Set Up Dashboards

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Start from a template or build a custom dashboard

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Tracing Project Overview

• Trace Volume and Errors: total requests and error counts
• Latency: average span latency and distribution
• Model Performance: token count and cost per model
• Token Usage: prompt, completion, and total tokens over time
• Average Cost: cost over time (if cost tracking is set up)
• Average Eval Scores: eval score trends over time

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Token Tracking and Latency

• Total and Average Token Counts: prompt and completion tokens over time
• Number of Requests: request volume
• Average Latency: response time trends

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Add a widget

• Time Series
• Eval score
• Statistic
• Distribution
• Pivot Table
• Scatter Plot
• Text

Time Series widgets show how a metric changes over time. A good first widget: p95 latency for LLM spans, so you can see slow responses at a glance.

• Widget type: Time Series
• Metric: p95 latency (add a custom metric using APPROX_QUANTILE)
• Filter: attributes.openinference.span.kind = 'LLM'

Most production dashboards track an eval score over time — hallucination, relevance, or QA correctness. A Time Series of the Hallucination Eval Score tells you whether response quality is trending up or down.

• Widget type: Time Series
• Metric: Hallucination Eval Score — add a custom metric:

-- Hallucination rate percentage
SELECT COUNT(*) FILTER (WHERE "eval.Hallucination.label" = 'hallucinated') /
       COUNT(*) FILTER (WHERE "eval.Hallucination.label" IS NOT NULL) * 100
FROM MODEL

Statistic widgets show a single aggregate value — the current state of things. Pair them with a Time Series for a “right-now” reading alongside the trend.

• Widget type: Statistic
• Metric: error rate as a percentage (custom metric using COUNT(*) FILTER (WHERE "status_code" = 'ERROR') / COUNT(*))

Distribution widgets show the spread of values across a dimension. Group errors by graph.node.id to see which part of your agent is failing most.

• Widget type: Distribution
• Metric: count of error spans
• Group by: attributes.graph.node.id

Pivot Table widgets compare a metric across categories in a table. Group by span kind to see how latency breaks down across LLM, Retriever, Tool, and Agent spans at a glance.

• Widget type: Pivot Table
• Group By Dimension: attributes.openinference.span.kind
• Value: latency_ms with Metric Avg, labeled “Average Latency”

Scatter Plot widgets show how two attributes correlate. Plot token count against latency for LLM spans to see whether longer prompts actually take longer to respond.

• Widget type: Scatter Plot
• X-Axis: latency_ms
• Y-Axis: attributes.llm.token_count.total
• Filter: attributes.openinference.span.kind = 'LLM'

Text widgets add context, section headers, or notes alongside your charts. Use them to explain what a dashboard tracks, link to runbooks, or separate dashboard sections.

• Widget type: Text
• Content: Markdown — headings, bold, lists, and inline code all render

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Using your dashboards

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Global filters and time range

• Time range — presets (Last 15 Min to Last 6 Months), custom range, or typed value. Timezone persists across sessions.
• Metadata filters — scope by attributes like model name, environment, or custom metadata.

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Per-widget filters

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View traces behind a widget

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Interact with widgets

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Export and share

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Copy a dashboard

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Next step