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If your product calls a language model, that call is a dependency like any other — except that it is slower, more expensive per request, and fails in ways an HTTP status code does not describe. LLM observability tracks those calls specifically.
This is about your AI workloads. For the AI features aiAxonIQ runs over your telemetry — anomaly detection, forecasting, natural-language query — see AI features. The two are frequently confused.

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What is tracked

The overview aggregates these; the request list is where you find an individual call.

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Why it needs its own view

A model call breaks the assumptions ordinary request monitoring makes.
  • Cost varies per request by one to two orders of magnitude, depending on prompt and response length. Request count tells you nothing about spend.
  • Latency is measured in seconds, and a p99 of 30 seconds may be normal rather than an incident.
  • A 200 can be a failure. A refusal, a truncation or an unusable answer is a successful HTTP response.
Rate, errors and duration are still useful. They are just not sufficient.

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Retention

Individual request records are kept for 30 days; rolled-up aggregates for 90 days. So per-request forensics has a one-month window, while cost and volume trends go back a quarter.

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Prompt analysis

Prompts can be analysed for structure and quality. Useful when a change in prompt template lines up with a change in cost or error rate — a connection that is otherwise very hard to see, because the prompt is usually the thing nobody versions.

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What it does not capture

Answer quality is not measured. Nothing here tells you whether a response was correct, useful or hallucinated. Those need evaluation against your own criteria, which is a different discipline from observability.What this gives you is everything around the answer: how long, how much, how often it failed, and which model.
Be careful what ends up in a prompt record. Prompts routinely contain customer data — that is frequently the point of them. Telemetry is append-only and retained for its full window, with no surgical delete.Redact before it leaves your network if your prompts carry personal data. See Collector configuration.

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A note on the eBPF agent

The zero-code agent natively decodes several GenAI APIs — OpenAI, Anthropic, Gemini and Bedrock — at the network boundary. That is a second, code-free route to seeing model calls, and it overlaps with this feature. It is early access and not integrated with the views described here. See Zero-code instrumentation.

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Availability

LLM observability is a plan feature. A request to it when it is not enabled returns an explicit upgrade error rather than an empty view — see Plan limits.

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Next

AI features

The AI aiAxonIQ runs over your telemetry.

AgentSight

eBPF-observed AI-agent behaviour.

Alerting

Alerting on cost and error rate.