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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →OpenTelemetry, Langfuse, and Helicone solve different layers of Claude monitoring. OpenTelemetry is the neutral instrumentation, context-propagation, collection, and export standard. Langfuse is an OpenTelemetry-based LLM observability and evaluation platform. Helicone combines an AI gateway with request-level LLM observability. For most production teams, the practical choice is not an either-or decision: instrument Claude with OpenTelemetry, then send the resulting OTLP data to Langfuse or another backend. Use Helicone when routing requests through a gateway is a primary requirement.
The short answer: choose a layer before choosing a product
Claude monitoring has two separate jobs. First, your application must create useful telemetry: model calls, token usage, latency, errors, sessions, and tool activity. Second, a system must receive that telemetry, retain it, visualize it, alert on it, and support investigations or evaluations. OpenTelemetry focuses on the first job and on transporting the data. Langfuse and Helicone provide products around the second job, with different operating models.
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Anthropic now supports OpenTelemetry spans from both the Claude SDK and the Claude API. That makes the standard a sensible foundation even if you later change dashboards or backends. Langfuse is the most clearly documented fit when you want an LLM-specific workspace with tracing and evaluation-oriented workflows. Helicone is the better-shaped option when an AI gateway or proxy, centralized request controls, and gateway-level visibility are central to your architecture. None of the official material reviewed provides a defensible pricing, latency, or feature-parity benchmark, so those should not be inferred from this comparison.
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What each option actually is
OpenTelemetry: the portable telemetry layer
The OpenTelemetry documentation defines the project as “a vendor-neutral open source Observability framework for instrumenting, generating, collecting, and exporting telemetry data such as traces, metrics, and logs.” It also states plainly: “OpenTelemetry is not an observability backend itself.”
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For a Claude application, OpenTelemetry can create spans in the application, propagate trace context across service boundaries, collect the data, and export it through OTLP or another configured exporter. It does not, by itself, provide your storage, dashboards, retention policy, alerting interface, role-based access controls, or evaluation workflow. Those remain responsibilities of the destination system.
The project reported support from more than 90 observability vendors in 2025. That count is time-sensitive, but it illustrates the portability goal: instrumentation can remain stable while the receiving backend changes.
Langfuse: an LLM observability and evaluation platform
Langfuse describes itself as based on OpenTelemetry. Its documented integrations include Python and JavaScript/TypeScript SDKs, a direct OpenTelemetry endpoint, Claude Agent SDK integrations, Anthropic integrations for JavaScript/TypeScript and Python, Claude Platform integration, and gateway integrations that include Helicone.
That combination makes Langfuse the strongest documented fit of these three for teams that want a dedicated LLM observability product rather than only a telemetry transport. Its documentation also places prompt and evaluation capabilities in the platform, so it is suited to connecting production traces with quality review and evaluation work. OpenTelemetry compatibility gives you a standards-based ingestion path rather than requiring every component to use a proprietary tracer.
Helicone: an AI gateway with observability
Helicone’s official homepage positions it as “Helicone / AI Gateway & LLM Observability.” The page lists Claude among supported providers, highlights tracing and agent debugging, and advertises self-hosting with a production-ready Helm chart.
This model is useful when requests already pass through, or should pass through, a central gateway. A gateway can become the place where teams apply routing and request-level controls while also collecting visibility data. The official homepage does not establish the same evaluation, prompt-management, or self-hosting feature depth documented for Langfuse, so do not assume those capabilities are equivalent without checking current product documentation.
What Anthropic exposes for Claude
Anthropic’s Claude Platform documentation says: “Trace your Messages API requests, tool uses, and Managed Agents sessions with OpenTelemetry spans from the Claude SDK and the Claude API.” These spans can be used to debug failed or slow requests, track token spend and latency, and build evaluations from real traffic.
API tracing and SDK tracing are separate choices
- API tracing covers clients that use Anthropic’s API. Anthropic documents an administrative setup for enabling it, which means the feature is not limited to one programming-language SDK.
- SDK tracing covers clients using the Claude SDK. The SDK emits spans inside your application, and your application’s OpenTelemetry exporter sends them to the observability platform you select.
- Both can be enabled. Teams can use API-level coverage, SDK-level detail, or both, depending on where they need visibility and how they want to avoid duplicate instrumentation.
