March 9, 2026

Build a SRE Observability Stack for Kubernetes with Rootly

Build a modern SRE observability stack for Kubernetes with Prometheus & Grafana. Learn how Rootly unifies SRE tools for incident tracking and resolution.

For Site Reliability Engineering (SRE) teams, maintaining visibility into dynamic Kubernetes environments is a critical challenge. While many teams excel at collecting telemetry data, the real value comes from acting on that data quickly and consistently when an incident occurs.

This guide covers the essential components of a modern SRE observability stack for Kubernetes. We'll review core open-source tools for data collection and analysis, then explain how Rootly provides the crucial incident management layer to make your observability data actionable.

The Pillars of Kubernetes Observability

A strong observability strategy rests on three foundational data types, often called the "three pillars of observability" [1]. To effectively diagnose issues in a distributed system, you need all three.

Metrics: Numerical measurements over time, such as CPU usage, request latency, or error rates. Metrics are ideal for dashboards, alerting, and understanding high-level system health.
Logs: Timestamped records of discrete events. A log entry captures what happened at a specific moment, providing the granular detail needed to debug a particular error.
Traces: A representation of a single request's end-to-end journey as it moves through multiple services. Traces are invaluable for pinpointing performance bottlenecks in a microservices architecture.

The goal isn't just to collect these data types but to connect them. A unified approach allows you to move seamlessly from a metric anomaly to the relevant logs and traces, which drastically reduces troubleshooting time [2].

Assembling Your Kubernetes Observability Stack: Core Tools

Building a powerful and cost-effective observability stack often starts with a foundation of best-in-class open-source tools. This approach offers maximum flexibility and control over your data.

Data Collection: OpenTelemetry and eBPF

The first step is instrumenting your applications and infrastructure to emit telemetry data.

OpenTelemetry (OTel) has emerged as the vendor-neutral, open standard for generating and collecting telemetry data. Adopting OTel helps you avoid vendor lock-in and ensures your data collection strategy is future-proof [3].

eBPF (extended Berkeley Packet Filter) is a powerful kernel technology that lets you gather deep visibility into system behavior without modifying application code. It's especially useful in Kubernetes for understanding performance at a low level.

Metrics, Monitoring, and Alerting: Prometheus & Grafana

For monitoring Kubernetes, Prometheus has become the de facto standard.

Prometheus is a time-series database and monitoring system. It scrapes metrics from configured endpoints, stores them efficiently, and provides a powerful query language for analysis. SRE teams commonly use it to track key indicators like Google’s Four Golden Signals (Latency, Traffic, Errors, and Saturation) [6].

Grafana is the visualization layer that brings Prometheus data to life. It allows teams to build rich, interactive dashboards to monitor system health and share insights across the organization. You can create a complete stack with these tools to visualize metrics and logs in one place [4].

Log Aggregation: Loki

Loki is a log aggregation system designed to integrate seamlessly with Prometheus and Grafana. It indexes metadata about your logs rather than the full-text content. This design makes it highly cost-effective and allows teams to correlate metrics and logs within the same Grafana interface, simplifying the debugging workflow.

The Missing Piece: From Observability Data to Incident Resolution

Your observability stack tells you when something is wrong. But what happens next?

When a critical Prometheus alert fires, who gets notified?
Where does the team coordinate the response?
How do you track the timeline, action items, and key decisions?

An observability stack is incomplete without a system to manage the incidents it uncovers. This is where dedicated SRE tools for incident tracking become essential. Rootly is the platform that answers these questions, acting as the command center for your entire incident response lifecycle.

How Rootly Completes Your Observability Stack

Rootly integrates with your observability tools to turn alerts into action, providing the structure and automation needed to resolve incidents faster.

Unify Alerts and Automate Response

Rootly connects directly to alerting tools like Prometheus (via Alertmanager), Datadog, and PagerDuty. When a qualified alert fires, Rootly automatically kicks off your incident response process. Automation can include:

Creating a dedicated Slack channel for the incident.
Inviting the on-call engineer and subject matter experts.
Starting a video conference bridge.
Populating the incident with data from the initial alert.

This automation eliminates manual work and ensures a consistent response every time, which is key to maintaining a fast and responsive SRE observability stack for Kubernetes.

Accelerate Triage with AI-Powered Insights

Rootly's AI capabilities help SREs make sense of the situation faster. As the use of AI in SRE becomes more common [5], these features provide a significant advantage. The platform can surface similar past incidents, suggest relevant runbooks, and highlight key data points to help teams identify the root cause more efficiently.

Centralize Incident Tracking and Collaboration

During an incident, Rootly serves as the single source of truth. It automatically builds a timeline of events, tracks action items, logs key decisions, and keeps stakeholders updated via integrated status pages. This eliminates the need for manual tracking in separate documents, ensuring all information is captured in one place for real-time visibility and post-incident analysis.

Learn from Incidents with Automated Retrospectives

Closing the feedback loop is critical for improving system reliability. Rootly uses all the data captured during an incident—the timeline, chat logs, and action items—to automatically generate a comprehensive retrospective. This structured process ensures your team learns from every incident and can implement meaningful changes to prevent future failures.

Conclusion: Build a Proactive and Actionable Stack with Rootly

A complete SRE observability stack for Kubernetes requires both best-in-class data collection tools like Prometheus and a powerful incident management platform to act on that data. While observability tools tell you what is broken, Rootly tells you what to do next.

By automating response workflows, centralizing collaboration, and facilitating learning, Rootly transforms your observability stack from a passive monitoring system into an active reliability engine. When you connect your tools to an incident management platform, you can truly build an SRE observability stack for Kubernetes with Rootly that is both proactive and actionable.

Ready to see how Rootly can unify your incident response? Book a demo or start your free trial today.