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Explore LLM Telemetry in Cribl Search ​

Use Cribl Search to explore, investigate, and visualize LLM telemetry.

When LLM telemetry lands in Cribl Search, you can:

  • Run ad hoc investigations (errors, latency, usage spikes).
  • Correlate LLM spans with infrastructure or security telemetry.
  • Build dashboards and scheduled searches for usage, cost, and performance.

Prerequisites ​

Search LLM Telemetry ​

Store your LLM telemetry in a Dataset, then query it directly in Cribl Search with KQL for fast investigation and analysis.

  • Create a Dataset and use Dataset rules to route your LLM telemetry into it. Search Datasets are hosted in lakehouse engines and are optimized for fast, schema-aware search and AI workflows.
  • Use that Dataset in queries to filter by service, model, or environment; slice by token usage or cost; and narrow on error or status fields for failing requests.
Cribl Search in-place query that runs against OpenAI telemetry, filters those events, and then groups them by event type and finish reason
Cribl Search in-place query that runs against OpenAI telemetry, filters those events, and then groups them by event type and finish reason

Investigate Incidents and Performance Regressions ​

During an incident (for example, elevated LLM error rates or latency):

  • Start from an LLM telemetry Dataset in Search.
  • Use filters and aggregation operators (such as summarize or timestats) to break down failures by model or deployment, prompt type or application feature, and tenant, user, or region.
  • Correlate with other Datasets (for example, infrastructure or API gateway logs) using join or related operators to see upstream and downstream symptoms together.

Build Dashboards for LLM Usage and Cost ​

Use Cribl Search Dashboards to visualize:

  • Requests over time by model or application.
  • Token usage and derived cost over time.
  • Error rates by environment or tenant.

Add charts for P95/P99 latency, token count distributions, and traffic share per model or feature path.

Cribl Search dashboard showing a bar chart of OpenAI event types and their event counts.
Cribl Search dashboard showing a bar chart of OpenAI event types and their event counts.

Automate Checks with Scheduled Searches and Notifications ​

Turn critical queries into Scheduled Searches–for example, alert when estimated LLM cost exceeds a threshold over the last hour, or when error rate for a model exceeds a limit over 10 minutes. Attach Notifications for email, Slack, SNS, or other targets.

Query for monitoring LLM model error rate that can be saved as a Scheduled Search with Notifications
Query for monitoring LLM model error rate that can be saved as a Scheduled Search with Notifications