Monitor an AI application by instrumenting its services and model workflows with OpenTelemetry, sending telemetry through an OpenTelemetry Collector when you need centralized processing, and using Prometheus for metrics. Add traces and logs in suitable backends so you can investigate individual requests behind an alert. For AI-specific visibility, track model calls, tokens, retrieval, tool execution, failures, and evaluation outcomes—while keeping sensitive prompt and response content out of telemetry by default.
What OpenTelemetry and Prometheus each do
OpenTelemetry (OTel) is a vendor-neutral framework for instrumenting applications and generating, collecting, processing, and exporting telemetry. It is not a storage or query backend. Prometheus is a metrics-oriented system that scrapes, stores, and queries time-series metrics. They complement one another: use OpenTelemetry to produce and move telemetry, and Prometheus to work with metrics.
The three principal signals answer different questions:
| Signal | What it shows | Best used for |
|---|---|---|
| Metrics | Aggregated measurements such as request rate, error rate, latency distributions, resource use, and token counts. | Dashboards, alert rules, and changes over time. |
| Traces | The sequence of work for an individual request, including service calls, model requests, retrieval, and tools. | Finding where a request slowed down or failed. |
| Logs and events | Timestamped diagnostic details and discrete outcomes, which can be associated with a trace. | Investigating what happened in a particular operation. |
Metrics are efficient for detecting patterns across many requests, but do not preserve every request’s detailed context. Traces and logs supply that detail. Together, the signals help answer both whether an AI service is unhealthy and why a particular workflow behaved as it did.
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How to connect OpenTelemetry to Prometheus
A common design sends application telemetry in OTLP format to an OpenTelemetry Collector, then routes metrics into a Prometheus-compatible workflow. Prometheus and OpenTelemetry document interoperability in both directions: Prometheus can work with metrics exposed from an OpenTelemetry pipeline, and the Collector can ingest Prometheus metrics. The precise exporter, endpoint, and configuration depend on the selected Collector components and backend; do not assume that every Prometheus setup accepts every OTLP signal or transport directly.
- Instrument the application. Add OpenTelemetry SDK instrumentation or compatible instrumentation to the application components whose behavior you need to observe.
- Export to a Collector when central processing is useful. Configure applications to send OTLP telemetry to the Collector. A Collector tier is optional in simpler deployments, but provides a central place for processing and routing.
- Configure the Collector pipeline. Use receivers to accept telemetry, processors for tasks such as batching, filtering, enrichment, retries, or sampling, and exporters to send each signal to its destination.
- Route metrics to Prometheus. Choose a Prometheus-compatible integration path and configure Prometheus to receive or scrape the resulting metrics as that path requires.
- Send traces and logs to appropriate destinations. Prometheus is the metrics part of this design; choose backends suited to the other signals and preserve the context needed to connect them.
- Verify the complete path. Confirm that expected metrics arrive, dimensions are bounded, and a representative trace or log can be related to the metric view using the correlation features your backends support.
Direct SDK export can be a reasonable topology when the pipeline is simple and per-application configuration is manageable. A Collector tier is more useful when teams need shared filtering, enrichment, retries, sampling, or routing rules. It also creates another service to operate and secure.
What to instrument in an AI application
AI agents combine model capabilities with tools and application-level reasoning. Since their behavior can vary between requests, instrumentation should capture the workflow around a model call, not just whether the web service returned successfully.
| Area | Useful telemetry | Why it matters |
|---|---|---|
| Request and model call | Operation name, provider and model version, request and response latency, retries, rate limits, timeouts, and error type. | Distinguishes a slow or failing model interaction from problems elsewhere in the service. |
| Usage | Input and output token counts as metrics or trace attributes, with aggregation appropriate to the backend. | Shows how usage changes across operations, models, or deployments without putting request-specific values into metric labels. |
| Retrieval | Spans for retrieval and vector-database operations, plus document identifiers where policy allows. | Helps isolate retrieval delays and understand which stage of a workflow supplied context. |
| Tools and agent workflow | Tool name, execution timing and outcome; conversation, agent, workflow, and deployment identifiers. | Shows which step failed or consumed time and helps group behavior by workflow or release. |
| Quality and evaluation | Evaluation outcomes or quality scores associated with the relevant trace. | Connects operational behavior with measures of whether the result met the application’s criteria. |
Prefer bounded, operational dimensions for metrics, such as a known operation name or model identifier. Put request-level detail in traces or logs when it is useful and permitted. Avoid recording tool arguments or results wholesale: they may contain credentials, personal information, or other sensitive content.
