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The Failures page groups traces by failure signature, surfaces regressions, and lets you configure alerts so you find out about incidents before your users do.

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Cluster list

Each row is a failure cluster — a group of traces that share the same normalised error signature (error type, message skeleton, and top contributing span). Columns:
  • Signature — human-readable cluster label (e.g. timeout in llm:openai.chat.completions).
  • Count — traces in the cluster within the selected window.
  • Trend — sparkline of cluster volume, last 24 h.
  • First / last seen — helpful for spotting regressions tied to a deploy.
  • Impact — unique sessions and unique users affected.
  • Status — new, acknowledged, resolved.
Filter by project, environment, model, status, and time range. The URL encodes the full filter state, so share links with teammates.

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Cluster detail

Click any cluster to open the root-cause view.
  • Top contributing spans — the 3–5 span names most frequently marked as the failure origin across traces in the cluster, with counts and average latency.
  • Representative error messages — de-duplicated error strings with per-variant counts. Click to pivot to a matching trace.
  • Linked traces — paginated list of every trace in the cluster; click through to the trace detail and span waterfall.
  • Feedback overlay — any negative user feedback attached to cluster traces shows up here for context.
Use the Acknowledge and Resolve buttons to change cluster status. Resolved clusters are hidden by default in the list view; filter status = resolved to see them.

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Alert rules

From Failures → Alert rules, create rules that fire on cluster or failure-rate conditions. Rule fields:
  • Name — shown on the alert payload.
  • Trigger — one of:
    • failure_rate > threshold over a rolling window (e.g. > 2% over 5 minutes)
    • cluster_count > threshold for a new or existing cluster
    • cluster.first_seen — fires the first time a signature appears
  • Scope — project, environment, model, or metadata filter.
  • Channel — webhook URL (JSON payload) or email recipients.
  • Cooldown — suppress repeated fires within the interval (default 15 minutes).
Rules can be put in dry-run mode — they evaluate and appear in the alert history but do not send notifications. Use this when tuning thresholds.

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Common workflows

  • New deploy monitoring. Filter to first_seen > deploy_time to see clusters introduced by the latest release.
  • Triage the weekly on-call. Sort clusters by Impact desc, work top-down.
  • Close the loop with ownership. Add a metadata filter (metadata.team = "payments") to alert rules so only the right team gets paged.