Span categories to expect
The API documentation lists spans for the following areas:
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- Messages API requests
- Sessions API requests
- Token usage
- Model runs
- Managed Agents turns
- Server tools
- Client tools
- Self-hosted sandbox tools
The SDK documentation is more specific about the unit of emission: “With OpenTelemetry, the Claude SDK can emit a span for each request that it sends and each tool that its tool runner runs.” This distinction matters in agent systems. A single user interaction may contain several model requests and multiple tool calls; a useful trace should preserve those relationships instead of reducing the interaction to one aggregate duration.
How the three approaches fit together
| Decision dimension | OpenTelemetry | Langfuse | Helicone |
|---|---|---|---|
| Primary role | Instrumentation, context propagation, collection, and export | LLM observability platform based on OpenTelemetry | AI gateway plus LLM observability |
| Backend included | No; you select and operate a destination | Yes, as part of its platform experience | Yes, as part of its gateway and observability product |
| Claude integration evidence | Anthropic documents API and SDK spans | Documents Claude Agent SDK, Anthropic SDK, Claude Platform, and direct OTLP integrations | Official homepage lists Claude support and agent tracing/debugging |
| Evaluation orientation | Not a built-in product workflow | Evaluation-oriented integrations and platform capabilities are documented | Equivalent depth is not established by the cited homepage |
| Gateway requirement | Not required | Can ingest telemetry and documents gateway integrations | Gateway/proxy deployment is a central use case |
| Self-hosting evidence in the cited material | Depends on the backend you choose | Deployment details must be checked for your edition and current documentation | Homepage advertises a production-ready Helm chart |
| Portability | Highest at the instrumentation layer | Retains OpenTelemetry compatibility | Depends on how your gateway and exporters are deployed |
The table compares roles, not product quality. A gateway product and a telemetry standard cannot be ranked on the same single scale because they answer different architectural questions.
Reference architectures for Claude monitoring
Pattern 1: Claude SDK or API to an OTLP backend
Your application enables Anthropic’s SDK or API tracing, registers an OpenTelemetry tracer provider, and exports spans through OTLP to the backend already used by your engineering organization. This is the least product-specific design. It is appropriate when your main goal is consistent traces across Claude calls and the rest of your services.
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The backend must still provide storage, search, dashboards, retention, permissions, alerting, and any evaluation workflow. OpenTelemetry does not supply those interfaces.
Pattern 2: OpenTelemetry to Langfuse
In this design, Claude emits native spans and your exporter sends them to Langfuse’s documented OpenTelemetry endpoint or through one of its Anthropic and Claude integrations. This preserves standard instrumentation while adding an LLM-focused workspace for trace inspection and evaluation-oriented workflows.
This pattern is usually the clearest choice when you need to move from “the request was slow” to questions such as which model turn, tool call, prompt version, or production example caused a quality problem. Confirm the exact integration path for your language, SDK, and deployment model before rollout.
Pattern 3: Claude requests through Helicone’s gateway
Here, the gateway is the control point for provider requests and request-level visibility. The official homepage emphasizes Claude support, tracing, agent debugging, and a self-hosting route through Helm. This arrangement can simplify centralized routing and capture when applications would otherwise implement provider-specific controls in several services.
Do not treat gateway visibility as proof that every internal application span, custom tool span, or evaluation record is automatically present. Verify how the gateway correlates requests with your application trace and how tool activity is represented.
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Pattern 4: Hybrid deployment
A hybrid can use OpenTelemetry in every service, send standard application and Claude spans to a general OTLP backend, and route selected LLM traces to an LLM-focused platform. This is useful when infrastructure teams require one central telemetry pipeline while product or model-quality teams need specialized trace and evaluation views.
Hybrid designs require an explicit policy for duplicate events, sampling, retention, and access. Without that policy, the same prompt and response may be copied into several systems, increasing both privacy exposure and storage cost.
How to enable Claude tracing
Anthropic’s documented SDK setup follows a short sequence. Exact package names and configuration fields vary by SDK version, so use the current Anthropic and OpenTelemetry documentation for your language.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11- Install or upgrade the Anthropic SDK. Use a version that supports the tracing features you need, especially if you want tool-runner spans or content controls.
- Register an OpenTelemetry tracer provider. The provider is the component that creates and manages spans in your application process.
- Replace the console exporter with an OTLP exporter. Point the exporter at the OTLP endpoint for your selected backend, with the authentication and transport settings required by that destination.
- Choose tracing scope. Enable SDK tracing, API tracing, or both. If both are active, check whether your backend will show duplicate representations of the same request.
