Rootly: Build an SRE Observability Stack for Kubernetes

Learn to build a powerful SRE observability stack for Kubernetes with Prometheus & OTel. Discover how to connect it to SRE tools for incident tracking.

Managing distributed applications on Kubernetes is complex. As systems scale, simply watching high-level dashboards isn't enough. To diagnose and resolve issues quickly, site reliability engineering (SRE) teams need the ability to ask any question about their system's state, at any time. Building a dedicated SRE observability stack for Kubernetes is no longer a luxury—it's essential for maintaining reliability [1].

This article provides a blueprint for building a modern observability stack using open-source tools. We'll cover the core components and show you how to connect your data to an incident management platform to make it truly actionable.

The Three Pillars of Observability

A complete observability practice provides a full picture of system health by collecting and correlating three distinct types of telemetry data: metrics, logs, and traces. A robust stack must address all three.

Metrics: The What

Metrics are time-series numerical data, such as CPU usage, request latency, or error counts. They are essential for monitoring overall system health, identifying performance trends, and triggering alerts when a threshold is breached. In the Kubernetes world, Prometheus is the de facto open-source standard. Its pull-based model and powerful query language (PromQL) make it a cornerstone for production-grade monitoring [2].

Logs: The Why

Logs are immutable, timestamped records of discrete events. While metrics tell you what happened, logs provide the detailed, contextual narrative that explains why. They are invaluable for debugging specific errors and understanding the story behind a metric spike. Tools like Loki offer a cost-effective, horizontally scalable solution that is purpose-built for the ephemeral nature of Kubernetes workloads [3].

Traces: The Where

Distributed tracing follows a single request as it travels across the various microservices in your application. In a complex architecture, traces are critical for identifying performance bottlenecks, understanding service dependencies, and pinpointing exactly where latency or errors occur. OpenTelemetry-compatible backends like Jaeger allow you to visualize a request's entire journey, making it possible to isolate problematic services or network calls.

Architecting Your Open-Source Kubernetes Observability Stack

You can create a fast SRE observability stack for Kubernetes by combining powerful, open-source tools into a cohesive architecture. Here’s a practical, implementation-focused approach.

Unify Data Collection with OpenTelemetry

As of March 2026, OpenTelemetry (OTel) is the industry standard for instrumenting applications and collecting telemetry data [4]. Its main benefit is vendor neutrality, offering a unified set of APIs for generating and collecting metrics, logs, and traces. By using the OTel Collector, you can standardize how telemetry is processed and exported to various backends, which prevents vendor lock-in and simplifies your architecture [5].

Scrape and Store Metrics with Prometheus

In this stack, Prometheus serves as the metrics backend. It's configured to automatically discover and scrape metrics from services running in your Kubernetes cluster using ServiceMonitor or PodMonitor custom resources. With its powerful query language, PromQL, engineers can perform complex time-series analysis and define the precise alerting rules that signal a potential incident [6].

Visualize and Alert with Grafana

Grafana is the unified "single pane of glass" for your stack. It connects to multiple data sources, allowing you to build dashboards that correlate metrics from Prometheus, logs from Loki, and traces from Jaeger. This capability enables teams to pivot seamlessly from a high-level alert to the specific logs or traces needed for an investigation. Grafana is also where you configure the alerting rules that will ultimately trigger your incident response process.

From Observability to Actionable Incident Management

An observability stack's true value is realized only when it drives a fast and effective incident response. Collecting data is just the beginning; you must make it actionable.

The Gap Between an Alert and a Resolution

An alert fires in Grafana—what happens next? For many teams, the process is manual and chaotic. Engineers waste valuable time creating Slack channels, finding the right on-call person, and hunting for context across different tools. Every manual step introduces delay and increases Mean Time to Resolution (MTTR). This is where dedicated SRE tools for incident tracking become critical.

Integrating SRE Tools for Incident Tracking

The solution is an incident management platform that automates and orchestrates the entire response process. By integrating directly with your observability stack, these platforms eliminate the manual toil associated with incidents. When an alert fires, the platform can automatically trigger workflows, notify the right people, and centralize all communication. As a result, incident management platforms are considered one of the must-have SRE tools for 2026.

How Rootly Complements Your Observability Stack

Rootly acts as the command center for incidents, turning the data from your observability stack into coordinated, decisive action. As an AI-native incident management platform [7], Rootly streamlines every phase of the response lifecycle. When you build a powerful SRE observability stack for Kubernetes, connecting it to Rootly closes the loop between detection and resolution.

Automated Incident Creation: Rootly ingests alerts from Prometheus or Grafana to automatically declare an incident, create a dedicated Slack channel, and assemble the response team.
Context at Your Fingertips: It pulls relevant Grafana dashboards, logs, and runbooks directly into the incident channel. This gives responders immediate context without forcing them to switch between tools.
AI-Powered Insights: Rootly uses AI to identify similar past incidents, suggest potential root causes, and help generate post-incident summaries, freeing up engineers to focus on resolving the issue.

Connecting your observability stack to Rootly provides an essential incident management suite for SaaS companies that makes your data actionable and your response process repeatable.

Conclusion: Build a Smarter, Faster SRE Practice

A modern SRE observability stack for Kubernetes combines open-source tools like OpenTelemetry, Prometheus, Loki, and Grafana to provide comprehensive system visibility. However, to maximize its value and truly improve reliability, this stack must be integrated with one of the leading SRE tools for incident tracking.

Rootly serves as the central hub that transforms observability data into coordinated action, automated workflows, and faster resolutions. By automating the toil of incident response, you empower your team to build a smarter, faster, and more resilient SRE practice.

Ready to connect your observability stack to an AI-powered incident management platform? Book a demo of Rootly to see how it works.