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The best alternative is usually not another proprietary tracer. Anthropic’s Claude tracing already emits OpenTelemetry (OTel) data, so you can send API and SDK spans to an observability system you already operate, or to an LLM-focused platform such as Langfuse or Braintrust. The right choice depends on whether your priority is low vendor lock-in, prompt and token analysis, evaluation workflows, enterprise operations, or data residency.
Claude tracing supports Messages API requests, tool uses, and Managed Agents sessions. You can enable administrator-controlled API tracing, application-controlled SDK tracing, or both. Start with metadata-only collection, then enable full-content capture only after reviewing privacy, retention, access, and regional requirements.
What Anthropic’s native tracing already does
Anthropic’s official OpenTelemetry documentation describes Claude tracing as an OTel-based system rather than a closed export format. Compatible destinations can receive spans using standard OTLP transport and GenAI semantic-convention attributes, allowing Claude activity to appear beside ordinary service telemetry.
The Tool Desk
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- API tracing: configured by an administrator in the Claude Console. It records Claude API activity without requiring changes to every application that calls the API.
- SDK tracing: configured in application code. It adds spans from the Claude SDK and application-side tool runners, making internal orchestration visible inside the request trace.
Teams can use either layer or both. API tracing is useful for centrally observing requests and server-side tool activity. SDK tracing is useful when you need the application’s own boundaries, retries, tool implementations, and nested operations. Anthropic labels SDK tracing beta and warns that options and span schemas may change; pin the SDK version before building dashboards or queries around them.
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Metadata-only versus full-content mode
Metadata-only mode is the default. It includes identifiers, timing, status, token counts, and selected names or descriptions. Full-content mode includes model prompts and responses. Full content makes prompt debugging and trace-based evaluation possible, but it also places sensitive data under the destination provider’s retention and access controls. Treat this as a data-governance decision, not merely a debugging toggle.
Compare the main alternatives
| Approach | Best fit | Distinct strengths | Important trade-offs |
|---|---|---|---|
| OpenTelemetry plus an existing backend | Teams with a mature APM or telemetry platform | Low lock-in; Claude spans beside logs, metrics, databases, queues, and HTTP calls | LLM-specific prompt search, cost views, and evaluations may require extra work |
| Langfuse | Teams prioritizing LLM-native tracing or self-hosting | Token and cost tracking, prompt linking, scoring, and OTel ingestion | Requires a separate platform and careful server-side key management |
| Braintrust | Teams turning production traces into evaluation data | Trace-to-dataset workflows, scoring, regression checks, and quality measurement | Confirm current SDK coverage and commercial terms for your stack |
| Datadog or another enterprise observability platform | Organizations centered on security, access control, alerting, and operations | Unified dashboards, incident workflows, and existing enterprise controls | Compare LLM-specific search, evaluations, retention, residency, and usage billing |
| Other OTel destinations | Teams already standardized on a particular vendor | Grafana Cloud, Honeycomb, New Relic, Sentry, Splunk, LangSmith, and PostHog are documented or compatible destinations | Capabilities differ; verify current GenAI views and ingestion support |
Option 1: OpenTelemetry with your existing observability backend
This is the lowest-lock-in architecture. Anthropic can export API spans to any OTLP/HTTP endpoint, while SDK spans use the application’s OpenTelemetry provider. Your existing backend then stores and visualizes Claude spans with the rest of the service graph.
When this approach is the right default
- Your operations team already maintains an OTel collector, an APM product, or a centralized telemetry policy.
- Incident responders need one timeline containing Claude calls, application logs, database queries, queue work, and outbound HTTP requests.
- You require existing identity, role-based access, retention, or regional controls.
- You want to preserve the option to change backends without rewriting Claude instrumentation.
What to verify before committing
Confirm that the backend accepts OTLP over HTTP, recognizes GenAI semantic attributes, and can index the fields you need. A backend may ingest spans successfully while offering only basic generic trace views. Ask specifically whether prompts and responses can be searched safely, whether token counts are queryable, and whether trace volume can be sampled without losing the events needed for evaluation.
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Option 2: Langfuse for LLM-native tracing
Langfuse documents integrations for Anthropic and Claude Platform and uses an OpenTelemetry-based architecture. Its OTel-native SDK adds LLM-oriented helpers for token usage, cost tracking, prompt linking, and scoring, while also accepting traces produced by other OTel-instrumented libraries.
Choose Langfuse when
- Prompt versions, generations, token counts, and model costs are first-class operational data.
