{"slug": "anthropic-monitoring-observability-with-opentelemetry", "title": "Anthropic Monitoring & Observability with OpenTelemetry", "summary": "Anthropic has released a monitoring guide for its Claude API that integrates with OpenTelemetry and SigNoz, enabling developers to track token usage, request latency, error rates, and costs across model calls. The setup provides correlated traces, logs, and metrics in unified dashboards, allowing teams to identify slow responses, detect rate limit errors, and configure alerts before issues affect users. The guide includes step-by-step instructions for both no-code auto-instrumentation and code-based instrumentation using Python.", "body_md": "Why Monitor the Anthropic API?\n\nAnthropic monitoring gives you production-level visibility into your Claude API applications, tracking token usage, request latency, error rates, and costs across every model call. This guide shows you how to instrument Anthropic Claude with [OpenTelemetry](https://opentelemetry.io/) and export traces, logs, and metrics to SigNoz, so you can observe model performance and debug issues in real time.\n\nWith this setup, SigNoz gives you correlated traces, logs, and metrics in unified dashboards, making it straightforward to identify slow Claude API responses, detect rate limit errors, track per-request token consumption, and configure alerts before issues affect your users.\n\nPrerequisites\n\n- SigNoz setup (choose one):\n[SigNoz Cloud account](https://signoz.io/teams/)with an active ingestion key- Self-hosted SigNoz instance\n\n- Internet access to send telemetry data to SigNoz Cloud\n- An\n[Anthropic](https://docs.anthropic.com/en/home)API account with a working API Key `pip`\n\ninstalled for managing Python packages*(Optional but recommended)*A Python virtual environment to isolate dependencies\n\nMonitoring Anthropic\n\nNo-code auto-instrumentation is recommended for quick setup with minimal code changes. It's ideal when you want to get observability up and running without modifying your application code and are leveraging standard instrumentor libraries.\n\n**Step 1:** Install the necessary packages in your Python environment.\n\n```\npip install \\\n  opentelemetry-distro \\\n  opentelemetry-exporter-otlp \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics \\\n  openinference-instrumentation-anthropic\n```\n\n**Step 2:** Add Automatic Instrumentation\n\n```\nopentelemetry-bootstrap --action=install\n```\n\n**Step 3:** Configure logging level\n\nTo ensure logs are properly captured and exported, configure the root logger to emit logs at the INFO level or higher:\n\n``` python\nimport logging\nlogging.getLogger().setLevel(logging.INFO)\n```\n\nThis sets the minimum log level for the root logger to INFO, which ensures that `logger.info()`\n\ncalls and higher severity logs (WARNING, ERROR, CRITICAL) are captured by the OpenTelemetry logging auto-instrumentation and sent to SigNoz.\n\n**Step 4:** Run an example\n\n``` python\nimport anthropic\n\nclient = anthropic.Anthropic()\nmessage = client.messages.create(\n    model=\"claude-3-7-sonnet-20250219\",\n    max_tokens=1000,\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"What is signoz\"\n        }\n    ]\n)\nprint(message.content)\n```\n\n📌 Note: Before running this code, ensure that you have set the environment variable\n\n`ANTHROPIC_API_KEY`\n\nwith your generated API key.\n\n**Step 5:** Run your application with auto-instrumentation\n\n```\nOTEL_RESOURCE_ATTRIBUTES=\"service.name=<service_name>\" \\\nOTEL_EXPORTER_OTLP_ENDPOINT=\"https://ingest.<region>.signoz.cloud:443\" \\\nOTEL_EXPORTER_OTLP_HEADERS=\"signoz-ingestion-key=<your-ingestion-key>\" \\\nOTEL_EXPORTER_OTLP_PROTOCOL=grpc \\\nOTEL_TRACES_EXPORTER=otlp \\\nOTEL_METRICS_EXPORTER=otlp \\\nOTEL_LOGS_EXPORTER=otlp \\\nOTEL_PYTHON_LOG_CORRELATION=true \\\nOTEL_PYTHON_LOGGING_AUTO_INSTRUMENTATION_ENABLED=true \\\nopentelemetry-instrument <your_run_command>\n```\n\nis the name of your service`<service_name>`\n\n`<region>`\n\n: Your[SigNoz Cloud region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint)`<your-ingestion-key>`\n\n: Your SigNoz[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)- Replace\n`<your_run_command>`\n\nwith the actual command you would use to run your application. For example:`python main.py`\n\nUsing self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\nCode-based instrumentation gives you fine-grained control over your telemetry configuration. Use this approach when you need to customize resource attributes, sampling strategies, or integrate with existing observability infrastructure.\n\n**Step 1:** Install the necessary packages in your Python environment.\n\n```\npip install \\\n  anthropic \\\n  opentelemetry-api \\\n  opentelemetry-sdk \\\n  opentelemetry-exporter-otlp \\\n  opentelemetry-instrumentation-httpx \\\n  opentelemetry-instrumentation-system-metrics \\\n  openinference-instrumentation-anthropic\n```\n\n**Step 2:** Import the necessary modules in your Python application\n\n**Traces:**\n\n``` python\nfrom openinference.instrumentation.anthropic import AnthropicInstrumentor\nfrom opentelemetry import trace\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\n```\n\n**Logs:**\n\n``` python\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter\nfrom opentelemetry._logs import set_logger_provider\nimport logging\n```\n\n**Metrics:**\n\n``` python\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry import metrics\nfrom opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor\nfrom opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor\n```\n\n**Step 3:** Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud\n\n``` python\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter\nfrom opentelemetry import trace\nimport os\n\nresource = Resource.create({\"service.name\": \"<service_name>\"})\nprovider = TracerProvider(resource=resource)\nspan_exporter = OTLPSpanExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_TRACES_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nprocessor = BatchSpanProcessor(span_exporter)\nprovider.add_span_processor(processor)\ntrace.set_tracer_provider(provider)\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud trace endpoint with appropriate`OTEL_EXPORTER_TRACES_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/traces`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n**Step 4:** Instrument Anthropic using `AnthropicInstrumentor`\n\nand the configured Tracer Provider\n\n``` python\nfrom openinference.instrumentation.anthropic import AnthropicInstrumentor\n\nAnthropicInstrumentor().instrument(tracer_provider=provider)\n```\n\n📌 Important: Place this code at the start of your application logic — before any Anthropic functions are called or used — to ensure telemetry is correctly captured.