from opentelemetry import traceprint<\/strong>(“Processing order…”)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nThis creates a trace span around the process_order operation, recording how long it takes and allowing it to be exported to any compatible backend like Jaeger, Tempo, or a commercial observability platform.<\/p>\n\n\n\n
\nWhy OpenTelemetry Matters<\/strong><\/li>\n<\/ol>\n\n\n\nBecause OpenTelemetry is vendor-neutral, teams can instrument their code once and switch observability backends later, such as moving from a self-hosted Jaeger setup to a commercial platform, without rewriting instrumentation throughout their codebase.<\/p>\n\n\n\n
\n \n
\ud83d\udca1 Did You Know?<\/strong>\n Prometheus<\/strong> was originally developed at SoundCloud<\/strong> in 2012 and became the second project accepted into the Cloud Native Computing Foundation (CNCF)<\/strong> in 2016, shortly after Kubernetes<\/strong>. Its early integration with Kubernetes helped establish Prometheus as the de facto monitoring and alerting solution for cloud-native environments. Today, it is widely used across Kubernetes-based infrastructures to collect metrics, monitor system health, and power observability platforms at scale.\n\n<\/div>\n\n\n\nHow Prometheus, Grafana, and OpenTelemetry Work Together<\/strong><\/h2>\n\n\n\nA typical modern observability stack combines all three tools, each handling a distinct role.<\/p>\n\n\n\nTool<\/strong><\/td>Role<\/strong><\/td><\/tr>OpenTelemetry<\/td> Instruments application code to generate metrics, logs, and traces<\/td><\/tr> Prometheus<\/td> Collects and stores the metrics data over time<\/td><\/tr> Grafana<\/td> Visualizes the stored metrics through dashboards and alerts<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nIn this setup, OpenTelemetry exports metrics in a Prometheus-compatible format, Prometheus scrapes and stores them, and Grafana queries Prometheus to render dashboards that engineers monitor daily.<\/p>\n\n\n\n
\n \n
\ud83d\udca1 Did You Know?<\/strong>\n Grafana<\/strong> began in 2014 as a fork of Kibana<\/strong>, created because its founder wanted stronger support for time-series data<\/strong> and the Graphite<\/strong> monitoring ecosystem. Over time, Grafana evolved into a full observability platform. Today, Grafana Labs<\/strong> also maintains Grafana Loki<\/strong> for log aggregation and Grafana Tempo<\/strong> for distributed tracing, enabling teams to visualize and analyze metrics, logs, and traces\u2014the three core pillars of modern observability\u2014through a unified interface.\n\n<\/div>\n\n\n\nConclusion<\/strong><\/h2>\n\n\n\nMonitoring and observability have become essential disciplines as systems grow more distributed and complex, making it impossible to understand production behavior through guesswork alone. <\/p>\n\n\n\n
Prometheus, Grafana, and OpenTelemetry together form a widely adopted, open-source observability stack that covers metric collection, visualization, and standardized instrumentation across any backend.<\/p>\n\n\n\n
FAQ<\/strong><\/h2>\n\n\n\n
\n
\n
What is the difference between monitoring and observability?<\/strong>\u00a0<\/h3>\n\n\n
Monitoring tracks predefined metrics to detect known issues through alerts, while observability provides the broader data needed to investigate unknown or unexpected problems.<\/p>\n\n<\/div>\n<\/div>\n
\n
What is Prometheus used for?<\/strong>\u00a0<\/h3>\n\n\n
Prometheus collects and stores time-series metrics by scraping data from application endpoints at regular intervals, forming the foundation of most modern monitoring stacks.<\/p>\n\n<\/div>\n<\/div>\n
\n
What is the role of Grafana in observability?<\/strong>\u00a0<\/h3>\n\n\n
Grafana visualizes data from sources like Prometheus through customizable dashboards, graphs, and alerts, without collecting the underlying data itself.<\/p>\n\n<\/div>\n<\/div>\n
\n
What is OpenTelemetry and why is it important?<\/strong>\u00a0<\/h3>\n\n\n
OpenTelemetry is a vendor-neutral framework for generating metrics, logs, and traces, allowing teams to instrument code once and switch observability backends later without rewriting instrumentation.<\/p>\n\n<\/div>\n<\/div>\n
\n
What are the three pillars of observability?<\/strong>\u00a0<\/h3>\n\n\n
Metrics, logs, and traces. Metrics show aggregate system behavior, logs show specific event details, and traces show how requests move through distributed services.<\/p>\n\n<\/div>\n<\/div>\n
\n
Does Grafana store metric data itself?<\/strong>\u00a0<\/h3>\n\n\n
No. Grafana connects to external data sources like Prometheus, Elasticsearch, or InfluxDB and visualizes the data they store, rather than storing data on its own.<\/p>\n\n<\/div>\n<\/div>\n
\n
How often does Prometheus collect metrics by default?<\/strong>\u00a0<\/h3>\n\n\n
The scrape interval is configurable, but a common default is every 15 to 30 seconds, depending on the application’s monitoring requirements.<\/p>\n\n<\/div>\n<\/div>\n
\n
Can OpenTelemetry work with Prometheus and Grafana together?<\/strong>\u00a0<\/h3>\n\n\n
Yes. OpenTelemetry can export metrics in a Prometheus-compatible format, which Prometheus stores and Grafana then visualizes, forming a complete observability pipeline.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"
Many development teams only discover production issues after users start complaining, simply because they lack visibility into how their systems are actually performing. Monitoring and observability solve this by giving teams real-time insight into metrics, logs, and traces before small issues become major outages. Understanding tools like Prometheus, Grafana, and OpenTelemetry is now a core […]<\/p>\n","protected":false},"author":63,"featured_media":119814,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[621],"tags":[],"views":"25","authorinfo":{"name":"Vishalini Devarajan","url":"https:\/\/www.guvi.in\/blog\/author\/vishalini\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/06\/monitoring-with-prometheus-grafana-opentelemetry-300x118.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/118330"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/63"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=118330"}],"version-history":[{"count":4,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/118330\/revisions"}],"predecessor-version":[{"id":119813,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/118330\/revisions\/119813"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/119814"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=118330"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=118330"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=118330"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}