Build an SRE Observability Stack for Kubernetes with Rootly

Build an SRE observability stack for Kubernetes with open-source tools. See how Rootly closes the loop with automated incident tracking & response.

Kubernetes is the standard for orchestrating containerized applications, but its dynamic, distributed nature makes it complex to manage. Traditional monitoring, which tracks the health of individual components, doesn't provide the deep system understanding required in these environments. To know what’s truly happening inside your clusters, you need modern observability.

An observability stack moves beyond simple health checks. It provides the rich, contextual data you need to ask any question about your system's behavior and understand not just if something is wrong, but why. This article guides you through building a foundational sre observability stack for kubernetes with leading open-source tools. More importantly, it shows how to connect that stack to an incident management platform like Rootly to create a complete solution that transforms data into decisive action. This integrated approach is a cornerstone of any modern SRE stack.

The Three Pillars of a Kubernetes Observability Stack

A comprehensive observability strategy is built on three distinct types of telemetry data: metrics, logs, and traces. Each offers a unique perspective on your system's health and behavior [1].

Metrics: The "What"

Metrics are time-series numerical data about your system. For Kubernetes, this includes data like pod CPU utilization, container memory usage, API server request latency, and ingress error rates. Because they are efficient to store and query, metrics are ideal for building dashboards to visualize trends, establishing performance baselines, and triggering alerts when a key indicator crosses a defined threshold.

Logs: The "Why"

While a metric alert tells you what happened (for example, "error rate spiked"), logs provide the context to understand why. Logs are timestamped, event-specific records. An application log might contain a detailed error message with a full stack trace, while a Kubernetes component log could show that a pod failed to start. By centralizing and indexing logs, engineers can quickly search for the specific event data needed for debugging.

Traces: The "Where"

In a distributed microservices architecture, a single user request often travels through dozens of individual services. A trace follows that request's entire journey, showing the time it spent in each component. This helps you pinpoint performance bottlenecks and understand service dependencies, answering the critical question of where in the system a slowdown or error is occurring.

Building Your Foundational Stack with Open Source Tools

You can build a production-ready observability stack for Kubernetes using a combination of powerful, community-standard open-source tools. This popular combination provides deep visibility into complex cloud-native environments and is a common blueprint for production-grade setups [4].

Collecting Metrics with Prometheus

Prometheus is the de facto standard for metrics collection in the Kubernetes ecosystem. It uses a pull-based model, periodically scraping metrics from endpoints exposed by your applications and infrastructure. Its powerful query language (PromQL) and native service discovery make it a perfect fit for dynamic environments. Teams typically deploy it using the kube-prometheus-stack Helm chart, which bundles Prometheus, Alertmanager, and pre-configured Grafana dashboards.

Aggregating Logs with Loki or Fluentd

To make sense of logs from across your cluster, you need a centralized aggregation system. Loki is a highly effective choice designed for cost-efficiency and seamless integration with Prometheus and Grafana. It works by indexing only the metadata about your logs (like pod labels) rather than the full-text content, which significantly reduces storage costs and leverages the same label-based querying you use with Prometheus. Fluentd is another popular and powerful alternative for log collection and forwarding.

Gaining Insights with Traces using OpenTelemetry

OpenTelemetry (OTel) is the CNCF standard for instrumenting applications to generate traces, metrics, and logs [2]. By using its unified APIs and SDKs, you can create standardized telemetry across all your services and avoid vendor lock-in. The OpenTelemetry Collector is deployed as an agent to receive, process, and export this data to various backends, simplifying your instrumentation strategy.

Visualizing and Alerting with Grafana and Alertmanager

Grafana serves as the unified visualization layer for your entire stack. It allows you to build dashboards combining metrics from Prometheus, logs from Loki, and traces from an OpenTelemetry-compatible backend. For alerting, Prometheus's companion service, Alertmanager, handles deduplicating, grouping, and routing alerts defined in Prometheus. It can send notifications to receivers like Slack, PagerDuty, or a generic webhook—the critical handoff point to your incident management process.

The Missing Piece: Connecting Observability to Incident Management

Your observability stack excels at detecting problems and generating alerts. But an alert is just a signal. The most important step is what your team does with that signal. This is the domain of incident management: the structured process for responding to, resolving, and learning from service interruptions.

This is where your observability data becomes actionable. Instead of just seeing an alert, you need a system that automatically kicks off a coordinated response. The best SRE tools for incident tracking don't just create tickets; they orchestrate the entire response workflow. Rootly acts as the command center that integrates directly with your observability tools. It turns alerts from Alertmanager into immediate, automated actions, giving you a truly powerful SRE observability stack for Kubernetes.

How Rootly Completes Your SRE Observability Stack

Rootly provides the response and coordination layer that sits on top of your data stack. It closes the loop between detection and resolution, ensuring every alert is handled swiftly, consistently, and effectively.

Automate Incident Response, Not Just Alerting

When Alertmanager sends an alert to a configurable Rootly webhook, it instantly launches a customizable workflow. This automation saves critical time and reduces the cognitive load on your on-call engineers. A typical workflow can:

Create a dedicated Slack channel for the incident.
Invite the correct on-call engineers and subject matter experts.
Start a video conference bridge.
Pull relevant Grafana dashboards and runbook links directly into the incident channel.

Centralize Communication and Context

Scattered communication during an incident leads to confusion and slows resolution. Rootly acts as the single source of truth by automatically creating an incident timeline, tracking tasks, and helping assign roles like Incident Commander. With integrated Status Pages, you can keep internal stakeholders and external customers informed without distracting the engineers working on the fix.

Drive Continuous Improvement with Data-Driven Retrospectives

The ultimate goal of incident management isn't just fixing the immediate problem; it's learning from it to prevent future occurrences. Rootly streamlines the post-incident review by automatically gathering all relevant data—the timeline, chat logs, attached graphs, and action items—into a retrospective report. This makes it simple to analyze contributing factors and track follow-up tasks to improve system resilience. For any team seeking to build a strong reliability culture, this is an essential incident management suite for SaaS companies.

Accelerate Resolution with AI

As of 2026, AI-driven precision is a key feature of modern observability stacks [3]. Rootly integrates AI to further accelerate incident response. These capabilities can summarize long incident threads for new responders, suggest similar past incidents to provide context, or help draft the narrative for a retrospective. This allows your team to spend less time on manual tasks and more time on high-impact problem-solving.

Conclusion: Build a Complete and Actionable SRE Stack

A powerful observability stack built with tools like Prometheus, Grafana, Loki, and OpenTelemetry gives you unparalleled visibility into your Kubernetes environment. It enables you to move from reactive monitoring to proactive observability.

However, visibility alone is not enough. This stack becomes truly transformative when connected to a dedicated incident management platform like Rootly. By automating response workflows, centralizing communication, and streamlining the learning process, Rootly turns your observability data into swift, structured, and effective action. This combination of visibility and action is the foundation of a modern, resilient engineering organization.

Ready to connect your observability stack to a world-class incident management platform? Book a demo or start your free trial today.