> ## Documentation Index
> Fetch the complete documentation index at: https://docs.usechamber.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Cost Explorer

> Analyze, forecast, and optimize GPU spend across your organization

Know exactly where your GPU dollars go. The Cost Explorer gives you full visibility into spend across teams, clusters, GPU types, and workload classes — with built-in forecasting, waste detection, and optimization recommendations to help you get more out of every GPU-hour.

Navigate to **Cost Explorer** in the Chamber dashboard sidebar to get started.

## At a Glance

Cost Explorer is built to answer the questions every GPU infrastructure team asks:

* **How much are we spending?** Total and per-team GPU costs with trend analysis
* **Where is the money going?** Breakdown by team, user, cluster, GPU type, and workload class
* **What will we spend next?** Month-end forecasts with confidence intervals
* **What are we wasting?** Idle capacity, failed workload costs, and underutilized resources
* **How do we optimize?** Actionable recommendations for spot instances, rightsizing, and failure reduction

## Key Metrics

Four headline numbers are displayed at the top of every Cost Explorer view:

| Metric                  | Description                                                       |
| ----------------------- | ----------------------------------------------------------------- |
| **Total Spend**         | GPU cost from completed workloads in the selected time range      |
| **Month to Date**       | Cumulative GPU spend so far this calendar month                   |
| **Projected Month-End** | Where you'll land by end of month, based on current trajectory    |
| **Trend vs. Prior**     | Percentage change comparing the recent period to the prior period |

## Filtering and Grouping

### Filters

Slice your data by any combination of:

* **Team** — One or more teams
* **Cluster** — Specific capacity pools
* **GPU Type** — Hardware type (A100, H100, etc.)
* **Workload Class** — Type of workload (training, inference, interactive, etc.)

Filters apply across every chart, metric, and table on the page. Active filters appear as removable pills below the filter bar.

### Grouping

Change how the spend chart and cost breakdown are organized:

* Team / User / Cluster / GPU Type / Workload Class

Switch grouping at any time to see costs from a completely different angle.

### Time Range

Pick from presets or define your own window:

* Last 7 / 30 / 90 days
* This month / Last month
* **Custom range** with a calendar picker

<Tip>
  Filters and time range are saved in the URL — bookmark or share a specific cost view with your team.
</Tip>

## Spend Over Time

A time-series chart shows how your spend trends across the selected period:

* **Granularity** — Toggle between daily, weekly, or monthly aggregation
* **Stacked series** — Top contributors are shown individually; smaller ones grouped as "Other"
* **Forecast overlay** — Project costs forward with a shaded 95% confidence band

### Forecasting

Turn on the forecast overlay to see where spend is heading:

* Uses linear regression fitted to your daily historical data
* Shows a projected line with upper and lower confidence bounds
* Defaults to an end-of-month projection, but you can pick any date up to 365 days ahead
* **Trend detection** automatically classifies your spend as increasing (over 10% growth), decreasing (over 10% decline), or stable

## Capacity Utilization

This section compares what you're paying for (provisioned GPU capacity) against what's actually being used by workloads:

| Metric                  | Description                                           |
| ----------------------- | ----------------------------------------------------- |
| **Total Capacity Cost** | Cost of all provisioned GPUs across selected clusters |
| **Used Cost**           | Cost attributable to completed workloads              |
| **Wasted Cost**         | Idle capacity — provisioned but sitting unused        |
| **Utilization %**       | Percentage of provisioned capacity actively used      |

Utilization is color-coded so you can spot problems at a glance:

* **Green** (70%+) — Healthy
* **Orange** (40-70%) — Worth reviewing
* **Red** (under 40%) — Significant optimization opportunity

<Note>
  Capacity metrics come from cluster telemetry. Team and Workload Class filters only affect the "Used Cost" number, since provisioned capacity isn't tied to specific workloads.
</Note>

## Cost Breakdown

A donut chart shows the proportional distribution of spend by your selected dimension. Items contributing less than 1% are grouped as "Other" — hover to expand the full list.

## Cost Events Table

Dig into the details with a full table of individual cost events:

| Column         | Description                   |
| -------------- | ----------------------------- |
| Date           | When the cost was incurred    |
| Team           | Team name                     |
| User           | Who submitted the workload    |
| Cluster        | Capacity pool                 |
| GPU Type       | Hardware type                 |
| Workload Class | Type of workload              |
| Hours          | GPU instance-hours consumed   |
| Rate           | Hourly rate for this GPU type |
| Cost           | Total cost for this event     |

### Pagination and Export

* Navigate results with cursor-based pagination
* Set your page size: 10, 25, 50, or 100 rows
* **Export to CSV** to pull data into spreadsheets or external tools (up to 100K rows)

## GPU Rate Configuration

Cost calculations rely on per-GPU-type hourly rates. Org Admins can set these up:

1. Click the **settings icon** in the Cost Explorer header
2. Set the hourly rate for each GPU type in your organization
3. Rates apply retroactively to all cost calculations

## Waste Analysis

Chamber automatically surfaces wasted GPU spend from three sources:

| Waste Category               | Description                                                                          |
| ---------------------------- | ------------------------------------------------------------------------------------ |
| **Failed Workloads**         | GPU time consumed by workloads that ultimately failed                                |
| **Preempted Workloads**      | GPU time from workloads preempted before completion                                  |
| **Short Runtime, High Cost** | Workloads under 5 minutes that cost more than \$1 — often a sign of misconfiguration |

## Optimization Recommendations

Chamber analyzes your usage patterns and surfaces actionable next steps:

<CardGroup cols={2}>
  <Card title="Spot Instance Savings" icon="piggy-bank">
    Identifies workloads that could run on spot or preemptible instances, with estimated monthly savings.
  </Card>

  <Card title="Failure Reduction" icon="triangle-exclamation">
    Flags teams or GPU types with failure rates above 20% and calculates the cost impact.
  </Card>
</CardGroup>