OpenAI Monitoring with OpenTelemetry SigNoz released a guide for monitoring OpenAI applications using OpenTelemetry and its observability platform, enabling developers to track token usage, model latency, error rates, and request traces in real time. The setup requires Python 3.8+ or Java 8+ with the OpenAI library, and provides step-by-step instrumentation instructions for both SigNoz Cloud and self-hosted instances. The integration aims to help teams identify cost drivers, detect API failures, and trace individual LLM calls through their full application stack. This guide walks you through setting up OpenAI monitoring using OpenTelemetry and SigNoz. You will instrument your OpenAI applications to capture traces, logs, and metrics and visualize them in real time in SigNoz. OpenAI monitoring with SigNoz gives you visibility into: Token usage — track input and output tokens per request to understand cost drivers Model latency — measure response times across models and request types Error rates — detect API failures, rate limit errors, and timeouts before they impact users Request traces — follow individual LLM calls through your full application stack Requirements - Python 3.8 or newer - OpenAI Python library openai = 1.0.0 - Valid OpenAI API key - SigNoz setup choose one : SigNoz Cloud account https://signoz.io/teams/ with an active ingestion key- Self-hosted SigNoz instance Setup Step 1. Create a virtual environment python3 -m venv .venv source .venv/bin/activate Step 2. Install the OpenTelemetry dependencies pip install opentelemetry-distro~=0.51b0 pip install opentelemetry-exporter-otlp~=1.30.0 pip install opentelemetry-instrumentation-openai-v2 Step 3. Add automatic instrumentation opentelemetry-bootstrap --action=install Step 4. Run your application OTEL SERVICE NAME=