Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsChoose an OpenTelemetry backend by verifying that it accepts the OTLP signal and the span attributes your instrumentation emits, then checking whether it makes the model calls, tool activity, retrieval, errors, and context you need visible. OTLP compatibility is a starting point—not proof that a backend provides the same AI-specific views, controls, or debugging experience as another.
What the backend needs to do
OpenTelemetry standardizes instrumentation and telemetry conventions; the backend receives exported traces and makes them available for inspection or analysis. Shared semantic conventions can make telemetry more consistent, but they do not guarantee that your instrumentation emits every relevant field or that a backend interprets those fields as you expect. Review the OpenTelemetry semantic conventions alongside the conventions and attributes supported by your instrumentation and candidate backend.
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For LLM and agent tracing, the practical question is whether the full run remains understandable: can you follow an agent span into a model call or tool call, see retrieval activity where relevant, and identify errors, retries, latency, and token usage? The answer depends on the emitted span format, backend mappings, and the views available in your configuration.
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- Ingestion: Confirm OTLP support for the signal you export and the endpoint, headers, and exporter configuration required for your deployment.
- Convention and attribute support: Match your emitted GenAI semantic conventions or OpenInference spans to the backend’s documented support. Do not assume that similarly named attributes are mapped identically.
- Debugging workflow: Check whether you can inspect model calls, tools, retrieval, errors, token usage, latency, and parent-child relationships at the level your team needs.
- Trace correlation: Verify that agent spans can be followed alongside relevant application and distributed traces, rather than becoming an isolated view.
- Governance and operations: Evaluate hosting and data location, redaction, access controls, retention, routing or filtering, query usability, operational workload, current service limits, and current costs.
These are selection criteria, not a tested scoring matrix. No comparative pricing or service-limit assessment is established here, so check current vendor terms directly.
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What the documented backend examples establish
The examples below show documented paths and capabilities, not a market-wide ranking or proof of feature parity. Validate each against the exact instrumentation and deployment you intend to use.
| Backend or path | What documentation establishes | What to validate |
|---|---|---|
| Langfuse | Langfuse describes direct OpenTelemetry export and an OTEL-native SDK. Its SDK maps spans to observations and documents helpers for token usage, cost tracking, prompt linking, and scoring. It also says the SDK shares OpenTelemetry context so spans from other instrumented libraries can be exported, while default filtering focuses on LLM-relevant spans. See the OpenTelemetry guide and integration documentation. | Check which spans are filtered, how your provider setup behaves, and whether the observations expose the fields and context needed for your workflow. |
| Datadog Agent Observability | Datadog documents support for selected GenAI semantic conventions and OpenInference, plus mappings for OpenLLMetry and Langfuse attributes. Its guide specifies instrumentation requirements. It says traces in the Agent Observability Traces page can take 3–5 minutes to appear, while APM-enabled traces appear immediately in APM Traces. These timings describe Datadog’s documented flow, not an independently measured latency guarantee. See the Datadog instrumentation guide. | Confirm your span format and required instrumentation fields, then inspect actual spans to ensure the mappings preserve the information you use to debug. |
| Grafana Tempo and Honeycomb | Microsoft’s agent-monitoring guide lists Grafana Tempo and Honeycomb as examples of OTLP-compatible backends, alongside Datadog, and documents a Langfuse path. See the Microsoft agent-monitoring guide. | The documented compatibility path does not establish particular GenAI dashboards or feature parity in every configuration. Check ingestion, query experience, and required field visibility in your setup. |
Test with representative traces before you commit
A successful export is not enough if the backend drops, hides, or fails to map attributes that explain a run. Use a small set of representative traces to check the complete path from instrumentation to the backend’s query and inspection experience.
- Model call: Verify that the model operation and its useful context appear, including token usage if your instrumentation emits it and the backend supports it.
- Tool call: Follow the agent’s parent-child context into a tool operation and confirm that the relevant input, output, and timing fields are visible as intended.
- Error or retry: Check whether failures and retries are represented clearly enough to distinguish the original failure from later attempts.
- Retrieval, if used: Inspect a retrieval step and confirm that the query and relevant context are represented as expected, subject to your redaction policy.
- Surrounding service trace: Check whether the agent spans correlate with the application or distributed traces needed to understand the broader request.
Record any missing or differently mapped fields and compare them with the instrumentation’s emitted attributes and the backend’s documented requirements. That evidence is more useful than relying on a generic statement that a product supports OTLP.
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Agent traces may include model messages, retrieval queries, documents, and tool information. The OpenTelemetry GenAI attribute material flags message and query fields as potentially sensitive. Before exporting traces, decide which content may leave the application boundary and who may inspect it.
- Identify sensitive message, query, document, and tool fields in the spans your instrumentation emits.
- Determine where redaction or filtering occurs and whether it applies before data reaches the destination.
- Check access controls, retention, hosting location, and any data-location requirements against the intended deployment.
- Test that filtering does not remove context needed for debugging or unintentionally retain content your policy excludes.
The OpenTelemetry conventions describe data fields; they do not decide your organization’s retention or access policy. Confirm those controls with the backend configuration and your governance requirements.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plan for multiple OpenTelemetry destinations
Sending traces to more than one tool can introduce instrumentation conflicts, unwanted spans, or missing data. Langfuse documents these issues for existing OpenTelemetry setups and discusses isolated tracer providers or span filtering as approaches to consider. See its guide to existing OpenTelemetry setups.
Decide which component owns instrumentation and export configuration, how destinations are routed, and which spans each destination should receive. Then verify both destinations with the same representative traces; successful delivery to one does not prove correct delivery to the other.
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