- Developers need an LLM-specific trace view rather than a generic span tree.
- You want to attach scores or annotations to production generations.
- Self-hosting or a controlled deployment is important enough to justify operating another service.
Server-side deployment is mandatory
Langfuse’s SDK documentation states that tracing SDKs are for server-side code. Secret keys must never be shipped in browser or mobile bundles. Browser-originated Claude calls should go through your backend or a controlled proxy, which can keep credentials private and apply authentication, filtering, and rate controls.
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Before selecting a cloud or self-hosted arrangement, verify its current retention, regional availability, access model, and commercial terms. Those details change independently of Anthropic’s tracing protocol.
Option 3: Braintrust for trace-driven evaluation
Anthropic lists Braintrust as an OTLP destination and describes it as an observability platform for agents. Anthropic also identifies Braintrust among platforms that can turn real traces into evaluations, helping teams test whether a prompt, tool, model, or orchestration change improved quality.
Use Braintrust when quality measurement is the core problem
- Production traces need to become curated datasets.
- Reviewers must score outputs with consistent criteria.
- Teams run regression checks before releasing prompt or tool changes.
- Engineering and product teams need a shared view of quality, not only latency and errors.
Keep generic infrastructure telemetry in your normal OTel system if that is already established, and export the LLM spans or selected trace records needed for evaluation. Confirm the current SDK coverage for your language, framework, and tool-calling pattern before rollout.
Option 4: Datadog and other enterprise observability platforms
Datadog is listed by Anthropic as an OTLP destination. Anthropic’s documentation also points to general-purpose platforms such as Grafana Cloud, Honeycomb, Sentry, and Splunk for debugging Claude traces alongside ordinary logs and metrics.
This route is strongest when your organization values centralized security controls, alerting, dashboards, on-call workflows, and existing procurement over a dedicated LLM user experience. It can reduce operational fragmentation: the team investigating a failed agent run does not need to switch systems to inspect service health and Claude spans.
Questions to ask an enterprise platform
- Can it display GenAI attributes as searchable fields rather than opaque JSON?
- Can authorized users inspect prompts and responses without exposing them to every telemetry user?
- How are sensitive fields redacted before ingestion?
- Can token counts, model names, and tool spans be aggregated by service, tenant, or release?
- Does sampling preserve failed requests and representative full agent runs?
- Are retention, residency, deletion, and audit controls aligned with your policy?
How to choose between the alternatives
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| Decision axis | What a strong answer looks like | Warning sign |
|---|---|---|
| Existing-stack fit | Claude spans appear in the same trace and identity system as your services | Responders must reconstruct a request across unrelated products |
| OTLP support | Native OTLP/HTTP ingestion and documented GenAI attributes | Custom proprietary adapters are required |
| Prompt and response handling | Field-level redaction, restricted access, and controlled indexing | Full content is enabled globally with no policy boundary |
| Token and cost visibility | Input and output usage can be grouped by model, route, tenant, and release | Usage is visible only in raw payloads |
| Evaluation | Scores, datasets, annotations, and regression workflows are built in or easy to add | Quality review remains a manual export process |
| Residency and retention | Documented regions, retention controls, deletion behavior, and access logs | Terms are unclear or vary without notice |
| Volume economics | Sampling and filtering are available before expensive storage | Every span and full payload is retained by default |
Implementation: a production-safe rollout
1. Define the trace boundary
List the events you must explain in a failed or slow request: incoming request, Claude API call, tool selection, tool execution, retries, downstream services, and final response. Decide whether API tracing, SDK tracing, or both are required. API tracing gives centralized request coverage; SDK tracing adds application and tool-runner detail.
2. Start with metadata-only collection
Enable the default metadata-only mode first. Validate identifiers, durations, statuses, token counts, model names, and tool names. This lets you test dashboards and sampling while limiting exposure of user content.
3. Choose an OTLP transport path
Send telemetry to an OTLP/HTTP endpoint over HTTPS on port 443, or place an OpenTelemetry Collector behind a load balancer or reverse proxy as Anthropic documents. A collector can centralize authentication, redaction, batching, routing, and sampling before data reaches one or more backends.
4. Add full content selectively
Enable prompts and responses only for approved services, environments, tenants, or sampled requests. Document what content may contain personal, confidential, regulated, or security-sensitive data. Restrict who can view it and define deletion procedures with the destination provider.
5. Keep credentials off client devices
Tracing keys and destination credentials belong in server-side configuration or a secret manager. Never place them in browser JavaScript, mobile application bundles, or publicly readable configuration. Route client requests through a backend or controlled proxy.