- Set content and propagation options. Decide whether spans contain metadata only or prompt and response content, and decide whether trace context should propagate across calls.
- Send a controlled test request. Exercise a normal model request and a tool-using request. Confirm that the expected parent-child relationships, token fields, latency, and errors arrive in the backend.
- Set retention and access policies before production traffic. Treat prompts, responses, tool arguments, and tool results as potentially sensitive application data.
The exporter is the handoff point: the SDK creates spans locally, while the exporter delivers them to your chosen observability platform. If no OTLP-compatible destination is configured, enabling spans alone does not give you a searchable monitoring interface.
Privacy controls you must decide up front
Metadata-only versus content tracing
The Claude SDK defaults to metadata_only. In that mode, the span does not include the full prompt and response content. Switching to content adds prompts and responses, which can make debugging and evaluation easier but creates a substantially larger data-governance obligation.
Anthropic documents a default maximum content size of 0.75 MB per span. Treat that as a limit on captured content, not as a guarantee that a complete conversation will fit in one span. Large prompts, long responses, tool payloads, and repeated turns may require a deliberate truncation or redaction policy.
Conversation correlation and propagation
Conversation IDs can link related calls so that a multi-turn interaction is inspectable as one workflow. Propagation can be disabled when trace context should not cross a boundary, and tracing can be turned off for a client when a particular workflow must not emit spans.
Use correlation identifiers that help an operator find the relevant interaction without placing raw personal data into every span attribute. Keep user identity, tenant identifiers, and business metadata subject to the same access controls as the underlying application.
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Regulated and restricted data
Anthropic notes that organizations with HIPAA, FERPA, or K-12 data restrictions export metadata only. It also notes that SDK spans are not affected by that destination rule, so your own SDK-level content configuration remains important. In practice, compliance cannot be delegated to a backend checkbox: configure the SDK, exporter, destination retention, access permissions, and redaction behavior together.
Zero data retention
For eligible Messages API requests using zero data retention, Anthropic says exported content is held in memory until delivery. That behavior reduces persistence on the service side but does not eliminate the data handling obligations created when your exporter sends content to an external observability backend.
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1. Instrumentation portability and lock-in
Ask whether your application emits standard OpenTelemetry spans or proprietary events. A portable instrumentation layer lets you change visualization and storage without rewriting every Claude call. Langfuse retains that compatibility through its OpenTelemetry basis. A gateway-centered deployment should document how to export or correlate data if the gateway is later replaced.
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Do not stop at “Claude is supported.” Verify all of the span types your application uses:
- Messages API and Sessions API requests
- Individual model runs and token usage
- Managed Agents turns, if applicable
- Server, client, and self-hosted sandbox tools
- Custom application spans surrounding retrieval, orchestration, authorization, and post-processing
A platform may display provider requests while omitting the application spans needed to explain why a request was issued. Test a complete agent trace, not only a single chat completion.
3. Evaluation workflow
If your team needs to turn real production traces into test cases, label failures, compare prompt or model changes, and review quality over time, evaluation support becomes a selection criterion rather than an optional extra. Langfuse documents evaluation-oriented integrations. The cited Helicone homepage establishes tracing and debugging, but not equivalent evaluation depth.
4. Deployment and data control
Compare cloud and self-hosted options, data residency, retention controls, redaction, role-based access, audit requirements, and network paths. Helicone’s homepage advertises self-hosting through a Helm chart; other deployment claims must be checked against the edition and documentation you will actually use.
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At minimum, your monitoring design should expose latency, token usage, errors, model identity, tool timing, and cost attribution. It should let an operator navigate from a user or feature trace to the model span and then into the tool or downstream service that caused the delay. OpenTelemetry can carry the context; the backend determines how easily people can search, alert, and investigate it.
6. Setup and ownership
Native Claude tracing reduces instrumentation work, but an OTLP backend is still required for storage and visualization. Decide who owns exporter configuration, schema changes, retention, access requests, sampling, and incident response. A product that is easy to enable but difficult to govern can become an operational liability at scale.
Can Claude traces go to an existing observability stack?
Yes, when the destination accepts the exporter protocol and attributes emitted by your Claude integration. The documented flow is SDK or API spans in your application, followed by an OpenTelemetry exporter to the selected backend. Before production, verify that the backend accepts OTLP over the transport you intend to use, preserves parent-child relationships, indexes token and latency fields, and supports the retention and access policies you require.
