Monitoring Anthropic API with SigNoz

Overview

This guide walks you through setting up monitoring for Anthropic API using OpenTelemetry and exporting logs, traces, and metrics to SigNoz. With this integration, you can observe model performance, capture request/response details, and track system-level metrics in SigNoz, giving you real-time visibility into latency, error rates, and usage trends for your Anthropic applications.

Instrumenting Anthropic in your LLM applications with telemetry ensures full observability across your AI workflows, making it easier to debug issues, optimize performance, and understand user interactions. By leveraging SigNoz, you can analyze correlated traces, logs, and metrics in unified dashboards, configure alerts, and gain actionable insights to continuously improve reliability, responsiveness, and user experience.

Prerequisites

A SigNoz Cloud account with an active ingestion key
Internet access to send telemetry data to SigNoz Cloud
An Anthropic API account with a working API Key
pip installed for managing Python packages
(Optional but recommended) A Python virtual environment to isolate dependencies

Monitoring Anthropic

Step 1: Install the necessary packages in your Python environment.

pip install \
  anthropic \
  opentelemetry-api \
  opentelemetry-sdk \
  opentelemetry-exporter-otlp \
  opentelemetry-instrumentation-httpx \
  opentelemetry-instrumentation-system-metrics \
  openinference-instrumentation-anthropic

Step 2: Import the necessary modules in your Python application

Traces:

from openinference.instrumentation.anthropic import AnthropicInstrumentor
from opentelemetry import trace
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

Logs:

from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
from opentelemetry._logs import set_logger_provider
import logging

Metrics:

from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry import metrics
from opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor

Step 3: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud

from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry import trace
import os

resource = Resource.create({"service.name": "<service_name>"})
provider = TracerProvider(resource=resource)
span_exporter = OTLPSpanExporter(
    endpoint= os.getenv("OTEL_EXPORTER_TRACES_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
processor = BatchSpanProcessor(span_exporter)
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)

<service_name> is the name of your service
OTEL_EXPORTER_TRACES_ENDPOINT → SigNoz Cloud trace endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/traces
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

Step 4: Instrument Anthropic using AnthropicInstrumentor and the configured Tracer Provider

from openinference.instrumentation.anthropic import AnthropicInstrumentor

AnthropicInstrumentor().instrument(tracer_provider=provider)

📌 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.

Step 5: Setup Logs

import logging
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from opentelemetry.exporter.otlp.proto.http._log_exporter import OTLPLogExporter
import os

logger_provider = LoggerProvider(resource=resource)
set_logger_provider(logger_provider)

otlp_log_exporter = OTLPLogExporter(
    endpoint= os.getenv("OTEL_EXPORTER_LOGS_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
logger_provider.add_log_record_processor(
    BatchLogRecordProcessor(otlp_log_exporter)
)
# Attach OTel logging handler to root logger
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.basicConfig(level=logging.INFO, handlers=[handler])

logger = logging.getLogger(__name__)

<service_name> is the name of your service
OTEL_EXPORTER_LOGS_ENDPOINT → SigNoz Cloud endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/logs
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

Step 6: Setup Metrics

from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from opentelemetry import metrics
from opentelemetry.instrumentation.system_metrics import SystemMetricsInstrumentor
import os

resource = Resource.create({"service.name": "<service-name>"})
metric_exporter = OTLPMetricExporter(
    endpoint= os.getenv("OTEL_EXPORTER_METRICS_ENDPOINT"),
    headers={"signoz-ingestion-key": os.getenv("SIGNOZ_INGESTION_KEY")},
)
reader = PeriodicExportingMetricReader(metric_exporter)
metric_provider = MeterProvider(metric_readers=[reader], resource=resource)
metrics.set_meter_provider(metric_provider)

meter = metrics.get_meter(__name__)

# turn on out-of-the-box metrics
SystemMetricsInstrumentor().instrument()
HTTPXClientInstrumentor().instrument()

<service_name> is the name of your service
OTEL_EXPORTER_METRICS_ENDPOINT → SigNoz Cloud endpoint with appropriate region:https://ingest.<region>.signoz.cloud:443/v1/metrics
SIGNOZ_INGESTION_KEY → Your SigNoz ingestion key

📌 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 Python Custom Metrics.

Step 7: Run an example

import anthropic
 
client = anthropic.Anthropic()
message = client.messages.create(
    model="claude-3-7-sonnet-20250219",
    max_tokens=1000,
    messages=[
        {
            "role": "user",
            "content": "What is signoz"
        }
    ]
)
print(message.content)

📌 Note: Before running this code, ensure that you have set the environment variable ANTHROPIC_API_KEY with your generated API key.

View Traces, Logs, and Metrics in SigNoz

Your Anthropic commands should now automatically emit traces, logs, and metrics.

You should be able to view traces in Signoz Cloud under the traces tab:

Anthropic Trace View — *Anthropic API Trace View*

When 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.

Anthropic Detailed Trace View — *Detailed view of Anthropic API traces*

You 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:

Anthropic Log View — *Anthropic API logs displayed in SigNoz logs tab*

When you click on any of these logs in SigNoz, you'll see a detailed view of the log, including attributes:

Anthropic Detailed Log View — *Detailed view of Anthropic API log*

You should be able to see Anthropic related metrics in Signoz Cloud under the metrics tab:

Anthropic Metrics — *Anthropic API metrics displayed in SigNoz metrics tab*

When you click on any of these metrics in SigNoz, you'll see a detailed view of the metric, including attributes:

Anthropic Detailed Metrics — *Detailed view of Anthropic API metrics with attributes*

Dashboard

You can also check out our custom Anthropic API dashboard here 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.