DeepSeek Monitoring & Observability with OpenTelemetry

SigNoz has released a new guide for monitoring DeepSeek API calls using OpenTelemetry, enabling developers to track model latency, error rates, and token usage in unified dashboards. The integration allows users to instrument the DeepSeek API with OpenTelemetry and export traces, logs, and metrics to SigNoz for end-to-end observability. This setup helps teams debug issues, optimize performance, and improve reliability across AI workflows.

What is DeepSeek Monitoring? DeepSeek monitoring with OpenTelemetry gives you full visibility into your DeepSeek API calls. This guide walks you through instrumenting the DeepSeek API with OpenTelemetry https://opentelemetry.io/ and exporting traces, logs, and metrics to SigNoz, so you can track model latency, error rates, and token usage in one place. With full DeepSeek monitoring in SigNoz, you can correlate traces, logs, and metrics in unified dashboards, configure alerts on latency and error rates, and gain actionable insights to continuously improve the reliability and responsiveness of your DeepSeek applications. This end-to-end DeepSeek observability makes it easier to debug issues, optimize performance, and understand user interactions across your AI workflows. 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 - A DeepSeek https://api-docs.deepseek.com/ API account with a working API Key - For Python: pip installed for managing Python packages and optional but recommended a Python virtual environment to isolate dependencies - For JavaScript: Node.js version 14 or higher and npm installed for managing Node.js packages Monitoring DeepSeek with OpenTelemetry The DeepSeek API uses an API format compatible with OpenAI. By modifying the configuration, you can use the OpenAI SDK or software compatible with the OpenAI API to access the DeepSeek API. Hence, a similar method to monitor OpenAI APIs can be used for monitoring DeepSeek APIs as well. To read more about this, you can read the DeepSeek API Docs https://api-docs.deepseek.com/ 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 \ openai \ openinference-instrumentation-openai 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 logging.getLogger "httpx" .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 openai import os client = OpenAI api key=os.getenv "DEEPSEEK API KEY" , base url="https://api.deepseek.com" response = client.chat.completions.create model="deepseek-chat", messages= {"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "What is SigNoz?"}, , stream=False print response.choices 0 .message.content 📌 Note: Before running this code, ensure that you have set the environment variable DEEPSEEK API KEY with your generated API key. 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 \ openai \ openinference-instrumentation-openai Step 2: Import the necessary modules in your Python application Traces: python from openinference.instrumentation.openai import OpenAIInstrumentor 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 DeepSeek using OpenAInstrumentor and the configured Tracer Provider python from openinference.instrumentation.openai import OpenAIInstrumentor OpenAIInstrumentor .instrument tracer provider=provider 📌 Important: Place this code at the start of your application logic, before any DeepSeek 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/ 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 DeepSeek-specific metrics. DeepSeek does not expose metrics as part of their SDK. If you want to add custom metrics to your DeepSeek 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 openai import os client = OpenAI api key=os.getenv "DEEPSEEK API KEY" , base url="https://api.deepseek.com" response = client.chat.completions.create model="deepseek-chat", messages= {"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "What is SigNoz?"}, , stream=False print response.choices 0 .message.content 📌 Note: Before running this code, ensure that you have set the environment variable DEEPSEEK API KEY with your generated API key. Step 1: Install the necessary packages in your Node.js project. npm install \ @opentelemetry/api \ @opentelemetry/sdk-node \ @opentelemetry/sdk-trace-node \ @opentelemetry/sdk-logs \ @opentelemetry/sdk-metrics \ @opentelemetry/exporter-otlp-http \ @opentelemetry/instrumentation \ @opentelemetry/instrumentation-http \ @opentelemetry/host-metrics \ @opentelemetry/resources \ @opentelemetry/semantic-conventions \ @arizeai/openinference-instrumentation-openai \ openai Step 2: Import the necessary modules in your JavaScript/Node.js application Traces: js const { NodeSDK } = require '@opentelemetry/sdk-node' const { Resource } = require '@opentelemetry/resources' const { ATTR SERVICE NAME } = require '@opentelemetry/semantic-conventions' const { NodeTracerProvider } = require '@opentelemetry/sdk-trace-node' const { BatchSpanProcessor } = require '@opentelemetry/sdk-trace-base' const { OTLPTraceExporter } = require '@opentelemetry/exporter-otlp-http' const { registerInstrumentations } = require '@opentelemetry/instrumentation' const { OpenAIInstrumentation } = require '@arizeai/openinference-instrumentation-openai' Logs: js const { LoggerProvider, BatchLogRecordProcessor } = require '@opentelemetry/sdk-logs' const { OTLPLogExporter } = require '@opentelemetry/exporter-otlp-http' const { logs } = require '@opentelemetry/api' Metrics: js const { MeterProvider, PeriodicExportingMetricReader } = require '@opentelemetry/sdk-metrics' const { OTLPMetricExporter } = require '@opentelemetry/exporter-otlp-http' const { HttpInstrumentation } = require '@opentelemetry/instrumentation-http' const { HostMetrics } = require '@opentelemetry/host-metrics' const { metrics } = require '@opentelemetry/api' Step 3: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud js const { NodeTracerProvider } = require '@opentelemetry/sdk-trace-node' const { BatchSpanProcessor } = require '@opentelemetry/sdk-trace-base' const { OTLPTraceExporter } = require '@opentelemetry/exporter-otlp-http' const { Resource } = require '@opentelemetry/resources' const { ATTR SERVICE NAME } = require '@opentelemetry/semantic-conventions' const { trace } = require '@opentelemetry/api' const resource = Resource.default { attributes: { ATTR SERVICE NAME : '<service name ', }, } const provider = new NodeTracerProvider { resource: resource, } const traceExporter = new OTLPTraceExporter { url: process.env.OTEL EXPORTER TRACES ENDPOINT, headers: { 'signoz-ingestion-key': process.env.SIGNOZ INGESTION KEY, }, } const spanProcessor = new BatchSpanProcessor traceExporter provider.addSpanProcessor spanProcessor provider.register 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 DeepSeek using OpenAIInstrumentation and the configured Tracer Provider js const { registerInstrumentations } = require '@opentelemetry/instrumentation' const { OpenAIInstrumentation } = require '@arizeai/openinference-instrumentation-openai' registerInstrumentations { instrumentations: new OpenAIInstrumentation { tracerProvider: provider, } , , } 📌 Important: Place this code at the start of your application logic, before any DeepSeek functions are called or used, to ensure telemetry is correctly captured. Step 5 : Setup Logs js const { LoggerProvider, BatchLogRecordProcessor } = require '@opentelemetry/sdk-logs' const { OTLPLogExporter } = require '@opentelemetry/exporter-otlp-http' const { Resource } = require '@opentelemetry/resources' const { ATTR SERVICE NAME } = require '@opentelemetry/semantic-conventions' const { logs } = require '@opentelemetry/api' const logResource = Resource.default { attributes: { ATTR SERVICE NAME : '<service name ', }, } const loggerProvider = new LoggerProvider { resource: logResource, } const logExporter = new OTLPLogExporter { url: process.env.OTEL EXPORTER LOGS ENDPOINT, headers: { 'signoz-ingestion-key': process.env.SIGNOZ INGESTION KEY, }, } loggerProvider.addLogRecordProcessor new BatchLogRecordProcessor logExporter logs.setGlobalLoggerProvider loggerProvider // Create a logger instance const logger = logs.getLogger 'deepseek-app' 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/ 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 js const { MeterProvider, PeriodicExportingMetricReader } = require '@opentelemetry/sdk-metrics' const { OTLPMetricExporter } = require '@opentelemetry/exporter-otlp-http' const { Resource } = require '@opentelemetry/resources' const { ATTR SERVICE NAME } = require '@opentelemetry/semantic-conventions' const { metrics } = require '@opentelemetry/api' const { HttpInstrumentation } = require '@opentelemetry/instrumentation-http' const { registerInstrumentations } = require '@opentelemetry/instrumentation' const metricResource = Resource.default { attributes: { ATTR SERVICE NAME : '<service name ', }, } const metricExporter = new OTLPMetricExporter { url: process.env.OTEL EXPORTER METRICS ENDPOINT, headers: { 'signoz-ingestion-key': process.env.SIGNOZ INGESTION KEY, }, } const metricReader = new PeriodicExportingMetricReader { exporter: metricExporter, exportIntervalMillis: 10000, } const meterProvider = new MeterProvider { resource: metricResource, readers: metricReader , } metrics.setGlobalMeterProvider meterProvider // Create a meter instance const meter = metrics.getMeter 'deepseek-app' // Register HTTP instrumentation for outbound HTTP metrics registerInstrumentations { instrumentations: new HttpInstrumentation , } // Initialize host metrics for system-level monitoring const hostMetrics = new HostMetrics { meterProvider: meterProvider, } hostMetrics.start 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/ 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 . 📌 Note: HostMetrics provides system-level metrics CPU, memory, disk, network, etc. , and HttpInstrumentation provides outbound HTTP request metrics such as request duration. These are not DeepSeek-specific metrics. DeepSeek does not expose metrics as part of their SDK. If you want to add custom metrics to your DeepSeek application, see JavaScript Custom Metrics . Step 7: Run an example js const OpenAI = require 'openai' const client = new OpenAI { apiKey: process.env.DEEPSEEK API KEY, baseURL: 'https://api.deepseek.com', } async function main { try { const response = await client.chat.completions.create { model: 'deepseek-chat', messages: { role: 'system', content: 'You are a helpful assistant' }, { role: 'user', content: 'What is SigNoz?' }, , stream: false, } console.log response.choices 0 .message.content // Optional: Log the interaction logger.emit { severityText: 'INFO', body: 'DeepSeek API call completed successfully', attributes: { 'deepseek.model': 'deepseek-chat', 'deepseek.response.length': response.choices 0 .message.content.length, }, } } catch error { console.error 'Error calling DeepSeek API:', error // Optional: Log the error logger.emit { severityText: 'ERROR', body: 'DeepSeek API call failed', attributes: { 'error.message': error.message, 'deepseek.model': 'deepseek-chat', }, } } } main 📌 Note: Before running this code, ensure that you have set the environment variable DEEPSEEK API KEY with your generated API key. View DeepSeek Traces, Logs, and Metrics in SigNoz Once configured, your DeepSeek application automatically emits traces, logs, and metrics. DeepSeek traces are available in SigNoz 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. DeepSeek logs are available in SigNoz under the Logs tab. You can also click 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: DeepSeek related metrics are available in SigNoz 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: DeepSeek Monitoring Dashboard The DeepSeek API dashboard template https://signoz.io/docs/dashboards/dashboard-templates/deepseek-dashboard/ provides specialized visualizations for monitoring your DeepSeek API usage. It includes pre-built charts tailored for LLM usage, along with import instructions to get started quickly.