---
title: "Use built-in dashboards | Grafana Cloud documentation"
description: "Monitor agent activity, performance, cost, and quality using the AI Observability analytics dashboards."
---

# Use built-in dashboards

AI Observability includes pre-built analytics dashboards that visualize agent activity, performance, cost, and quality. The dashboards use Prometheus metrics and AI Observability query APIs to surface actionable insights.

## Access dashboards

Navigate to **Analytics** in the AI Observability plugin. The dashboards are organized into these areas:

- **Activity**: generation counts, conversation counts, and active agents over time.
- **Performance**: latency distributions, time to first token, and error rates.
- **Tokens and cost**: token usage by model and provider, cost breakdown, and cache efficiency.
- **Tools**: tool call frequency, tool execution duration, tool error rates, and usage percentage per tool.
- **Quality**: evaluation scores, score distributions, and quality trends.

## Identify performance issues

Use the performance dashboard to spot problems:

- **High latency**: filter by agent or model to find slow generations. Drill into traces for specific conversations to identify bottlenecks.
- **Error spikes**: the error rate panel shows failures over time. Click through to conversations with errors to inspect the `call_error` payload.
- **Slow time to first token**: for streaming agents, the TTFT panel reveals which models or prompts have poor streaming performance.

## Optimize costs

The tokens and cost dashboard helps you find optimization opportunities:

- **Cost by model**: compare cost across models and providers. Consider switching expensive calls to cheaper models where quality is acceptable.
- **Cache efficiency**: the cache read ratio shows how effectively prompt caching reduces token usage. Low cache rates may indicate prompts that change too frequently.
- **Token usage trends**: spot unexpected increases in token usage that may indicate prompt regression or unnecessary verbosity.

## Track quality

The quality dashboard visualizes evaluation scores alongside operational metrics:

- **Score trends**: monitor if quality improves or degrades after agent version changes.
- **Score distributions**: identify if responses cluster around high or low scores.
- **Correlation**: compare quality scores with latency and cost to find the right balance.

## Use Prometheus metrics directly

If you need custom dashboards, query the AI Observability OpenTelemetry metrics in Prometheus:

Expand table

| Metric                                   | Description                                             |
|------------------------------------------|---------------------------------------------------------|
| `gen_ai_client_operation_duration`       | LLM call duration histogram.                            |
| `gen_ai_client_token_usage`              | Token consumption histogram.                            |
| `gen_ai_client_time_to_first_token`      | Streaming TTFT histogram.                               |
| `gen_ai_client_tool_calls_per_operation` | Tool calls per generation.                              |
| `sigil_build_info`                       | Build version info with `revision` and `branch` labels. |

If you enable evaluation metrics push (`SIGIL_EVAL_METRICS_PUSH_ENDPOINT`), per-tenant evaluation metrics are also available in Prometheus for custom dashboards and alerting.

## Set up alerts

Create Grafana alerts on AI Observability metrics to proactively catch issues:

- Alert on error rate exceeding a threshold.
- Alert on p95 latency exceeding SLO targets.
- Alert on cost per day exceeding budget.
- Alert on evaluation score drops below a quality threshold.

Configure alerts in Grafana using the standard alerting workflow with the Prometheus data source.

## Next steps

- [Optimize cost and performance](/docs/grafana-cloud/machine-learning/ai-observability/guides/cost-optimization)
- [Browse conversations](/docs/grafana-cloud/machine-learning/ai-observability/guides/conversations)