Amazon Bedrock Monitoring and Observability with OpenTelemetry

Amazon Bedrock users can now monitor model performance, latency, error rates, and usage trends by integrating OpenTelemetry with SigNoz. The open-source observability framework exports logs, traces, and metrics from Bedrock applications to SigNoz dashboards, enabling real-time debugging and optimization of AI workflows. This integration provides unified visibility into request/response details and system-level metrics without requiring direct model deployment management.

Overview This guide walks you through setting up monitoring and observability for Amazon Bedrock using OpenTelemetry https://opentelemetry.io/ 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 - SigNoz setup choose one : SigNoz Cloud account https://signoz.io/teams/ with an active ingestion key- Self-hosted SigNoz instance - Internet access to send telemetry data to SigNoz Cloud - An AWS account with Amazon Bedrock https://aws.amazon.com/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 No-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. Step 1: Install the necessary packages in your Python environment. pip install \ opentelemetry-distro \ opentelemetry-exporter-otlp \ opentelemetry-instrumentation-httpx \ opentelemetry-instrumentation-system-metrics \ openinference-instrumentation-bedrock \ boto3 Step 2: Add Automatic Instrumentation opentelemetry-bootstrap --action=install Step 3: Configure logging level To ensure logs are properly captured and exported, configure the root logger to emit logs at the INFO level or higher: python import logging logging.getLogger .setLevel logging.INFO This sets the minimum log level for the root logger to INFO, which ensures that logger.info calls and higher severity logs WARNING, ERROR, CRITICAL are captured by the OpenTelemetry logging auto-instrumentation and sent to SigNoz. Step 4: Run an example python import boto3 import os import logging logging.getLogger .setLevel logging.INFO logger = logging.getLogger name 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: 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 5: Run your application with auto-instrumentation OTEL RESOURCE ATTRIBUTES="service.name=<service name " \ OTEL EXPORTER OTLP ENDPOINT="https://ingest.<region .signoz.cloud:443" \ OTEL EXPORTER OTLP HEADERS="signoz-ingestion-key=<your-ingestion-key " \ OTEL EXPORTER OTLP PROTOCOL=grpc \ OTEL TRACES EXPORTER=otlp \ OTEL METRICS EXPORTER=otlp \ OTEL LOGS EXPORTER=otlp \ OTEL PYTHON LOG CORRELATION=true \ OTEL PYTHON LOGGING AUTO INSTRUMENTATION ENABLED=true \ opentelemetry-instrument <your run command is the name of your service <service name <region : Your SigNoz Cloud region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint <your-ingestion-key : Your SigNoz ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ - Replace <your run command with the actual command you would use to run your application. For example: python main.py Using 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 . Code-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. 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: python 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: python 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: python 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 python 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 is the name of your service <service name → SigNoz Cloud trace endpoint with appropriate OTEL EXPORTER TRACES ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/traces → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ Using 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 . Step 4: Instrument Bedrock using BedrockInstrumentor and the configured Tracer Provider python 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 python 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 is the name of your service <service name → SigNoz Cloud endpoint with appropriate OTEL EXPORTER LOGS ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/logs → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ 📌 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. Using 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 . Step 6 : Setup Metrics python 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 is the name of your service <service name → SigNoz Cloud endpoint with appropriate OTEL EXPORTER METRICS ENDPOINT region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint : https://ingest.<region .signoz.cloud:443/v1/metrics → Your SigNoz SIGNOZ INGESTION KEY ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ 📌 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 . Using 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 . Step 7: Run an example python 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: 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 https://signoz.io/docs/dashboards/dashboard-templates/amazon-bedrock-dashboard/ 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.