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OpenTelemetry for GenAIOpenTelemetry for GenAI
OpenTelemetry for GenAI

OpenTelemetry for GenAI

Ingest and analyze OTel GenAI traces & metrics for your AI workloads.

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OTel based AI Agents topology and dependency map showing multi-agent call relationships with per-agent LLM req counts, token usage, and response time.Prompts stream showing all Agent and LLM interactions with model version, token count, and full prompt/response content.Trace-level debug view showing agentic span hierarchy, tool calls, prompt and response content, and agent topology for a single AI agent run.OTel based LLM-as-judge evaluation detail with score, pass/fail label, explanation, and gen_ai.evaluation.* attributes.Evaluation performance for OTel based gen_ai applications
  • Product information

Overview

OpenTelemetry is the vendor-neutral, CNCF standard for collecting traces, metrics, and logs from any application. The GenAI semantic conventions extend OpenTelemetry with a consistent, provider-agnostic schema for AI-specific signals — model names, token counts, latency, cost, agent runs, tool calls, and evaluation results — across every major LLM provider.

Dynatrace natively understands gen_ai.* attributes with zero transformation required. Send spans and metrics directly to the Dynatrace OTLP endpoint or through a Collector and your data populates the AI Observability app immediately — no third-party instrumentation library, no attribute normalization step.

This is the lowest-overhead path to AI observability: instrument once with the open standard, export over OTLP, and get full model performance, cost, and agentic workflow visibility out of the box.

Use cases

Monitor AI service health and performance

  • Detect bottlenecks by tracking real-time metrics: gen_ai.client.operation.duration and gen_ai.client.token.usage — across models and providers.
  • Manage costs with automated token-level cost calculations broken down by gen_ai.provider.name and gen_ai.request.model.
  • Set error budgets for performance and cost controls

End-to-end tracing and debugging of agentic AI

  • Achieve full visibility of prompt flows, tool calls, retrieval steps, and agent handoffs — from invoke_agent to final response — using standardized span names and the W3C Trace Context propagation built into OTel.
  • Capture detailed debug data including full prompt and completion content via gen_ai.input.messages and gen_ai.output.messages (opt-in, disabled by default to protect sensitive data).
  • Pinpoint exact failure points across chat, execute_tool, retrieval, and invoke_workflow spans with no vendor-specific wrappers.

Multi-provider and multi-model visibility

  • Instrument once and compare performance side by side across OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Gemini, and any custom endpoint: all using the same gen_ai.* schema.
  • Detect provider-level degradation and attribute token usage and latency to specific models for accurate cost allocation.
  • Track model version rollouts and routing decisions in a single trace view.

Evaluate AI quality in production

  • Attach evaluation results with dt-evals — scores, labels, and explanations — to inference spans using gen_ai.evaluation.result events, enabling LLM-as-judge and custom evaluator output alongside latency and cost.
  • Correlate quality regressions with model version changes, prompt updates, or provider switches in the same distributed trace.
  • Build quality dashboards on top of gen_ai.evaluation.score.value and gen_ai.evaluation.name metrics alongside standard operational signals.

Get started

Instrumenting your AI application with native OpenTelemetry requires no additional library beyond the standard OTel SDK. Initialize a TracerProvider, add gen_ai.* attributes to your spans, and point the OTLP exporter at your Dynatrace environment. For metrics, add a MeterProvider and emit gen_ai.client.token.usage and gen_ai.client.operation.duration histograms alongside your traces.

To get started, see our OpenTelemetry GenAI instrumentation examples on GitHub and the OpenTelemetry and AI Observability getting started guide.

Additionally, you can read more about how to install the AI Observability app.

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