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OpenTelemetry is the instrumentation and export standard; it is not a monitoring console. Langfuse is an OpenTelemetry-based LLM observability platform with documented Claude and Anthropic integrations and evaluation workflows. Helicone combines an AI gateway with request-level LLM observability, making it a natural fit when proxying traffic is central. For many production Claude systems, the practical design is to emit Claude traces with OpenTelemetry and send them to Langfuse or another OTLP-compatible backend.
The right choice depends on whether you need a portable telemetry layer, a dedicated LLM tracing and evaluation workspace, or a gateway that sits directly in the request path. Claude now exposes spans for API requests, SDK calls, model runs, sessions, and tool-using workflows, so the decision can be made on trace depth, privacy, deployment, evaluation, and operational cost visibility rather than on whether tracing is possible at all.
What each option actually is
These products are often compared as if they were interchangeable monitoring tools. They occupy different layers, so a useful comparison starts with their roles.
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OpenTelemetry: the neutral telemetry layer
OpenTelemetry is a vendor-neutral, open-source observability framework for instrumenting, generating, collecting, and exporting traces, metrics, and logs. Its own documentation is explicit: OpenTelemetry is not an observability backend itself.
It supplies APIs, SDKs, context propagation, collectors, and exporters. You still need a backend for storage, search, dashboards, alerting, retention, access control, and evaluation workflows.
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For a Claude application, OpenTelemetry can carry a trace from an incoming user request through your application code, retrieval or database calls, Claude model spans, tool execution, and the final response. Because the telemetry format is portable, you can change the destination without rewriting all application instrumentation. The OpenTelemetry project reported support from more than 90 observability vendors in 2025; that count is time-sensitive and should not be treated as a permanent guarantee.
Langfuse: an LLM observability and evaluation platform
Langfuse describes itself as based on OpenTelemetry. It provides an LLM-focused control plane for traces, generations, token and latency analysis, prompts, and evaluation-oriented workflows, while retaining OTLP compatibility. Its documented integrations include Python and JavaScript/TypeScript SDKs, a direct OpenTelemetry endpoint, Claude Agent SDK integrations, Anthropic JavaScript/TypeScript and Python integrations, Claude Platform integration, and gateway integrations including Helicone.
Langfuse is therefore the closest match when your team wants a dedicated LLM workspace rather than only a transport standard. You can still instrument with OpenTelemetry and preserve a path to another backend later.
Helicone: an AI gateway plus observability
Helicone presents itself as an AI Gateway & LLM Observability
product. Its official homepage lists Claude among supported providers and emphasizes tracing and debugging agents. It also advertises self-hosting through a production-ready Helm chart.
The gateway position matters: Helicone can be useful when routing, proxying, provider abstraction, and request-level visibility belong in the same operational component. The official homepage does not establish the same depth of evaluation, prompt-management, or self-hosting capabilities documented for Langfuse, so those areas must be checked against the current product documentation before you standardize on them.
What Claude exposes for monitoring
API tracing and SDK tracing are separate choices
Anthropic documentation says you can trace Messages API requests, tool uses, and Managed Agents sessions with OpenTelemetry spans from the Claude SDK and Claude API. SDK tracing, API tracing, or both can be enabled.
- API tracing: covers clients using the Claude API and requires an administrative setup. It is useful when applications use several languages, custom HTTP clients, or services that do not use the Anthropic SDK.
- SDK tracing: covers clients that use the Claude SDK. The SDK emits spans inside your application, and your OpenTelemetry exporter sends them to the selected backend.
API tracing and SDK tracing can be combined, but plan the ownership of each span so that one request is not counted twice. A common pattern is to use SDK spans for application-side context and API tracing for centrally governed coverage, then verify the resulting trace tree in the backend.
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Span coverage includes tools and sessions
Anthropic’s API documentation lists spans for Messages API and Sessions API requests, token usage, model runs, Managed Agents turns, server tools, client tools, and self-hosted sandbox tools. The SDK documentation states that the SDK can emit a span for each request it sends and each tool its tool runner runs.