Generic infrastructure dashboards may show that a request was slow, while an LLM-oriented platform may make prompts, generations, tools, and evaluations easier to inspect. Choose based on which investigation is more common for your team, and consider routing standard service spans and specialized LLM views separately if one system cannot do both well.
Common failure modes and fixes
The dashboard is empty
- Confirm that the tracer provider is registered before the Anthropic client is created.
- Confirm that the exporter endpoint, credentials, and transport match the backend.
- Send a request that definitely uses the instrumented client.
- Check exporter logs and flush behavior when the process exits quickly.
Only one span appears for a complex agent run
- Check whether you enabled SDK tracing or only API tracing.
- Verify that the code uses the supported Claude SDK tool runner for tool-use spans.
- Inspect whether your backend is collapsing child spans in its interface.
- Confirm that application-level spans surround retrieval, orchestration, and tool execution.
Prompts or responses are missing
- The SDK default is
metadata_only; content capture must be explicitly selected. - Check the per-span content limit of 0.75 MB.
- Review redaction and destination policies that may remove content after export.
- Do not enable content capture in restricted environments without an approved data policy.
Duplicate traces appear
Duplicates commonly result from enabling both API and SDK tracing or from exporting the same event through a gateway and a direct client path. Decide which layer is authoritative for each request and use correlation fields to distinguish a duplicate representation from a genuine retry.
Tool calls cannot be connected to the user request
Check conversation IDs, trace-context propagation, and parent-child relationships. If propagation was disabled intentionally, add a compliant correlation identifier that does not expose sensitive content. Test server tools, client tools, and self-hosted sandbox tools separately because they may traverse different service boundaries.
A practical rollout plan
- Map the workflow. List every Claude client, API path, agent turn, tool type, and downstream service.
- Start with metadata. Use the default metadata-only mode while validating span names, relationships, token fields, latency, and errors.
- Choose the destination by job. Use an existing OTLP backend for broad service observability, Langfuse for LLM traces plus evaluation workflows, or Helicone when a gateway is the architectural control point.
- Test representative traces. Include a successful request, a timeout or error, a multi-turn conversation, and a tool-using agent run.
- Add content selectively. Capture prompts and responses only for approved environments, teams, and workflows, with documented redaction and retention.
- Define operating thresholds. Alert on latency, error rate, token consumption, and failed tool calls using the fields your backend actually indexes.
- Review access and retention. Limit who can read content-bearing spans and set deletion rules before expanding traffic.
- Revisit the architecture after real usage. Keep instrumentation portable so a change in backend, gateway, or evaluation process does not force a rewrite of Claude integrations.
Which should you choose?
Choose OpenTelemetry first when portability is the priority
Use the standard as your base when you already operate an observability stack, expect backend changes, need traces that join Claude with ordinary services, or want to avoid coupling application code to one LLM product. Remember that you still need a backend for visualization, retention, alerting, access control, and evaluations.
Choose Langfuse when LLM analysis and evaluations are central
Langfuse is the clearest fit when the team needs a dedicated LLM observability workspace, documented Claude and Anthropic integrations, OpenTelemetry ingestion, and evaluation-oriented workflows. It is especially compelling for organizations that want to inspect production generations and connect them to quality improvement rather than only track infrastructure timings.
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Choose Helicone when gateway control is central
Helicone fits a design in which an AI gateway or proxy should mediate requests and provide centralized LLM visibility. Its official materials emphasize Claude support, tracing, agent debugging, and a self-hosted Helm deployment. Validate the exact trace, evaluation, retention, and access features required by your use case instead of assuming they match another LLM platform.
Use a combination when responsibilities are split
Many production teams can use OpenTelemetry for instrumentation and context propagation, then select Langfuse, Helicone, or an existing OTLP backend for storage and analysis. The deciding factors are portability, required agent trace depth, evaluation needs, privacy and deployment constraints, and the quality of token, latency, error, and cost visibility—not the label attached to the product.
Bottom line
OpenTelemetry is the foundation, not the finished monitoring product. Claude’s API and SDK now expose spans for model requests, sessions, token usage, agent turns, and tool activity, so you can build a portable trace pipeline around the official instrumentation. Langfuse is the strongest documented choice for an LLM observability and evaluation workspace. Helicone is the gateway-oriented choice when centralized request routing and gateway-level tracing matter most. Start with OpenTelemetry-compatible Claude instrumentation, verify the trace depth and privacy behavior with representative agent runs, and select the backend that matches your evaluation, deployment, and operational requirements.
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