Operations | Monitoring | ITSM | DevOps | Cloud

This article explains how to use opentelemetry-go Metrics API to collect metrics, for example, go-redis/cache stats.

It has become common knowledge that the nature of software development has changed as AI-code generation and agent-based features gain adoption. In perhaps a more subtle shift, the fundamentals of software instrumentation are changing too. As OpenTelemetry becomes the standard instrumentation layer across enterprises, with thousands of developers (many from Honeycomb) actively contributing to it, the nature of the telemetry data captured itself is evolving to meet the growing demand for rich context.

Over the course of my 20 years as a developer, SRE, and now observability product leader, software has typically progressed at a good pace. But now, the emergence of two transformative technologies are fundamentally reshaping enterprise observability: generative AI (GenAI) and OpenTelemetry (OTel). We surveyed over 500 IT decision-makers for a new report:The Landscape of Observability in 2026: Balancing Cost and Innovation.

OpenTelemetry instrumentation is the foundation of modern microservices observability, but getting it right in production requires more than just enabling auto-instrumentation. This guide covers production-tested OpenTelemetry best practices that help engineering teams achieve reliable distributed tracing, control observability costs, and extract maximum value from their telemetry data.

This guide shows you how to implement OpenTelemetry’s auto-instrumentation for complete distributed tracing across your microservices, from initial setup through production optimization and troubleshooting.

Distributed tracing has become essential for modern software teams. As applications evolve into complex distributed systems with microservices, APIs, databases, and third-party integrations, understanding how a single user request travels through your entire stack is no longer optional, it’s critical for maintaining performance, reliability, and user satisfaction.

The distributed tracing landscape has evolved from “observability add-on” to core production infrastructure. In 2026, distributed tracing is no longer optional for engineering teams operating microservices, Kubernetes, or AI-driven workloads. It is now tightly coupled with incident response, cost optimization, and AI-assisted debugging.