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Traditional monitoring can tell you an AI endpoint is responding quickly and returning successful HTTP responses. It cannot, by itself, tell you whether the answer is grounded, whether an agent used the right tool, or whether a policy was followed. LLM observability adds a view of the full AI workflow and evaluates its behavior alongside familiar service-health signals.
Why is traditional monitoring not enough for LLM applications?
Application performance monitoring (APM) remains useful for tracking latency, errors, request volume, and infrastructure health. The gap is that those signals describe service operation, not the meaning or appropriateness of a model’s output. A response can return HTTP 200 and still be irrelevant, unsupported, unsafe, or the result of an incorrect tool action.
Generative systems can also produce different outputs for similar requests. Their behavior depends on more than the model endpoint: prompts, retrieved context, model routing, tool results, and guardrails all influence what happens. Microsoft puts the limitation plainly: “Uptime and error rates are not good indicators of quality and reliability in AI systems.”
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LLM observability therefore extends—not replaces—APM. It connects operational signals to the sequence of steps that produced an answer and adds evaluation of the result.
What should you instrument in an LLM application?
Build a correlated trace around a user request or agent run. Represent meaningful operations as linked spans so an engineer can follow the request through orchestration, retrieval, model calls, tools, and relevant policy decisions. AWS documents hierarchical traces for orchestration, LLM calls, tools, and retrieval; Microsoft and Google also describe tracing AI execution paths.
Capture enough lineage to explain a run
- Request and run identifiers: Correlate the AI work with the user-facing request and the surrounding application trace.
- Model and prompt metadata: Record the provider or route, model identifier, and prompt or template version. Prefer version identifiers over copying full prompt text into broadly accessible telemetry.
- Retrieval details: Record which retrieval and reranking steps ran and the provenance of the material used, subject to privacy and access controls.
- Tool activity: Capture tool identity, invocation outcome, and failures. Include arguments or results only when needed and protected appropriately.
- Execution measurements: Measure duration and token use at both request and relevant step level; record retries, fallbacks, and errors when the application implements them.
- Evaluation and policy outcomes: Associate the run with the evaluation result or guardrail decision that applies.
This lineage helps distinguish a regression tied to a prompt change from one tied to a model route, retrieval corpus, tool, or policy. Google Cloud’s documentation separates the roles of logs for events and errors, metrics for latency and token usage, traces for execution paths, and prompt/response data for quality analysis.
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Keep metrics useful and bounded
Use metrics for aggregate trends such as request duration, token consumption, errors, request volume, and tool-call volume or failure rates. Avoid placing raw prompts, completions, retrieved chunks, or user identifiers in metric labels: these can be sensitive and create high-cardinality dimensions that are difficult to manage. A May 2026 OpenTelemetry community discussion recommends separating spans, low-cardinality metrics, and events or logs; it is guidance under discussion, not a ratified requirement.
How do you monitor hallucinations and response quality in production?
Operational signals should be paired with task-specific evaluations. For retrieval-augmented generation, assess whether answers are grounded in retrieved evidence and relevant to the request. For agents, assess task completion and whether tool use was correct. For systems with risk controls, monitor safety and policy outcomes.
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Establish behavioral baselines and alert on meaningful changes rather than treating every score as a pass/fail verdict. Microsoft Foundry documents evaluation during development and production, including pre-deployment datasets, sampled continuous monitoring, scheduled evaluation for drift, and red teaming. Google Cloud documents using prompt and response inputs for quality analysis.
Automated evaluation scores are diagnostic signals, not ground truth. Their meaning depends on the evaluator, model, dataset, and task; a score should help identify where to investigate, not certify that every response is correct or safe. When an alert fires, inspect the correlated trace and evaluation context to locate the affected step or behavior.
Should you use OpenTelemetry or a dedicated observability platform?
These choices are not mutually exclusive. OpenTelemetry (OTel) provides a shared instrumentation approach for connecting AI spans with application traces. A cloud observability service can provide dashboards, trace exploration, and evaluation workflows around that telemetry. Choose based on your existing infrastructure and the capabilities your team needs.
| Documented option | What its official documentation describes | Useful fit to assess |
|---|---|---|
| Microsoft Foundry with Azure Monitor Application Insights | Evaluation, monitoring, and OTel-based tracing integrated with Application Insights; documented signals include quality and safety scores, token consumption, latency, errors, and agent or tool execution. | Teams evaluating an integrated Microsoft cloud workflow for AI tracing and lifecycle evaluation. |
| Amazon OpenSearch Service | Hierarchical AI-agent traces, GenAI semantic attributes, automatic capture for named frameworks and providers, and a trace exploration interface. | Teams assessing agent-path visibility and trace exploration in an AWS context. |
| Google Cloud Application Monitoring | Agent dashboards and topology views, trace-derived metrics such as model-call counts and token use, and prompt/response inputs for quality analysis; its documentation describes aggregating trace data with application labels and events that follow OTel GenAI conventions. | Teams assessing agent dashboards and trace-derived monitoring in a Google Cloud context. |
These are examples from vendor documentation, not independent comparative tests, and they do not establish a universal best product. Compare them against your own requirements: framework and provider coverage, whether the full agent path is visible, evaluation and safety workflows, access and retention controls, interoperability and export, and the operational and usage costs that apply to your deployment.
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What is stable about OpenTelemetry GenAI conventions?
Microsoft, AWS, and Google Cloud documentation use or recommend OpenTelemetry GenAI semantic conventions for AI-related traces or aggregation. That makes OTel a reasonable shared-instrumentation starting point, particularly when AI spans need to connect to existing application traces.
Do not assume that every proposed metric name, instrument type, or optional cost extension is settled. A May 2026 discussion in the OpenTelemetry specification repository records open questions about those areas and about separating spans, metrics, and sensitive events. The discussion shows active work; it does not establish that each proposal is a stable convention. Check the current specification before standardizing a specific attribute or metric.
How should you protect observability data?
Prompts, responses, retrieved material, user context, and tool arguments or outputs can contain sensitive information. Rich telemetry can aid incident reconstruction and help detect misuse, but it also increases exposure if copied indiscriminately into logs or dashboards. Microsoft recommends data contracts that balance forensic needs with privacy, residency, minimization, retention obligations, access controls, and encryption.
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- Set access rules for trace content and evaluation data before broad dashboard access is granted.
- Specify redaction or minimization, encryption, data residency, and retention requirements for prompts, responses, retrieved content, and tool data.
- Keep sensitive or high-cardinality values out of metric dimensions; use appropriately controlled traces or events when detailed context is necessary.
Treat these controls as part of ongoing operations: telemetry needs to remain useful as models, prompts, tools, policies, and data-handling obligations change.
What a useful LLM observability setup answers
For a particular request or run, an engineer should be able to determine which model and prompt version ran, what retrieval sources and tools were involved, where time and token use accumulated, and what evaluation or policy result applied. Aggregate dashboards should then show whether service health and task-specific behavior are changing over time. That combination makes observability useful for both incident diagnosis and production quality management.
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