Operations | Monitoring | ITSM | DevOps | Cloud

OpenMetrics and OpenTelemetry are popular standards for instrumenting cloud-native applications. Both projects are part of the Cloud Native Computing Foundation (CNCF) and aim to simplify how we generate, collect and monitor services in a modern cloud-native distributed application environment. Let's have a look at how both the standards are aiming to help solve the observability conundrum.

A few days ago I hosted a live conversation with Pranav, co-founder of Chatwoot, about issues his team was running into with LLM observability. The short version: building, debugging, and improving AI agents in production gets messy fast. There's multiple competing standards for default libraries for LLM observability. And many such libraries like OpenInference which claim to be based on OpenTelemetry don't strictly adhere to it's conventions.

To effectively manage modern applications, you need to understand how they work on the inside. Distributed tracing is the key to this, providing a detailed picture of a request's journey across every service. OpenTelemetry has emerged as the industry-standard framework for implementing tracing and achieving true observability in complex, distributed systems. In this article, we embark on a journey to explore the core concept of context propagation within Open Telemetry.

OpenTelemetry is a broader, vendor-neutral framework for generating and collecting telemetry data (logs, metrics, traces), offering flexible backend integration. Jaeger, on the other hand, is focused on distributed tracing in microservices. Earlier Jaeger had its own SDKs based on OpenTracing APIs for instrumenting applications, but now Jaeger recommends using OpenTelemetry instrumentation and SDKs. Warning The original Jaeger client SDKs (based on OpenTracing) are archived and no longer maintained.

We talked to 7 companies in August 2025 who were looking to switch from New Relic. One engineering director said they're paying $1,000 a month and only ingesting 10% of their traces. Teams are defaulting to aggressive sampling, some at 1%, others at 10%, to manage costs.

In this post, we will talk about OpenTelemetry exporters. OpenTelemetry exporters help in exporting the telemetry data collected by OpenTelemetry. OpenTelemetry frees you from any kind of vendor lock-in by letting you export the collected telemetry data to any backend of your choice. In modern distributed systems, efficiently collecting, transmitting, and analyzing telemetry data from diverse sources poses a significant challenge.

OpenTelemetry is a Cloud Native Computing Foundation(CNCF) incubating project aimed at standardizing the way we instrument applications for generating telemetry data(logs, metrics, and traces). OpenTelemetry aims to provide a vendor-agnostic observability framework that provides a set of tools, APIs, and SDKs to instrument applications.

Manually deploying and managing OpenTelemetry components in a Kubernetes environment can be a complex and time-consuming task. It involves creating various Kubernetes resources, setting up configurations, and ensuring the components are properly integrated with the applications.