This makes Claude tracing more than a model latency timer. A single trace can show where time and tokens were spent, which tool call failed, whether a server-side or client-side tool was responsible, and how an agent turn relates to the parent user request. Confirm the exact span set for your SDK version and integration: an application using the raw API, the SDK tool runner, Managed Agents, and self-hosted tools will not produce identical trees.
Content and propagation controls
The Claude SDK defaults to metadata_only. In that mode, spans contain operational metadata without prompt and response bodies. Setting content mode includes prompts and responses. The documented default maximum content size is 0.75 MB per span.
Anthropic also documents conversation IDs for linking calls, propagation controls, and a way to turn tracing off for a client. Organizations with HIPAA, FERPA, or K-12 data restrictions should export metadata only; Anthropic notes that SDK spans are not affected by the destination rule described for those organizations. With zero data retention, exported content is held in memory until delivery for eligible Messages API requests.
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These settings are not cosmetic. Prompt and response bodies may contain personal data, credentials accidentally echoed by a tool, confidential business text, or regulated records. Decide whether the backend needs content at all, apply redaction before export where possible, and document retention and access policies before enabling content mode.
How to trace a Claude application with OpenTelemetry
The exact package names vary by language and SDK release, but Anthropic’s documented setup follows the same sequence.
- Install or upgrade the Anthropic SDK. Use a version that supports the tracing features required by your application, then record that version in your deployment manifest.
- Register an OpenTelemetry tracer provider. Configure the provider early in application startup, before code creates Claude clients or handles requests. Add resource attributes that identify the service, environment, and deployment version.
- Replace the console exporter with an OTLP exporter. Point the exporter at your chosen OTLP endpoint, whether that is a Langfuse endpoint, an existing observability stack, or another compatible collector. Configure authentication and TLS using your platform’s secret-management mechanism.
- Choose the content mode. Keep the default
metadata_onlyunless there is a documented need for prompt and response bodies. If content is enabled, set a deliberate size limit and redact sensitive fields. - Configure context propagation and conversation identity. Preserve the incoming trace context so model and tool spans appear under the correct application request. Use conversation IDs when multiple Claude calls belong to one user or agent conversation. Disable propagation only when an explicit isolation requirement demands it.
- Exercise every path in a staging environment. Trigger a normal Messages API call, a failed call, a client tool, a server tool, and an agent session if your application uses one. Confirm that the expected spans arrive, that token and latency attributes are populated, and that content policy is working.
- Promote the exporter and backend configuration separately from code. Keep endpoint URLs, credentials, sampling, retention, and content settings in deployment configuration. This lets you redirect telemetry without rebuilding the application.
The application’s exporter is responsible for delivery. Claude emits spans, but Claude does not provide the storage, dashboards, alert rules, or retention policy that make those spans useful over time.
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OpenTelemetry vs Langfuse vs Helicone
| Decision axis | OpenTelemetry | Langfuse | Helicone |
|---|---|---|---|
| Primary role | Instrumentation, context propagation, collection, and export standard | LLM observability and evaluation platform based on OpenTelemetry | AI gateway combined with LLM observability |
| Backend included | No; a separate OTLP backend is required | Yes, as part of the platform offering | Yes, with gateway-oriented request visibility |
| Claude integration documented | Claude SDK and API can emit OpenTelemetry spans | Direct Claude, Anthropic, Claude Agent SDK, and OpenTelemetry integrations | Claude support, tracing, and agent debugging are highlighted on the official homepage |
| Portability | Highest; exporters and collectors can be changed | OpenTelemetry-compatible, but the user experience is platform-specific | Gateway and observability control plane are product-specific |
| Evaluation workflow | Not supplied by the standard | Evaluation-oriented integrations and workflows are documented | Equivalent depth is not established by the cited homepage |
| Gateway or proxy function | Not a gateway | Integrates with gateways, including Helicone | Core part of the product positioning |
| Self-hosting evidence in the cited material | Depends on the selected collector and backend | Deployment options require checking current Langfuse documentation | Production-ready Helm chart is advertised |
| Pricing and plan limits | Not stated in the available official material | Not stated in the available official material | Not stated in the available official material |
The table shows why a binary “winner” is misleading. OpenTelemetry and a platform such as Langfuse can be used together. Helicone can also sit in a gateway path while traces continue through OpenTelemetry to a selected backend.