\n\n**Step 5**: Setup Logs\n\n``` python\nimport logging\nfrom opentelemetry._logs import set_logger_provider\nfrom opentelemetry.sdk._logs import LoggerProvider, LoggingHandler\nfrom opentelemetry.sdk._logs.export import BatchLogRecordProcessor\nfrom opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter\nimport os\n\nlogger_provider = LoggerProvider(resource=resource)\nset_logger_provider(logger_provider)\n\notlp_log_exporter = OTLPLogExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_LOGS_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nlogger_provider.add_log_record_processor(\n    BatchLogRecordProcessor(otlp_log_exporter)\n)\n# Attach OTel logging handler to root logger\nhandler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)\nlogging.basicConfig(level=logging.INFO, handlers=[handler])\n\nlogger = logging.getLogger(__name__)\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_LOGS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/logs`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n**Step 6**: Setup Metrics\n\n``` python\nfrom opentelemetry.sdk.resources import Resource\nfrom opentelemetry.sdk.metrics import MeterProvider\nfrom opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter\nfrom opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader\nfrom opentelemetry import metrics\nfrom opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor\nimport os\n\nresource = Resource.create({\"service.name\": \"<service-name>\"})\nmetric_exporter = OTLPMetricExporter(\n    endpoint= os.getenv(\"OTEL_EXPORTER_METRICS_ENDPOINT\"),\n    headers={\"signoz-ingestion-key\": os.getenv(\"SIGNOZ_INGESTION_KEY\")},\n)\nreader = PeriodicExportingMetricReader(metric_exporter)\nmetric_provider = MeterProvider(metric_readers=[reader], resource=resource)\nmetrics.set_meter_provider(metric_provider)\n\nmeter = metrics.get_meter(__name__)\n\n# turn on out-of-the-box metrics\nSystemMetricsInstrumentor().instrument()\nHTTPXClientInstrumentor().instrument()\n```\n\nis the name of your service`<service_name>`\n\n→ SigNoz Cloud endpoint with appropriate`OTEL_EXPORTER_METRICS_ENDPOINT`\n\n[region](https://signoz.io/docs/ingestion/signoz-cloud/overview/#endpoint):`https://ingest.<region>.signoz.cloud:443/v1/metrics`\n\n→ Your SigNoz`SIGNOZ_INGESTION_KEY`\n\n[ingestion key](https://signoz.io/docs/ingestion/signoz-cloud/keys/)\n\nUsing self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in [Cloud → Self-Hosted](https://signoz.io/docs/ingestion/cloud-vs-self-hosted/#cloud-to-self-hosted).\n\n📌 Note: SystemMetricsInstrumentor provides system metrics (CPU, memory, etc.), and HTTPXClientInstrumentor provides outbound HTTP request metrics such as request duration. These are not Anthropic-specific metrics. Anthropic does not expose metrics as part of their SDK. If you want to add custom metrics to your Anthropic application, see\n\n[Python Custom Metrics].\n\n**Step 7:** Run an example\n\n``` python\nimport anthropic\n\nclient = anthropic.Anthropic()\nmessage = client.messages.create(\n    model=\"claude-3-7-sonnet-20250219\",\n    max_tokens=1000,\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"What is signoz\"\n        }\n    ]\n)\nprint(message.content)\n```\n\n📌 Note: Before running this code, ensure that you have set the environment variable\n\n`ANTHROPIC_API_KEY`\n\nwith your generated API key.\n\nView Anthropic API Traces, Logs & Metrics in SigNoz\n\nYour Anthropic commands should now automatically emit traces, logs, and metrics.\n\nYou should be able to view traces in Signoz Cloud under the traces tab:\n\nWhen you click on a trace in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes.\n\nYou should be able to view logs in Signoz Cloud under the logs tab. You can also view logs by clicking on the “Related Logs” button in the trace view to see correlated logs:\n\nWhen you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:\n\nYou should be able to see Anthropic related metrics in Signoz Cloud under the metrics tab:\n\nWhen you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes:\n\nAnthropic API Monitoring Dashboard\n\nYou can also check out our custom Anthropic API dashboard [here](https://signoz.io/docs/dashboards/dashboard-templates/anthropic-dashboard/) which provides specialized visualizations for monitoring your Anthropic API usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.", "url": "https://wpnews.pro/news/anthropic-monitoring-observability-with-opentelemetry", "canonical_source": "https://signoz.io/docs/anthropic-monitoring", "published_at": "2026-05-21 00:00:00+00:00", "updated_at": "2026-05-26 14:18:01.659631+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "mlops", "ai-tools", "ai-infrastructure"], "entities": ["Anthropic", "Claude", "OpenTelemetry", "SigNoz", "Python"], "alternates": {"html": "https://wpnews.pro/news/anthropic-monitoring-observability-with-opentelemetry", "markdown": "https://wpnews.pro/news/anthropic-monitoring-observability-with-opentelemetry.md", "text": "https://wpnews.pro/news/anthropic-monitoring-observability-with-opentelemetry.txt", "jsonld": "https://wpnews.pro/news/anthropic-monitoring-observability-with-opentelemetry.jsonld"}}