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See the instant your models, tools, or hooks slow down or run up a bill, and catch a tail-latency spike before your users ever feel it. Three dedicated pages turn raw timings into p50, p95, and p99 you can read at a glance. The Models page showing a latency heat-map, a percentile band, and per-model token, cost, and context-window figures The Models page: a latency heat-map, a percentile band, and per-model tokens, estimated cost, and context-window fill.

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Stop letting averages hide your worst runs

An average latency number is comforting and useless: it smooths over the one call in fifty that stalls and pages your on-call at 2am. The Models, Tools, and Hooks pages refuse to do that. Each shares the same shape, so you learn it once:
  • A 24-bin sparkline for the trend at a glance: is this getting worse?
  • A vitals strip with p50, p95, and p99 latency, so the typical run and the tail sit side by side.
  • A latency heat-map, 24 time bins by latency buckets, that shows when the slow calls clustered.
  • A percentile band: a p50 line with p25 to p75 and p10 to p90 shaded ribbons and p99 dots, so the spread stays visible instead of averaged away.
A shared hover crosshair links the heat-map and the band, so a tail spike lines up in time across both instead of hiding behind a single mean line. Find all three pages in the observe section of your dashboard, each scoped to your organization and filterable by date range, environment, agent, and session.

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Models: see exactly what each model costs you

The Models page (shown up top) answers the two questions a bill always raises: which model, and how much. On top of the shared latency view, it adds per-model token consumption, estimated cost, and context-window fill, so runaway prompt growth and an impending compaction are visible before they surprise you. FailproofAI Observability recognizes common model IDs automatically. If a window looks wrong, or you run a private model of your own, correct it or add one under Settings, in model context windows, and the fill readouts follow.

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Tools: tell the slow apart from the broken

A tool call can be slow, or it can be quietly failing, and you want to know which one in seconds, not after digging through logs. The Tools page showing the shared latency heat-map and percentile band beside a success and failure breakdown and a tool-distribution bar The Tools page: the same heat-map and percentile band, plus a success and failure breakdown and a tool-distribution bar. Alongside the shared latency view, the Tools page adds a success and failure breakdown and a tool-distribution bar, so you see at a glance which tools you lean on most and which are eating your error budget.

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Hooks: pinpoint the exact hook and trigger

When a lifecycle hook drags a run, “hooks are slow” is not something you can act on. The Hooks page gets you to the one that matters. The Hooks page showing latency broken down by hook name and trigger event over the shared heat-map and percentile band The Hooks page: latency broken down by hook name and trigger event. Over the same latency heat-map and percentile band, the Hooks page breaks activity down by hook name and trigger event, so you land on the single hook and the single event that need attention.
  • Event stream: the live, colour-coded trail of every event.
  • Sessions: roll events up into one row per run and open its execution graph.
  • Error tracking: one triage surface for everything the dashboard paints red.
  • Dashboards: roll-up views across your fleet.