Traces illustration

Traces

All articles tagged 'Traces'

Istio, Linkerd and Envoy pods with sidecar proxies cabled together and to a meter

Service Mesh Observability: Istio, Linkerd and Envoy Metrics

Service mesh observability guide: the metrics Istio, Linkerd and Envoy proxies emit, what ambient mode changes, and why tracing still needs app changes.

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Sejal Pandey

Sejal Pandey

A switchboard patched into three tool units, with the middle unit's lamp glowing lime

MCP Server Monitoring: Tracing Tool Calls with OpenTelemetry

MCP server monitoring guide: spans, metrics and logs to record for tool calls, why failed tools look healthy, and how to trace requests from client to server.

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Sejal Pandey

Sejal Pandey

An isometric filing cabinet for the vector index, its middle drawer lit lime, cabled to a retro CRT whose screen shows a trace waterfall with the retrieval span highlighted

RAG Pipeline Observability: What to Trace at Every Stage

RAG pipeline observability guide: the spans, attributes and metrics to record for embedding, retrieval, reranking and generation, plus evals for bad answers.

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Sejal Pandey

Sejal Pandey

Capturing HTTP Request and Response Bodies in .NET Traces with PHI Redaction

Capturing HTTP Request and Response Bodies in .NET Traces with PHI Redaction

> Standard OTel .NET instrumentation captures headers, status codes, and timing — not request or response bodies. Here's how to add body capture to your traces while keeping PHI out of your observability backend.

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Prathamesh Sonpatki

Prathamesh Sonpatki

Fixing Broken Traces in GCP Cloud Run

Fixing Broken Traces in GCP Cloud Run: A Custom OpenTelemetry Propagator

GCP's load balancer silently rewrites your traceparent header, orphaning spans in any OTLP backend. Here's the custom propagator that fixes it.

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Prathamesh Sonpatki

Prathamesh Sonpatki

LLM Observability: Importance, Best Practices, and Steps

What is LLM Observability? A Complete Guide (with OpenTelemetry)

> LLM observability is the practice of tracking what goes into an LLM, what comes out, and everything in between — latency, token usage, errors, and model behavior — so you can debug, optimize, and trust AI applications in production.

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Prathamesh Sonpatki

Prathamesh Sonpatki