OpenTelemetry and an LLM observability platform are usually complementary, not competing choices. OpenTelemetry (OTel) provides common ways to create and export telemetry; a platform receives that data and may add agent-focused trace views, prompt and evaluation workflows, or token-usage details. For most teams, the decision is how to instrument an agent, which backend can interpret the emitted data, and what debugging and governance features the team needs.
What is the difference?
OpenTelemetry is an instrumentation and telemetry ecosystem: APIs, SDKs, conventions, and transport for producing and moving signals such as traces. A platform is a destination and user-facing product. It can store and display telemetry, and may provide AI-specific workflows on top of it.
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In an agent workflow, a trace should connect the overall run to its component operations—such as model calls, tool invocations, and retrieval—through trace context and parent-child relationships. OTel provides the foundation for representing that work; a backend determines how it maps and presents the data. See the OpenTelemetry trace concepts and the semantic conventions. The conventions documentation listed version 1.44.0 at the time of review on October 4, 2026, including Generative AI areas for agent spans, provider conventions, events, metrics, and Model Context Protocol. Names and maturity can change, so check the exact convention and instrumentation-library versions you use.
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Evaluate the complete path from application instrumentation to the backend interface your team will actually use. “OTLP-compatible” only means a backend supports a transport path; it does not establish that it understands every GenAI attribute or displays agent relationships usefully.
#1 Best Overall
| Decision area | What to verify |
|---|---|
| Instrumentation coverage | Whether libraries cover your language, model providers, agent framework, retrieval components, and tools—and whether capture is automatic, manual, or mixed. |
| Trace fidelity | Whether the backend preserves a useful hierarchy across the full agent run, model calls, tools, and retrieval, and renders the attributes your instrumentation emits. |
| Portability and routing | Whether your app can export standard telemetry over OTLP, route it through an OTel Collector, and add or change destinations without rewriting instrumentation. Check mapping and filtering behavior before treating destinations as interchangeable. |
| AI workflow | Whether the product has the prompt/version, evaluation, scoring, experiment, or token-usage workflows your team needs. These capabilities vary by product. |
| Governance and deployment | Hosted versus self-managed operation, data residency, access controls, retention and deletion, redaction, and whether prompts or responses are captured. The cited documentation does not establish a cross-vendor security ranking. |
| Volume and cost | Expected span volume, sampling and filtering controls, storage and retention, and each vendor’s current pricing. No fair cross-vendor cost comparison is established here. |
What does using both look like?
Instrument the application with OTel and send telemetry to a backend that supports the signals and conventions you emit. This can make the instrumentation layer less tied to one destination, while the backend supplies product-specific views and workflows. Portability is not automatic: attribute mappings, convention versions, filtering, and presentation can differ.
Example: Langfuse
Langfuse documents an OTel-native SDK and direct OTel ingestion. Its documentation describes mapping GenAI data, including model identifiers and usage attributes, to platform observations, alongside features such as token usage, cost tracking, prompt linking, and scoring. Those are documented Langfuse capabilities, not guarantees about other OTel backends. Its mapping and filtering rules matter: filtering too aggressively can leave a trace incomplete, and mapping determines whether data appears as trace attributes, observation fields, or queryable metadata. Review its OpenTelemetry documentation for the current behavior.
Rank #2
Example: Amazon OpenSearch Service
Amazon OpenSearch Service documents an AI observability path built around OTel instrumentation, GenAI attributes, an OTel Collector, OpenSearch Ingestion, and an Agent Traces interface. Its documented trace structure uses hierarchical traces, trace and span identifiers, parent relationships, timestamps, duration, status, and selected gen_ai.* attributes. These are requirements of that documented OpenSearch route, not a universal schema for every backend. Consult the OpenSearch Service AI observability documentation for current prerequisites.
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- Start with your existing tracing stack. Preserve trace context already used by your services, then add GenAI conventions and provider or framework instrumentation where available.
- Represent application-specific work. Add custom spans or attributes where libraries do not capture important agent operations, such as a particular tool call or retrieval step.
- Inspect an end-to-end trace. Confirm the parent-child links connect the agent run to model calls, tools, and retrieval. Check operation and model attributes, errors, timestamps, usage attributes, and whether prompt or response content is captured.
- Test the actual destination. Verify how it maps and filters the emitted fields and whether its interface makes the relationships and details useful to your operators. Do not infer support for GenAI semantics from OTLP support alone.
- Document versions and data handling. Record the semantic-convention and instrumentation-library versions. Decide explicitly what prompts and outputs may be stored, and review the backend’s retention, access, and redaction controls.
What privacy and operational risks should you account for?
Agent telemetry may contain sensitive prompts, model outputs, identifiers, and tool details. Decide which content to capture, who can access it, how long it is retained, and how it is redacted or deleted. OTel baggage deserves particular care: Langfuse’s documentation warns that baggage can cross service boundaries and reach third-party APIs. Do not put passwords, API keys, or personal data in baggage.
Rank #3
Sampling and filtering can reduce what a backend receives, but removing spans can also make traces incomplete. Test the effect on representative agent runs rather than assuming every reduction preserves the debugging context you need.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which option fits your team?
- Use OTel as the common instrumentation layer when standard APIs and routing flexibility are priorities and your team can select or build the downstream analysis experience it needs.
- Use an LLM observability platform when its agent trace interface and AI-specific workflows meet your debugging or evaluation needs. Check exactly which conventions and fields it supports.
- Use both when you want OTel instrumentation with a platform’s specialized interface and workflows. Validate mappings, trace fidelity, data handling, and operational fit before relying on it.
No independent benchmark here establishes a universally best platform, or a winner for performance, reliability, security, usability, or price. Make the choice against your own instrumentation coverage, trace requirements, team workflow, governance constraints, and expected telemetry volume.
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