Setting Up OpenTelemetry for LLM Observability on a Self-Hosted Stack
The article discusses how to set up OpenTelemetry (OTel) for observability of Large Language Model (LLM) pipelines in a self-hosted environment, providing trace continuity, token and cost attribution, and vendor neutrality.
Why it matters
This article provides a practical guide for setting up observability for LLM workloads in a self-hosted environment, which can be more cost-effective than managed platforms for smaller-scale operations.
Key Points
- 1OpenTelemetry has become the de facto standard for distributed tracing, metrics, and logs, with semantic conventions for generative AI workloads
- 2The self-hosted stack includes an OTel Collector, Tempo/Jaeger for trace storage, Prometheus for metrics storage, and Grafana for visualization
- 3The OTel Collector is configured to receive telemetry from agents, process it, and export to the storage backends
Details
The author, who runs a small AI automation shop, found that most observability guides assumed the use of a managed platform. However, for those who prefer to own their data and infrastructure, OpenTelemetry provides a solid, vendor-neutral foundation. The article explains the benefits of using OTel for LLM workloads, including trace continuity across agent steps, token and cost attribution, and the ability to swap providers without rewriting observability code. The self-hosted stack is described, with the OTel Collector as the central hub that receives telemetry from agents, processes it, and exports to Tempo/Jaeger for trace storage and Prometheus for metrics storage. The article provides a minimal configuration for the OTel Collector to set up this self-hosted observability solution.
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