March 6, 2026

Build an SRE Observability Stack for Kubernetes with Rootly

Build a modern SRE observability stack for Kubernetes. Learn how to connect observability data to action with Rootly's automated incident response platform.

Kubernetes excels at orchestrating modern applications, but its dynamic nature creates significant visibility challenges. Features like ephemeral pods and constant churn make it powerful, yet also difficult to monitor and debug. Traditional monitoring falls short. You need true observability—the ability to ask new questions about your system's state without shipping new code.

This guide covers the components of a modern SRE observability stack for Kubernetes, from data collection to analysis. You'll also see how Rootly acts as the central hub for incident management, turning observability insights into swift, organized action. For more on this topic, see our guide on the top tools for your observability stack.

The Three Pillars of Kubernetes Observability

A strong observability practice is built on three data types, often called the "three pillars." Together, they provide a complete picture of system health, helping you understand both what is wrong and why [1].

Metrics: Numerical data that tracks system health over time, like the vital signs of your application. Examples in Kubernetes include CPU usage, pod restarts, and request latency. Prometheus is the industry-standard tool for collecting metrics.
Logs: Timestamped records of discrete events. Logs provide detailed, contextual information about specific moments, such as an application error or a user login. Tools like Loki are designed to handle logs efficiently in containerized environments.
Traces: A representation of a single request's complete journey as it travels through different microservices. Traces are essential for finding performance bottlenecks and understanding service interactions. OpenTelemetry is the emerging standard for generating trace data.

A common and powerful starting point is a stack that combines Prometheus for metrics, Loki for logs, and Grafana for visualization [2].

Assembling Your Observability Toolkit

An effective observability stack layers tools to collect, visualize, and alert on data. This toolkit gathers the raw information needed to understand system performance and forms the basis of a proactive reliability practice.

Data Collection & Ingestion

This layer is about gathering telemetry data from your Kubernetes clusters and applications.

OpenTelemetry: As a vendor-neutral standard, OpenTelemetry provides a single set of APIs and tools to instrument your code. It lets you generate and collect metrics, logs, and traces without being locked into a specific vendor's ecosystem [3].
Prometheus: This system scrapes metrics from services and infrastructure across your cluster. It stores this data in a time-series database and provides a flexible query language, PromQL, for analysis.
Log Collectors: Agents like Fluentd or Grafana Alloy run on cluster nodes to collect logs from applications and system components, forwarding them to a centralized backend like Loki. Modern approaches also leverage eBPF for efficient kernel-level data collection [4].

Visualization & Analysis

This layer is for making sense of the collected data.

Grafana is the leading open-source platform for creating dashboards. It connects to data sources like Prometheus and Loki, allowing you to build unified views that correlate metrics and logs. For example, you can create a dashboard showing a spike in API errors (metrics) and drill down into the corresponding logs from that exact moment.

Alerting & Incident Response

When dashboards reveal a problem, you need a system to trigger an alert and manage the response.

Alertmanager: This component integrates with Prometheus to handle alerts. It deduplicates, groups, and routes them to the correct destination, like a Slack channel or a generic webhook.
Rootly: This is where your observability stack becomes an action engine. Rootly receives alerts from Alertmanager and serves as the central platform for managing the entire incident lifecycle.

Integrating Rootly: From Alert to Action

Rootly bridges the gap between detecting a problem and resolving it, providing the automated workflows and collaboration features essential for SRE tools for incident tracking. It adds the automation and collaboration workflows needed for fast, effective incident management.

Centralize Alerts and Automate Incident Declaration

Rootly integrates with your entire monitoring stack. When an alert fires from Alertmanager—for example, signaling that a critical deployment is in a CrashLoopBackOff state—you can configure Rootly to automatically declare an incident. This automation reduces manual work and ensures critical alerts are never missed.

Automate Toil with AI-Powered Workflows

Once an incident is declared, Rootly's workflows automate the tedious tasks that slow your team down. Imagine this scenario:

An alert for high p99 latency on your payments service triggers an incident.
Rootly automatically creates a dedicated Slack channel, such as #inc-202603-payments-latency.
The on-call engineer for the payments team is paged and invited to the channel.
Rootly instantly populates the channel with alert details, a link to the relevant Grafana dashboard, and the team's incident response runbook.

By automating these steps, Rootly empowers teams to slash Mean Time to Resolution (MTTR) by up to 80%.

Manage the Incident and Communicate Effectively

During an incident, Rootly acts as the single source of truth. Its real-time command center helps teams collaborate efficiently. Key features include:

A real-time incident timeline that automatically captures key events, decisions, and chat messages.
Integrated task management to assign and track remediation actions.
Automated stakeholder communication through integrated status pages, which provides leaders and customers with instant updates on SLO breaches without distracting the response team.

Learn and Improve with Automated Retrospectives

The incident isn't over when the problem is fixed. Learning from incidents is key to building more resilient systems. Rootly automates this by generating a comprehensive retrospective document pre-filled with data from the incident, including the timeline, metrics, chat logs, and action items. This closes the feedback loop and turns every incident into a learning opportunity.

Conclusion: Build a Proactive SRE Practice with Rootly

A complete SRE observability stack for Kubernetes requires more than just data collection tools like Prometheus and OpenTelemetry. Its true power is unlocked when you connect that data to an intelligent incident response platform that drives action.

Rootly transforms your observability stack from a passive monitoring system into a proactive incident management engine. By automating workflows, centralizing communication, and simplifying learning, Rootly reduces MTTR and empowers your engineers to build more reliable systems.

Ready to connect your observability stack to an AI-native incident management platform? Book a demo or start a free trial to see how Rootly can streamline your incident response [5].