Which choice fits your Claude system?
Choose OpenTelemetry first when portability is the priority
Start with OpenTelemetry when you already operate an observability platform, expect to change vendors, or need one trace model across Claude, databases, queues, HTTP services, and non-LLM components. The standard lets you connect Claude spans to the rest of the request instead of trapping model telemetry inside a separate console.
This route requires more assembly. Your team must select and operate the OTLP collector or backend, create LLM-specific dashboards, define token and cost attributes, and build evaluation workflows if they are needed. OpenTelemetry solves transport and instrumentation, not those product decisions.
Choose Langfuse when LLM analysis and evaluation are central
Langfuse is the strongest fit when prompt and generation analysis, trace inspection, and evaluation workflows are first-class requirements. Its documented Claude and Anthropic integrations reduce the amount of custom adaptation needed, while its OpenTelemetry foundation preserves a standards-based ingestion path.
Check the current deployment, retention, residency, access-control, and redaction options against your organization’s requirements. The existence of an integration does not by itself answer where data is stored, how long it is retained, or who can view prompt content.
Choose Helicone when a gateway is part of the architecture
Helicone is worth considering when requests already need to pass through a gateway for routing, provider abstraction, authentication boundaries, or centralized request logging. Its published positioning emphasizes Claude support, agent tracing and debugging, and self-hosting with Helm.
Do not infer feature parity with a dedicated evaluation platform from gateway observability alone. If you require dataset management, repeatable evaluator runs, prompt versioning, or specialized experiment analysis, verify those capabilities directly before committing.
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Use a combination when responsibilities are clear
A layered design can use OpenTelemetry in application code, Helicone as a gateway, and Langfuse or another OTLP backend for analysis. This is sensible only when each layer has a defined responsibility. Otherwise, duplicate proxies and exporters can inflate traffic, complicate identity, and produce duplicate spans or conflicting cost figures.
Trace depth: questions to verify before deployment
Ask these questions against the exact Claude API, SDK, and agent features you use:
- Does a top-level application trace contain the Claude model span, or does the model call appear as an unrelated trace?
- Are input and output token counts recorded for every model run, including retries and streaming responses?
- Can you distinguish Messages API requests from Sessions API and Managed Agents turns?
- Do client tools, server tools, MCP-related actions, and self-hosted sandbox tools appear as separate spans?
- Are failures represented with an error status and useful exception attributes?
- Can a conversation ID connect multiple model calls without exposing the conversation body?
- Does sampling remove the traces needed for incident investigation or evaluation?
- Can you join model spans to user, tenant, feature, and deployment identifiers without placing sensitive text in those attributes?
Run this checklist in staging with representative failures. A successful single prompt proves only that basic export works; it does not prove that an agent workflow is observable.
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Metadata-only versus content traces
Metadata-only tracing is generally the safer default for production. It can still reveal model name, latency, token usage, status, and tool timing, which are enough for many operational alerts. Content mode is valuable for debugging prompt construction, tool arguments, and bad completions, but it increases the impact of a data leak and the obligations attached to retention.
If content is necessary, consider separate projects or tenants for sensitive and non-sensitive workloads, redact known secrets before export, restrict who can open trace bodies, and set an explicit deletion schedule. The 0.75 MB default maximum applies per span, not to an entire conversation, so a long workflow can still produce substantial content volume across many spans.
Propagation and correlation
Propagation connects a Claude span to its parent request and to downstream tools. It improves debugging but also carries correlation identifiers across service boundaries. Disable propagation only when isolation or policy requires it, and document the resulting loss of cross-service visibility.
