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

# Analytics

> Monitor evaluation health over time with Netra's analytics dashboard. Track pass rates, normalized scores, evaluator performance, and costs across test runs.

The Analytics tab gives you a time-filtered, at-a-glance view of how your [evaluation](/Evaluations/text-evaluations/Datasets) is performing across all test runs. Open any evaluation and click the **Analytics** tab to access it.

<img src="https://mintcdn.com/netra/rgn_MeP0fP5E_Fpu/images/analytics.png?fit=max&auto=format&n=rgn_MeP0fP5E_Fpu&q=85&s=528f149b920cdda4dd0950565f69f69d" alt="Evaluation Analytics tab" width="1687" height="1017" data-path="images/analytics.png" />

## Overall Health

The top of the Analytics page displays a summary card with four key metrics:

| Metric               | Description                                                                                            |
| -------------------- | ------------------------------------------------------------------------------------------------------ |
| **Health**           | Boolean pass/fail indicator — green when the latest run meets the pass threshold, red when it does not |
| **Normalized Score** | Average score across all evaluators, normalized to a 0–1 scale                                         |
| **Evaluators**       | Number of evaluators attached to this evaluation                                                       |
| **Eval Cost**        | Total token and API cost accumulated across all test runs                                              |

## Average Evaluation Score Across Time

A line graph plots the normalized evaluation score over time. Each data point represents one test run. Use this chart to:

* **Spot trends** — a declining line indicates gradual quality degradation
* **Identify regressions** — a sharp drop after a specific run pinpoints the change that introduced the issue
* **Validate improvements** — confirm that score increases after prompt or model updates

The graph supports time-range filtering to focus on specific periods.

## Evaluator Performance

Below the score graph, an evaluator performance card breaks down results per evaluator:

| Metric         | Description                                                                         |
| -------------- | ----------------------------------------------------------------------------------- |
| **Pass Rate**  | Percentage of test cases that passed this evaluator across the selected time period |
| **Fail Rate**  | Percentage of test cases that failed                                                |
| **Total Cost** | Token and API cost attributable to this evaluator across runs                       |

Each evaluator is listed as a separate row, letting you quickly identify which evaluator contributes most to failures or costs.

## How to Use Analytics

### Track Quality Over Time

1. Run the same evaluation after each deployment or model change
2. Open the **Analytics** tab to see the score trend
3. Investigate any dips or regressions in the graph

### Identify Weak Evaluators

1. Review the **Evaluator Performance** card
2. Focus on evaluators with the lowest pass rates
3. Tune those evaluators or investigate the underlying AI behavior

### Monitor Costs

1. Check the **Eval Cost** in the overall health card
2. Review per-evaluator costs to identify expensive evaluators
3. Optimize prompt size or evaluator frequency if costs grow unexpectedly

## Related

* [Test Runs](/Evaluations/TestRuns) - View detailed results for individual test runs
* [Evaluation Overview](/Evaluations/Evaluation-overview) - Understand the evaluation framework
* [Evaluators](/Evaluators/text-evaluators) - Configure scoring logic and criteria