings/abandonedWorkloads?window=7d" | jq -r '
    .[] | "\(.namespace)/\(.name)\tLast Active: \(.lastSeen[0:16])\tCost: $\(.monthlyCost | . * 100 | round / 100)/mo"
' | head -10

Cost Trend Analysis

#!/bin/bash
echo "=== Daily Cost Trend (last 7 days) ==="
kubecost_api "allocation?window=7d&aggregate=cluster&step=1d" | jq -r '
    .data[] | to_entries[] |
    "\(.value.start[0:10])\t$\(.value.totalCost | . * 100 | round / 100)"
' | column -t

echo ""
echo "=== Cost by Label (team) ==="
kubecost_api "allocation?window=7d&aggregate=label:team" | jq -r '
    .data[0] | to_entries[] |
    select(.key != "__unallocated__") |
    "\(.key)\t$\(.value.totalCost | . * 100 | round / 100)"
' | sort -t'$' -k2 -rn | column -t | head -15

Asset Cost Analysis

#!/bin/bash
echo "=== Asset Costs by Type (last 7d) ==="
kubecost_api "assets?window=7d&aggregate=type" | jq -r '
    .data[0] | to_entries[] |
    "\(.key)\t$\(.value.totalCost | . * 100 | round / 100)"
' | sort -t'$' -k2 -rn | column -t

echo ""
echo "=== Node Costs ==="
kubecost_api "assets?window=7d&aggregate=node" | jq -r '
    .data[0] | to_entries[] |
    "\(.key)\t$\(.value.totalCost | . * 100 | round / 100)\tType: \(.value.properties.instanceType // "unknown")"
' | sort -t'$' -k2 -rn | column -t | head -15

Safety Rules

• Read-only by default: Kubecost is primarily observational -- no destructive operations
• Window validation: Ensure window parameter matches data retention period
• Cost accuracy: Kubecost estimates may differ from cloud billing -- use for relative comparison
• Efficiency thresholds: Do not auto-resize based solely on efficiency scores -- validate with team

Output Format

Present results as a structured report:

Managing Kubecost Report
════════════════════════
Resources discovered: [count]

Resource       Status    Key Metric    Issues
──────────────────────────────────────────────
[name]         [ok/warn] [value]       [findings]

Summary: [total] resources | [ok] healthy | [warn] warnings | [crit] critical
Action Items: [list of prioritized findings]

Target ≤50 lines of output. Use tables for multi-resource comparisons.

Anti-Hallucination Rules

1. NEVER assume resource names — always discover via CLI/API in Phase 1 before referencing in Phase 2.
2. NEVER fabricate metric names or dimensions — verify against the service documentation or --help output.
3. NEVER mix CLI commands between service versions — confirm which version/API you are targeting.
4. ALWAYS use the discovery → verify → analyze chain — every resource referenced must have been discovered first.
5. ALWAYS handle empty results gracefully — an empty response is valid data, not an error to retry.

Counter-Rationalizations

Common Pitfalls

• Data lag: Kubecost needs 24-48h of data before efficiency scores stabilize
• Shared costs: Cluster overhead (system pods, control plane) may not be allocated to namespaces
• Idle costs: Unallocated resources appear as "idle" -- this is not waste, it is capacity headroom
• Network costs: Network egress costs are often estimated, not precise
• Multi-cluster: Ensure the correct cluster context when querying multi-cluster Kubecost
• Prometheus dependency: Kubecost relies on Prometheus/Thanos -- if metrics are missing, costs will be incomplete