Short answer: OpenTelemetry (OTel) and an LLM observability platform usually work best together. OTel provides common ways to create and export telemetry; a platform receives that data and may add agent-focused trace views, token and cost details, prompt workflows, or evaluation tools. The practical decision is how to instrument your agent, which backend can interpret the data you emit, and whether its workflow and governance fit your team.
What each option does
OpenTelemetry creates and moves telemetry
OpenTelemetry is an ecosystem of APIs, SDKs, conventions, and transport for telemetry such as traces. Its trace model connects related operations through context and parent-child relationships, letting an operator follow an overall request into its constituent work. See the OpenTelemetry trace concepts.
For AI applications, the OpenTelemetry semantic conventions include a Generative AI area covering agent spans, provider conventions, events, metrics, and Model Context Protocol. The documentation showed semantic-conventions version 1.44.0 on October 4, 2026; convention names and maturity are version-sensitive, so check the exact convention and instrumentation library you use. See the semantic conventions documentation.
An observability platform interprets and presents data
An LLM observability platform is a destination and user-facing product layer. Depending on the product, it may map telemetry into AI-specific observations and provide trace exploration, prompt linking, scoring, evaluation, token usage, or cost tracking. These features are product-specific, not guaranteed by OTel or by every backend.
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For example, Langfuse documents an OTel-native SDK and direct OTel ingestion, along with mappings for GenAI data such as model identifiers and usage attributes. It also documents platform features including prompt linking and scoring. Those are Langfuse capabilities as described in its OTel documentation, not a promise about other destinations.
How to decide
Compare your actual implementation options across these dimensions rather than treating the choice as a universal either-or decision.
Rank #2
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| Decision area | What to verify |
|---|---|
| Instrumentation coverage | Whether libraries cover your language, model providers, agent framework, retrieval components, and tools; and whether instrumentation is automatic, manual, or mixed. |
| Trace semantics and fidelity | Whether the emitted data preserves a useful hierarchy across the agent run, model calls, tool invocations, and retrieval—and whether the backend both accepts the conventions and renders their attributes meaningfully. |
| Portability and routing | Whether your application exports through OTLP and can route through an OpenTelemetry Collector, and whether adding or changing destinations avoids rewriting instrumentation. Verify mapping limitations before assuming destinations are interchangeable. |
| AI development workflow | Whether the product supplies the prompt/version, evaluation, scoring, experiment, or token-usage workflows your team needs. Verify each feature against the specific product. |
| Governance and deployment | Hosted versus self-managed operation, data residency, access controls, retention and deletion, redaction, and whether prompt and response content is captured. |
| Operational cost and volume | Expected span volume, sampling and filtering controls, storage and retention, and the vendor’s current pricing. No cross-vendor price or cost benchmark is established here. |
What makes an agent trace useful
A trace should connect the whole agent run to meaningful child operations, not just record an isolated model request. Depending on the application, those operations can include model calls, tool use, and retrieval. The hierarchy gives operators a way to locate where a failure, delay, or unexpected result occurred.
Backend support needs to be checked beyond whether it accepts OTLP. “OTLP-compatible” alone does not establish that a destination recognizes every GenAI attribute, preserves the relationships you need, or displays them usefully. Langfuse documents its own mapping and filtering behavior; Amazon OpenSearch Service describes a different, service-specific AI observability path that uses OTel instrumentation, GenAI attributes, an OTel Collector, OpenSearch Ingestion, and its Agent Traces interface. The OpenSearch workflow describes hierarchical traces, trace and span identifiers, parent relationships, timestamps, duration, status, and selected gen_ai.* attributes. See Amazon OpenSearch Service AI observability for that backend’s requirements; they should not be generalized to every backend.
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A practical implementation sequence
- Start with existing tracing. Identify the trace context and instrumentation stack already used by your application.
- Add GenAI instrumentation. Use available provider or framework instrumentation and relevant GenAI conventions. Add custom spans or attributes for agent operations not covered by those libraries.
- Inspect a complete emitted trace. Check parent-child links, model and operation attributes, tool and retrieval spans, errors, timestamps, usage attributes, and any prompt or output content that is captured.
- Test the intended destination. Verify how it maps, filters, and renders the actual fields in your traces. Langfuse documents that filtering can remove spans and leave incomplete traces, and that mapping affects whether fields appear as trace attributes, observation fields, or queryable metadata. See its mapping and filtering guidance.
- Apply backend-specific requirements. If using the documented OpenSearch route, follow its current service prerequisites and required trace attributes, including the OpenSearch Ingestion pipeline.
- Record versions. Document the semantic-convention and instrumentation-library versions in use, then check the official specifications before upgrading.
Handle prompt data and propagation deliberately
Telemetry may contain sensitive prompts, model outputs, identifiers, or other application data. Decide explicitly what content is captured and stored, who can access it, how long it is retained, and how it can be redacted or deleted. These choices are separate from whether a backend supports OTel.
Pay particular attention to OpenTelemetry baggage: it can cross service boundaries and reach third-party APIs. Langfuse’s documentation warns against propagating passwords, API keys, or personal data as baggage. See its OTel documentation.
Rank #4
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Which approach fits your team?
Use OTel as the foundation when portability matters
OTel is useful when you want shared instrumentation and a standard way to route telemetry to destinations. It does not, by itself, guarantee AI-specific dashboards, prompt management, evaluation workflows, or useful rendering of every GenAI field.
Add a specialized platform when its workflow earns its place
A platform can add value when its agent trace views or AI development tools match your team’s needs. Confirm that it supports your emitted conventions and fields, and assess its data handling and operating model directly. A backend’s OTLP support is only one part of that check.
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Use both when you need standard instrumentation and AI-focused tools
Instrument with OTel and export to a suitable platform when you want a portable telemetry layer plus platform-specific debugging or evaluation features. The combination still depends on the destination’s mapping, filtering, and rendering behavior.
What the available evidence does not establish
The official documentation cited here describes the standard and selected product workflows; it does not provide an independent comparison of platform performance, usability, reliability, security, or cost. It also does not establish one best platform. Pricing, plan limits, data-region availability, integration coverage, and convention maturity can change, so verify them for the products, versions, and deployment regions you are considering.
Quick Recap
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