Build an SRE Observability Stack for Kubernetes with Rootly

Site Reliability Engineers (SREs) are tasked with maintaining system reliability in increasingly complex Kubernetes environments. As distributed systems grow, so does the volume of operational data, making it difficult to sift through noise and respond to incidents effectively. An SRE observability stack for Kubernetes is the solution, but a complete stack requires more than just data collection—it needs an intelligent action layer. While data-gathering tools provide the necessary signals, Rootly provides the crucial component that bridges the gap between observability insights and automated DevOps incident management.

Understanding the Components of a Kubernetes Observability Stack

To gain meaningful visibility into a Kubernetes cluster, you need to establish a foundational data-gathering layer. This layer is responsible for collecting the telemetry data that reveals the health and performance of your applications and infrastructure.

The Three Pillars of Observability

A comprehensive view of system health is built on three core types of telemetry data: metrics, logs, and traces.

• Metrics: These are numerical, time-series data points that are ideal for monitoring resource utilization (CPU, memory), application performance, and other quantitative trends over time. Kubernetes components emit metrics in a Prometheus format, which can be stored in a time-series database for analysis and alerting [6].
• Logs: These are timestamped text records of events, which can be structured or unstructured. Logs provide the granular context needed for debugging specific issues, offering a chronological account of what happened within a component or application [7].
• Traces: A trace represents the end-to-end journey of a request as it propagates through a distributed system. Traces are crucial for identifying performance bottlenecks and understanding dependencies within complex microservices architectures [8].

Foundational Data Collection Tools

A variety of open-source site reliability engineering tools form the data collection layer of a modern observability stack. Prometheus has become the de facto standard for metrics collection in Kubernetes environments. For logs, collectors like FluentBit or Vector are commonly used to aggregate data from various sources. For tracing, OpenTelemetry has emerged as the industry standard for instrumenting applications to generate and export trace data. While these tools are powerful, they represent only one half of the equation; true reliability comes from what you do with this data. The shift to AI-powered monitoring tools is changing how SREs approach this challenge.

The Problem with a Traditional Stack: Data Overload and Manual Toil

Simply collecting metrics, logs, and traces is insufficient for effective incident management. A traditional stack that focuses only on data gathering often creates more problems than it solves, leading to significant operational friction.

Alert Fatigue and Data Silos

One of the most common pain points is alert fatigue. An overwhelming volume of alerts from disconnected tools desensitizes on-call engineers, increasing the risk that they'll miss critical signals. With data locked in separate silos—metrics in one dashboard, logs in another—engineers are forced to manually pivot between UIs to correlate information and build a mental model of an incident. This fragmented approach is a key limitation of traditional monitoring compared to AI-driven platforms.

Slow, Manual Incident Response

The manual toil involved in a traditional incident response process is immense. Engineers spend valuable time diagnosing issues, finding the root cause, and coordinating the response, all while the system remains degraded. This manual work directly inflates Mean Time to Resolution (MTTR) and diverts SREs from high-value proactive work, such as improving system architecture and automating processes. A core principle of SRE is to reduce this type of manual intervention through automation, moving teams away from reactive firefighting [1].

Rootly: The Intelligent Action Layer for Your Observability Stack

Rootly solves the problems of a traditional stack by serving as an intelligent orchestration layer. It sits on top of your existing observability tools to translate raw data into swift, automated action.

From Passive Monitoring to Automated Incident Management

Rootly ingests alerts from any monitoring tool and uses AI-driven workflows to reduce noise, de-duplicate events, and group related signals into a single, actionable incident. This transforms the incident management process from a reactive, manual scramble into a proactive, automated response model. By automating routine tasks, Rootly helps teams adhere to key SRE principles focused on creating robust and self-healing systems [2]. With Rootly, you can manage the entire incident lifecycle from declaration to postmortem without manual intervention.

Automating Kubernetes Rollbacks and Escalations

Rootly excels at taking powerful, context-aware actions within Kubernetes environments. For example, it can be configured to automatically trigger a Kubernetes rollback (kubectl rollout undo) the moment a monitoring tool detects that a new deployment is causing critical errors. This immediate, automated remediation minimizes customer impact.

Beyond automated rollbacks, Rootly’s smart escalation policies prevent alert fatigue by routing alerts directly to the right team based on service ownership and urgency. This ensures critical issues receive immediate attention from the correct subject matter experts. By enabling auto Kubernetes rollbacks and smart escalations, Rootly reduces both MTTR and the cognitive load on your engineering teams.

How to Build Your SRE Observability Stack with Rootly

Integrating Rootly into your existing toolchain is straightforward, allowing you to create a modern observability and response stack with minimal overhead.

Step 1: Integrate Your Existing Data Sources

Rootly connects seamlessly with the site reliability engineering tools you already use. It integrates with monitoring and alerting platforms like Prometheus, Grafana, PagerDuty, and Datadog. Rootly also enriches incidents by pulling in crucial context from service catalogs like Opslevel, giving responders immediate visibility into service ownership, dependencies, and documentation.

Step 2: Leverage the Native Kubernetes Integration

Rootly's native Kubernetes integration allows it to automatically watch Kubernetes API events related to deployments, pods, services, and more. This provides critical, real-time context from within the cluster without requiring engineers to kubectl their way through an investigation. This deep integration is fundamental to effectively scaling SRE practices in a complex, Kubernetes-driven infrastructure [3]. By connecting directly to the cluster, Rootly ensures that every incident has the relevant infrastructure context attached from the start. You can learn more about the specifics in the Rootly Kubernetes documentation.

Step 3: Configure Automated Workflows for Incident Response

Once connected, you can configure Rootly’s powerful workflow automation engine to standardize your incident response process. Examples of automated tasks include:

• Creating a dedicated Slack channel for the incident.
• Paging the correct on-call engineer based on service data.
• Pulling in service catalog data and runbooks.
• Automatically generating a post-incident timeline with key events.

These workflows dramatically reduce the cognitive load on engineers during an incident, allowing them to focus on resolution rather than process.

Conclusion: The Future is an Action-Oriented Observability Stack

A modern SRE observability stack for Kubernetes is incomplete without an intelligent action and orchestration layer. While tools like Prometheus provide the necessary data, they don't answer the "so what?" question. Rootly does, by automating the response and connecting insights to action. This approach is essential for reducing MTTR, cutting down engineering toil, and building more resilient services in complex, cloud-native environments. By transitioning from traditional monitoring to an AI-augmented, action-oriented model, SRE teams can finally move from firefighting to proactive reliability engineering.

Ready to transform your incident management? Book a demo of Rootly today.

‍