6. Pin the Claude SDK version
Because SDK tracing is beta, pin the SDK version in production. Treat an upgrade as a schema migration: review changed span names and attributes, run a test trace, inspect dashboards and queries, and only then promote the new version.
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7. Test the failure paths
- Claude request timeout or transport failure
- Tool invocation rejected by validation
- Tool execution timeout or downstream error
- Retry that creates duplicate or nested spans
- Streaming response interrupted midway
- Trace exporter unavailable while the user request must continue
Decide whether telemetry export is fail-open or fail-closed. In most user-facing systems, an observability outage should not prevent the application from completing a valid Claude request.
Tracing tool calls and token usage in production
A useful production trace shows the causal chain, not just a Claude request duration. The parent request span should contain the Claude operation. Child spans should identify tool selection and execution, including the tool name, start and end times, status, and downstream calls. Keep tool inputs and outputs in metadata-only form unless full content is explicitly approved.
Token counts should be recorded from the Claude response attributes exposed by the tracing integration, then aggregated by model, endpoint, application version, customer or tenant boundary, and time period. Use those dimensions to detect unexpected prompt growth, runaway tool loops, or a release that increases output length. Do not invent a cost figure from token counts alone; pricing and billing rules are separate, volatile inputs that must be verified for your account and date.
Privacy, retention, and access design
Sending a trace to a third party transfers responsibility for the copied data. Metadata-only mode reduces exposure but does not eliminate it: identifiers, names, descriptions, timing, and usage can still be sensitive. Full-content mode may contain user messages, proprietary instructions, credentials accidentally included in prompts, or personal information returned by tools.
- Classify prompt and response content before enabling full capture.
- Redact secrets and direct identifiers at the collector or application boundary.
- Separate production content from development traces and test fixtures.
- Use least-privilege roles for trace viewers and evaluators.
- Set retention and deletion rules that match contractual and regulatory requirements.
- Confirm where data is stored and processed, including backup and support access.
Common failure modes and fixes
Only top-level Claude requests appear
API tracing may be enabled while SDK tracing is absent, or the application’s OpenTelemetry provider may not be exporting child spans. Verify both layers and ensure the SDK provider is initialized before the Claude client and tool runner execute.
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Spans arrive but fields are unusable
The backend may accept OTLP but fail to map GenAI attributes into searchable fields. Inspect a raw span, check semantic-convention support, and add a collector transformation only where necessary.
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Prompts are missing from a debugging trace
Metadata-only mode intentionally omits them. Enable full content only for a controlled scope, then verify redaction and access policy before expanding coverage.
Dashboards break after an SDK upgrade
Tracing is beta and span schemas can change. Roll back to the pinned version, compare the old and new schemas in a test environment, and update queries deliberately.
Telemetry credentials are exposed in a client bundle
Remove the credential, rotate it, and move export behind a server-side backend or controlled proxy. Treat a published key as compromised even if no misuse is visible.
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What is not established by the available documentation
Anthropic’s cited materials describe capabilities and setup patterns, not independent benchmarks. They do not establish universal latency improvements, adoption rates, accuracy scores, quotas, retention periods, regional availability, service-level commitments, or current pricing for the listed destinations. Those values vary by provider, plan, region, and date and must be checked directly before procurement.
Frequently Asked Questions
Can I send Claude traces to more than one backend?
Yes. Use an OpenTelemetry Collector or another routing layer to export selected spans to multiple OTLP destinations, while applying separate redaction, sampling, and retention rules for each destination.
Do I need both API tracing and SDK tracing?
No. Use API tracing for centralized request coverage, SDK tracing for application and tool-runner detail, or both when you need complete end-to-end context.
Is full-content tracing required to measure token usage?
No. Metadata-only mode includes token counts. Full-content mode is needed for inspecting prompts and responses, not for basic usage accounting.
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Anthropic labels SDK tracing beta and warns that options and span schemas may change. Pinning prevents unexpected dashboard and query breakage while you validate upgrades.
The Bottom Line
For most teams, keep Claude tracing on OpenTelemetry and send it to the backend you already secure and operate. Add Langfuse when prompt, token, cost, and scoring workflows are central; choose Braintrust when trace-driven evaluation is the primary goal; and favor an enterprise APM platform when unified operations and governance matter most. Roll out metadata first, protect credentials on the server, and treat full-content capture as a controlled data-processing decision.
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