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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.

read11 min publishedJun 1, 2026

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 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 accountwith an active ingestion key- Self-hosted SigNoz instance

  • Internet access to send telemetry data to SigNoz Cloud

  • A DeepSeekAPI 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

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:

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

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>

: YourSigNoz Cloud region<your-ingestion-key>

: Your SigNozingestion key- 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.

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:

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:

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)

is the name of your service<service_name>

→ SigNoz Cloud trace endpoint with appropriateOTEL_EXPORTER_TRACES_ENDPOINT

region:https://ingest.<region>.signoz.cloud:443/v1/traces

→ Your SigNozSIGNOZ_INGESTION_KEY

ingestion key

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.

Step 4: Instrument DeepSeek using OpenAInstrumentor

and the configured Tracer Provider

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

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)
)
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 appropriateOTEL_EXPORTER_LOGS_ENDPOINT

region:https://ingest.<region>.signoz.cloud:443/v1/logs

→ Your SigNozSIGNOZ_INGESTION_KEY

ingestion key

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.

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__)

SystemMetricsInstrumentor().instrument()
HTTPXClientInstrumentor().instrument()

is the name of your service<service_name>

→ SigNoz Cloud endpoint with appropriateOTEL_EXPORTER_METRICS_ENDPOINT

region:https://ingest.<region>.signoz.cloud:443/v1/metrics

→ Your SigNozSIGNOZ_INGESTION_KEY

ingestion key

📌 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.

Step 7: Run an example

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:

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:

const { LoggerProvider, BatchLogRecordProcessor } = require('@opentelemetry/sdk-logs')
const { OTLPLogExporter } = require('@opentelemetry/exporter-otlp-http')
const { logs } = require('@opentelemetry/api')

Metrics:

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

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 appropriateOTEL_EXPORTER_TRACES_ENDPOINT

region:https://ingest.<region>.signoz.cloud:443/v1/traces

→ Your SigNozSIGNOZ_INGESTION_KEY

ingestion key

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.

Step 4: Instrument DeepSeek using OpenAIInstrumentation

and the configured Tracer Provider

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

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 appropriateOTEL_EXPORTER_LOGS_ENDPOINT

region:https://ingest.<region>.signoz.cloud:443/v1/logs

→ Your SigNozSIGNOZ_INGESTION_KEY

ingestion key

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.

Step 6: Setup Metrics

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 appropriateOTEL_EXPORTER_METRICS_ENDPOINT

region:https://ingest.<region>.signoz.cloud:443/v1/metrics

→ Your SigNozSIGNOZ_INGESTION_KEY

ingestion key

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.

📌 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

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 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.

source & further reading
signoz.io — original article OpenRouter Observability & Monitoring with OpenTelemetry Google ADK Observability & Monitoring with OpenTelemetry LiteLLM Observability & Monitoring with OpenTelemetry
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