Monitoring Amazon Bedrock with SigNoz

Overview

This guide walks you through setting up monitoring for Amazon Bedrock 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 Bedrock applications.

Many developers choose Amazon Bedrock over directly calling LLM models for its enterprise-grade features including unified API access to multiple foundation models (Claude, Llama, Titan, etc.), built-in safeguards for responsible AI, private and secure model invocations that don't leave AWS infrastructure, managed infrastructure that eliminates the need to manage model hosting, fine-tuning capabilities with your own data while maintaining privacy, and seamless integration with AWS services like S3, Lambda, and CloudWatch. These capabilities make Amazon Bedrock particularly valuable for organizations requiring production-grade reliability, compliance, and simplified model management without the complexity of direct model deployment.

Instrumenting Amazon Bedrock 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 AWS account with Amazon Bedrock working and access granted for LLM models
For Python: pip installed for managing Python packages and (optional but recommended) a Python virtual environment to isolate dependencies

Monitoring Amazon Bedrock

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

pip install \
  opentelemetry-api \
  opentelemetry-sdk \
  opentelemetry-exporter-otlp \
  opentelemetry-instrumentation-httpx \
  opentelemetry-instrumentation-system-metrics \
  openinference-instrumentation-bedrock \
  boto3

Step 2: Import the necessary modules in your Python application

Traces:

from openinference.instrumentation.bedrock import BedrockInstrumentor
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 Bedrock using BedrockInstrumentor and the configured Tracer Provider

from openinference.instrumentation.bedrock import BedrockInstrumentor

BedrockInstrumentor().instrument()

📌 Important: Place this code at the start of your application logic — before any Bedrock functions are called or used — to ensure telemetry is correctly captured.

Step 5: Setup Logs

import logging
from opentelemetry.sdk.resources import Resource
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

resource = Resource.create({"service.name": "<service_name>"})
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

📌 Note: No logs are automatically emitted by the BedrockInstrumentor. If you would like logs, you need to emit them manually via logger.info() or other logging methods.

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 Bedrock-specific metrics. Bedrock does not expose metrics as part of their SDK. If you want to add custom metrics to your Bedrock application, see Python Custom Metrics.

Step 7: Run an example

import boto3
import os
 
bedrock = boto3.client(
    service_name="bedrock-runtime",
    aws_access_key_id=os.getenv("AWS_ACCESS"), aws_secret_access_key=os.getenv("AWS_SECRET"),
    region_name="us-east-1"  # or your region
)
model_id = "us.anthropic.claude-sonnet-4-5-20250929-v1:0"

prompt = "What is SigNoz?"
logger.info(f"Sending prompt to model {model_id}: {prompt}") #sample log
body = {
    "anthropic_version": "bedrock-2023-05-31",
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": prompt}
            ]
        }
    ],
    "max_tokens": 512,
    "temperature": 0.7
}
response = bedrock.invoke_model(
    modelId=model_id,
    body=json.dumps(body)
)

response_body = json.loads(response['body'].read())

output_text = response_body['content'][0]['text']

print("Model output:\n", output_text)

📌 Note: Before running this code, ensure that you have set the environment variables AWS_ACCESS and AWS_SECRET with your AWS credentials.

View Traces, Logs, and Metrics in SigNoz

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

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

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.

You should be able to view logs in Signoz Cloud under the logs tab. You can also view correlated logs by clicking on the “Related Logs” button in the trace view to see correlated logs:

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

Bedrock Detailed Log View — *Amazon Bedrock Detailed Logs View*

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

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

Dashboard

You can also check out our custom Amazon Bedrock dashboard here which provides specialized visualizations for monitoring your Bedrock usage in applications. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.