FinOps Dashboard Setup
Build a cloud cost visibility dashboard with the right FinOps tooling. Compare Kubecost, CloudHealth, and CAST AI — then set up Grafana dashboards to track the KPIs that connect cloud spend to business value.
FinOps Tool Comparison
| Feature | Kubecost | CloudHealth | CAST AI |
|---|---|---|---|
| Primary focus | K8s cost visibility | Multi-cloud governance | K8s autonomous optimization |
| Auto-optimization | Recommendations only | RI purchase automation | ✓ Fully autonomous |
| Multi-cloud | K8s on any cloud | ✓ AWS + GCP + Azure | AWS + GCP + Azure K8s |
| Free tier | Free (15-day retention) | No free tier | Free up to $10K/month |
| Typical savings | Visibility-driven | 15–30% | 40–60% K8s costs |
| Target org size | Any | Enterprise | K8s-heavy teams |
| Pricing model | Free / per cluster | % of cloud spend | % of savings |
Grafana Dashboard — AWS Cost via CloudWatch
// Grafana panel JSON snippet — monthly cost by service
// Add as a new panel in your Grafana dashboard JSON
{
"title": "AWS Monthly Cost by Service",
"type": "barchart",
"datasource": "CloudWatch",
"targets": [
{
"queryType": "timeSeriesQuery",
"namespace": "AWS/Billing",
"metricName": "EstimatedCharges",
"dimensions": {
"ServiceName": ["Amazon EC2", "Amazon RDS", "AWS Lambda",
"Amazon S3", "Amazon EKS"]
},
"statistics": ["Maximum"],
"period": "2592000"
}
],
"fieldConfig": {
"defaults": {
"unit": "currencyUSD",
"thresholds": {
"steps": [
{"color": "green", "value": null},
{"color": "yellow", "value": 1000},
{"color": "red", "value": 5000}
]
}
}
}
} GCP Billing Export to BigQuery + Grafana
-- Enable billing export: GCP Console → Billing → Budget & alerts → Billing export -- Then query in Grafana using BigQuery datasource -- Cost by service this month SELECT service.description AS service, ROUND(SUM(cost), 2) AS total_cost, currency FROM `project.dataset.gcp_billing_export_v1_XXXXXX` WHERE DATE(_PARTITIONTIME) >= DATE_TRUNC(CURRENT_DATE(), MONTH) GROUP BY service.description, currency ORDER BY total_cost DESC LIMIT 20; -- Daily cost trend by team label SELECT DATE(usage_start_time) AS date, labels.value AS team, ROUND(SUM(cost), 2) AS daily_cost FROM `project.dataset.gcp_billing_export_v1_XXXXXX`, UNNEST(labels) AS labels WHERE labels.key = 'team' AND DATE(usage_start_time) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) GROUP BY date, team ORDER BY date, daily_cost DESC;
Key FinOps KPIs to Track
| KPI | Target | How to Calculate |
|---|---|---|
| Savings Plan utilization | > 80% | Used commitment / purchased commitment |
| Waste percentage | < 5% | Idle resource cost / total cloud spend |
| Tag coverage | > 95% | Tagged resources / total resources |
| Cost per unit | Trend ↓ | Total cloud cost / active users or requests |
| Budget variance | < ±10% | (Actual - Budget) / Budget × 100 |
| Env cost ratio | Prod > 70% | Production cost / total cluster cost |
Frequently Asked Questions
What is FinOps and why does it need dedicated tooling?▼
FinOps (Financial Operations) is the practice of bringing financial accountability to cloud spending through cross-functional collaboration between engineering, finance, and business teams. Cloud billing is fundamentally different from traditional IT costs — it's variable, granular, and accrues in real time. Without dedicated tooling, engineers can't see cost impact of their infrastructure decisions, and finance can't attribute spend to business outcomes. FinOps tools provide chargeback/showback reporting, anomaly detection, optimization recommendations, and the shared language between technical and financial teams.
What KPIs should a FinOps dashboard track?▼
The most important FinOps KPIs: (1) Cost per unit of business value — cost per API call, cost per user, cost per transaction. This ties cloud spend to revenue. (2) Savings Plan / Reserved Instance utilization — should be above 80%. (3) Waste percentage — idle resources as % of total spend, target below 5%. (4) Coverage rate — what % of eligible spend is covered by commitments. (5) Variance to budget — actual vs planned, with trend. (6) Environment cost breakdown — what % is production vs staging vs dev. (7) Tag coverage rate — what % of resources are properly tagged. These seven KPIs tell you if your FinOps program is working.
How does CAST AI differ from Kubecost and CloudHealth?▼
CAST AI is an autonomous Kubernetes optimization platform — it not only shows costs but automatically rightsizes nodes, switches workloads to Spot instances, and scales node groups for you. It's an active optimizer, not just a reporting tool. Kubecost is primarily a cost visibility and allocation tool for Kubernetes that shows recommendations but requires manual action. CloudHealth (VMware Aria) is a broad multi-cloud FinOps platform covering governance, tagging enforcement, reserved instance management, and reporting across AWS/GCP/Azure — it's enterprise-grade with a corresponding price tag. Choose CAST AI if you want automated K8s savings, Kubecost for K8s visibility, CloudHealth for enterprise multi-cloud governance.
Can I build a FinOps dashboard with open-source tools only?▼
Yes — a solid open-source FinOps stack uses: OpenCost for Kubernetes cost allocation, AWS Cost Explorer API or GCP Billing Export to BigQuery for cloud costs, Prometheus + node-exporter for resource utilization metrics, and Grafana for dashboards. Export cloud costs to an S3 bucket or BigQuery table, use Grafana's AWS CloudWatch or BigQuery datasource to query them, and combine with OpenCost's Prometheus metrics. This gives you cost by namespace, service, team, and environment in Grafana without any SaaS cost. The effort is in the setup and ongoing maintenance — roughly 1-2 days initially and a few hours per month.
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