From chaos to clarity: How OpenTelemetry unified observability across clouds

The problem: Observability tool sprawl The turning point: Why OpenTelemetry The solution: Implementing OpenTelemetry Benefits realized Lessons learned Conclusion Posted on November 27, 2025 by Arunvel Arunachalam, Infosys CNCF projects highlighted in this post Modern applications rarely live in a single place anymore. One organization’s application footprint was spread across AWS, Azure, and GCP , with some workloads still running on-prem. This multi-cloud approach gave them resilience and flexibility, but it came with a hidden cost: observability sprawl. Each cloud provider brought its own native observability stack. On AWS, they used CloudWatch ; on Azure, Azure Monitor ; on GCP, Stackdriver ; and in their on-prem setup, a mix of Prometheus and ELK. Add to that some third-party APM tools, and suddenly engineers were juggling five dashboards just to debug one request. This was slowing them down. Mean Time to Resolution (MTTR) kept climbing, and developers spent more time stitching together logs and traces than writing code. The turning point came when the organization adopted OpenTelemetry (OTel) , a CNCF graduated project and community-driven standard for observability. What started as a small experiment soon became the backbone of their observability strategy, aligning with the broader trend across the CNCF community toward platform engineering maturity and standardized telemetry practices. Let’s break down what was happening before OpenTelemetry: Multiple tools for the same purpose Metrics: Prometheus (on-prem) + CloudWatch (AWS) + Azure Monitor (Azure). Logs: ELK (on-prem) + Stackdriver (GCP).