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Protect prompts, responses, and other sensitive data
Prompt text, generated completions, tool arguments, and tool results can expose sensitive information. OpenTelemetry’s GenAI guidance treats content capture as opt-in in the relevant conventions. Keep it disabled by default unless there is a specific, approved debugging or evaluation need.
- Minimize collection. Record operational metadata and outcomes rather than full content whenever that is enough to troubleshoot.
- Redact before export. Apply filtering or transformation in the instrumentation or Collector pipeline so sensitive values do not reach downstream storage.
- Control access and retention. Restrict who can inspect detailed traces and logs, and set retention to match the data’s purpose and sensitivity.
- Sample deliberately. Sampling can reduce volume, but decide which traces to retain so important failures and evaluation cases remain diagnosable.
- Review every field. Identifiers, retrieved document metadata, and error details can also reveal sensitive information even when prompt capture is off.
Keep Prometheus metrics useful as the system grows
Prometheus metrics are time series identified by their names and label values. A label whose values vary for nearly every request can create an unbounded number of time series, increasing storage and query load. Do not use raw prompts, user IDs, request IDs, or unbounded tool arguments as metric labels. Put that detail in trace or log attributes instead, subject to the same privacy controls.
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For investigation, link an alert or metric view to representative traces where the chosen backend supports exemplars or trace identifiers. Keep resource attributes and trace context consistent across instrumentation so a metric can lead to the relevant service, deployment, and request details rather than an isolated number.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use semantic conventions with care
OpenTelemetry semantic conventions define common names for operations and attributes across signals and resources. Consistent names make instrumentation and dashboards more portable across libraries and backends. Infrastructure conventions are more established than the emerging GenAI and agent conventions; OpenTelemetry described active work on model, vector-database, agent-application, and agent-framework conventions in guidance dated March 6, 2025.
For AI instrumentation, pin the convention version you use, document any opt-in stability settings, and plan for migration as conventions mature. Avoid treating an evolving attribute name as a permanent interface without checking the convention status relevant to your implementation.
Build alerts around service health and AI workflow outcomes
Use metrics for alert conditions that can be aggregated and acted on. A useful starting set includes request rate, latency distributions, error rate, resource saturation, model failures, tool failures, retries, and rate limits. Add token usage and evaluation outcomes when they represent operational or product goals. Define thresholds in relation to your own service-level objectives and expected traffic; universal numeric thresholds are not established by the available official guidance.
Keep alerts actionable: identify the affected operation or deployment, include a route to the relevant dashboard, and make it possible to inspect a representative trace when supported. A rising error rate can identify a problem, while trace context helps locate whether it began in application code, a model call, retrieval, or a tool.
Quick Recap
Choose an implementation based on the trade-offs
| Decision | Option and benefit | Trade-off to plan for |
|---|---|---|
| Signal coverage | Metrics alone provide efficient aggregation; correlated metrics, traces, and logs add request-level diagnosis. | More signals mean more pipelines, storage, access controls, and retention decisions. |
| Collection topology | Direct SDK export reduces components; a Collector centralizes processing and routing. | A Collector adds a service to operate, while direct configuration can become inconsistent across applications. |
| Data control | Self-hosted components offer control over storage and sampling; managed services can provide hosted retention and query capabilities. | Assess operational responsibility, data handling, retention, and query needs rather than assuming one model fits every team. |
| AI content safety | Metadata-only capture minimizes exposure; approved redacted or sampled content may help particular debugging or evaluation tasks. | Content capture requires stronger privacy review, access controls, and retention decisions. |
| Convention maturity | Established infrastructure conventions can support more stable instrumentation. | GenAI and agent conventions are evolving, so versioning and migration planning matter. |
| Operational cost | Sampling, bounded metric dimensions, and appropriate retention can control data volume. | Ingest, storage, metric cardinality, retention, and query load all affect ongoing cost. |
Common mistakes to avoid
- Treating OpenTelemetry as a backend. It instruments and moves telemetry; select storage and query systems for the signals you collect.
- Expecting Prometheus to replace traces. Metrics reveal aggregate behavior; retain traces and logs when request-level diagnosis is needed.
- Putting unique values in metric labels. Keep high-cardinality or sensitive request data in controlled trace or log fields instead.
- Capturing AI content by default. Start with content capture off, then justify and protect any opt-in collection.
- Assuming GenAI attribute names will not change. Track convention stability and versions rather than coupling dashboards to undocumented assumptions.
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