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OpenTelemetry GenAI conventions and an LLM observability platform solve different parts of the problem: OpenTelemetry gives your application a portable way to describe AI operations in telemetry, while a platform ingests, interprets, and analyzes that data. Sending spans over OTLP does not guarantee a backend understands every GenAI attribute or offers the same AI-focused workflows. Choose by verifying the exact convention and version your backend supports, how it maps or filters spans, whether it preserves context with normal application traces, and how it handles sensitive prompt and completion data.
What is the difference between OpenTelemetry GenAI traces and an LLM platform?
OpenTelemetry is the instrumentation and telemetry layer. Its GenAI semantic conventions provide names and attributes for describing operations such as model calls and related work. A vendor-specific LLM observability platform is a destination and analysis product: it receives telemetry, maps it into its own data model, and may add interfaces or workflows for investigating AI behavior.
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OTLP is a transport protocol, not a guarantee of semantic compatibility. Two systems may both accept OTLP while recognizing different GenAI conventions, requiring different attributes, or transforming and dropping spans in different ways. OpenTelemetry’s conventions page identifies semantic-conventions version 1.44.0 and points GenAI convention details to a separate repository; check that repository for the current definitions: OpenTelemetry semantic conventions.
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What should you compare before choosing a backend?
| Decision area | What to verify | Why it matters |
|---|---|---|
| Convention and version | Exact GenAI convention version supported, alternative conventions accepted, required qualifying attributes, and any mapping warnings. | Spans can use OTLP yet remain unrecognized or fail a platform’s qualification rules. |
| Instrumentation coverage | Documented packages for your frameworks and providers, and whether tools, retrieval, and custom operations need extra spans. | Telemetry is only useful for operations your instrumentation actually captures. |
| Trace context | Whether LLM calls, tool calls, and agent steps appear inside the surrounding application request trace, and how they are nested. | Context helps connect an AI operation to the request path that triggered it. |
| LLM-specific workflows | Whether the product documents token and cost tracking, prompt linking, scoring, evaluation, or experimentation separately from trace ingestion. | Trace viewing alone may not provide the prompt and evaluation workflows a team needs. |
| Privacy and data handling | Default content capture, opt-in behavior, filtering or obfuscation, baggage propagation, retention, region, and compliance terms. | Prompts and completions may contain personal, credential, or regulated data. |
| Deployment and data location | Current hosting modes and regional options in the product’s official documentation. | Deployment and location can affect operational and governance requirements. |
How do the documented platform approaches differ?
Datadog Agent Observability: check attribute qualification and mapping
Datadog documents ingestion for traces built with OpenTelemetry GenAI semantic conventions v1.37+ or supported OpenInference conventions. Teams can use compatible instrumentation or create custom spans with required attributes. Datadog maps incoming data into its Agent Observability span schema; its documentation warns that traces can be dropped if no span qualifies with listed GenAI, OpenInference, or Langfuse attributes, and that spans without any gen_ai.* attribute can also be dropped individually. Review the required fields and mapping details rather than assuming an OTLP endpoint will pass every span through: Datadog OpenTelemetry documentation.
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New Relic AI Monitoring: relate AI work to request traces, with content capture off by default
New Relic documents sending GenAI spans through OTLP into AI Monitoring and showing LLM calls, tool calls, and agent steps within request traces. Its listed prerequisites include an ingest license key, an instrumented LLM application, and network egress to the account-region endpoint. New Relic’s AI Monitoring documentation states, “Content capture is off by default.” It cautions that prompts and completions can contain PII, credentials, or regulated data, and recommends filters or attribute-level obfuscation when needed. Review what your instrumentation emits before enabling content capture: New Relic AI Monitoring documentation.
Langfuse: OTLP ingestion with documented LLM-oriented helpers
Langfuse documents an OTLP endpoint and an OpenTelemetry-native SDK v4 that converts spans into Langfuse observations. Its SDK helpers cover token usage, cost tracking, prompt linking, and scoring; other OpenTelemetry-instrumented libraries can share the OpenTelemetry context. Langfuse also cautions against propagating sensitive information in baggage: baggage crosses service boundaries and may reach third-party APIs. Check its current deployment and data-location options alongside privacy controls: Langfuse OpenTelemetry documentation.
Amazon OpenSearch Service: hierarchical traces across agent and retrieval work
AWS describes AI observability in OpenSearch Service as based on GenAI semantic conventions and natively integrated with OpenTelemetry. Its documentation covers hierarchical traces for agent orchestration, LLM calls, tool invocations, and retrieval, and provides an instrumentation example using GenAI attributes with an OpenSearch Ingestion pipeline. Confirm the documented instrumentation and pipeline fit your application: Amazon OpenSearch Service AI observability.
LangSmith: treat the December 2024 announcement as historical
In an announcement dated December 9, 2024, LangChain described direct OpenTelemetry trace ingestion for LangSmith using the OpenLLMetry semantic convention and said support for other conventions, including OpenTelemetry GenAI, was planned. That announcement does not establish LangSmith’s current GenAI convention support. Verify the current product documentation before selecting it: LangChain’s December 9, 2024 announcement.
How can you validate a backend with your own traces?
- Define the required data. Decide whether you need prompt or completion text, token and cost information, tool and retrieval steps, scoring, or just span-level debugging. Identify any data that must not leave your environment.
- Confirm your instrumentation coverage. List the frameworks, model providers, tools, and retrieval components in your application. Check the candidate’s official documentation for compatible instrumentation and determine where you will need custom spans.
- Check convention support precisely. Compare the convention and version your instrumentation emits with the backend’s documented support. Note any required attributes, accepted alternatives, schema mapping, and rules that can drop or transform spans.
- Send a representative trace. Use a non-sensitive example that includes the relevant application request, model call, and any tool or retrieval steps. Inspect which spans arrive, how they nest, and whether the platform displays the attributes you need.
- Review privacy controls before collecting content. Keep prompt and completion capture disabled unless there is a defined need and approved handling. Test filtering or obfuscation, and review baggage, retention, region, and compliance requirements.
- Recheck support when versions change. Convention definitions, instrumentation packages, and platform support evolve; validate again when upgrading emitters or changing destinations.
Which approach is a better fit?
- Use OpenTelemetry as the instrumentation foundation when portability and a common telemetry vocabulary matter. It does not by itself provide a complete analysis interface or prove that a chosen backend interprets every emitted attribute.
- Favor an existing general APM destination when correlating AI spans with application request traces is central, provided its documented convention support and mapping behavior fit your emitted data.
- Consider an LLM-focused product when its documented token, cost, prompt, scoring, or evaluation workflows match your needs; verify which features require product-specific SDK helpers beyond trace ingestion.
- Prioritize deployment constraints when hosting model, region, or data location is a hard requirement, and confirm those options in current vendor documentation rather than inferring them from OTLP support.
There is no universal winner established by the available vendor documentation. The useful comparison is between the trace your application actually emits and the destination’s documented interpretation, privacy controls, and workflows.
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