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Anthropic documents that, for eligible Messages API requests with zero data retention, exported content is held in memory until delivery. That behavior does not remove the need to examine what your own exporter, collector, and backend retain. Data controls must be evaluated across the complete path, not only at the Claude API boundary.
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Operational visibility you should build
Whichever backend you select, create views and alerts around the dimensions that affect users and budgets:
- Latency by Claude model, route, tool, and deployment version.
- Input and output token usage by service, tenant, feature, and conversation.
- Errors grouped by API status, SDK exception, tool name, and retry outcome.
- Timeouts and queueing time separated from model execution time.
- Trace links from a user request to retrieval, application code, Claude, and tools.
- Sampling rules that preserve all errors and a useful percentage of successful traces.
- Alerts for sudden token growth, latency regressions, repeated tool failures, and export backlogs.
Cost attribution is especially important when several features share one Claude credential. Claude spans expose token usage, but a reliable cost view still requires your backend to apply the correct model rates and account for retries, cached or batched work where applicable, and any gateway behavior. No independent pricing or latency benchmark is established here, so do not select a product on an unsupported cost or performance claim.
Common failure modes and fixes
Nothing appears in the backend
Check that the tracer provider is registered before the Claude client is created, that the OTLP endpoint and protocol match, and that outbound TLS and firewall rules permit delivery. Temporarily enable exporter diagnostics in a non-production environment, then disable verbose logging after the path is confirmed.
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Inspect context propagation at the web-server, queue, and worker boundaries. A new root span created inside a worker will hide the relationship to the original user request. Pass the trace context explicitly through asynchronous jobs and verify that conversation IDs are stable where they should be.
Tool calls are missing
Confirm whether the application uses an Anthropic SDK tool runner, a custom tool loop, a Managed Agents session, or a gateway. The SDK can emit spans for requests and tools run by its tool runner; custom loops may require their own instrumentation. Test server, client, and sandbox tools independently.
Traces contain sensitive prompts
Set the SDK to metadata_only, turn off content capture where possible, remove existing content from the backend according to its deletion process, and review collector logs and dead-letter queues. Restrict trace-body access rather than relying only on application-level permissions.
Token or cost totals look inflated
Look for duplicate API and SDK instrumentation, retries represented as separate model runs, gateway-generated attempts, and multiple exporters sending the same span. Establish one accounting rule and attach an idempotent request or span identifier before aggregating.
Deployment and migration strategy
- Instrument the application with OpenTelemetry first. Keep the exporter destination configurable so the code is not tied to one vendor.
- Send a small staging sample to the intended backend. Validate model, session, tool, token, error, and latency fields before enabling broad sampling.
- Define privacy policy before production capture. Decide metadata versus content, redaction, retention, residency, RBAC, and audit requirements.
- Add LLM-specific views and evaluations. Use Langfuse’s documented evaluation-oriented workflows or build equivalent views in your existing backend; do not assume the telemetry standard supplies them.
- Introduce a gateway only for a gateway problem. If routing and proxy controls are required, evaluate Helicone in that role and verify that trace identity remains consistent end to end.
- Keep an exit path. Retain raw OpenTelemetry configuration, schema documentation, and export tests so a backend change does not require rewriting Claude instrumentation.
Bottom line
OpenTelemetry is the foundation, not the finished monitoring product. It gives Claude applications portable instrumentation, context propagation, and OTLP export, while leaving storage and user experience to a backend. Langfuse is the most complete fit of these three when you want a dedicated LLM observability and evaluation workspace with documented Claude integrations. Helicone is the more natural fit when an AI gateway, proxy deployment, and request-level debugging are central.
For a new production system, instrument Claude with OpenTelemetry, keep content capture disabled until it is justified, verify API and SDK span coverage for every tool path, and choose the backend according to portability, evaluation needs, privacy and deployment controls, and the quality of token, latency, and error attribution you can operate.
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