March 10, 2026

Build a High Performance SRE Observability Stack for K8s

Build a powerful SRE observability stack for Kubernetes. Learn how tools like Prometheus, Grafana, & Loki integrate with SRE incident tracking tools.

For Site Reliability Engineering (SRE) teams, keeping complex Kubernetes (K8s) environments reliable is impossible without deep visibility. This requires more than basic monitoring. It demands observability—the power to understand your system’s internal state by observing its external outputs. This lets you ask detailed questions about your system's behavior without needing to predict those questions in advance.

A complete SRE observability stack for Kubernetes is built on three pillars: metrics, logs, and traces [4]. This guide walks you through choosing and combining the right open-source tools to build a high-performance stack that helps your team resolve issues faster.

The Three Pillars of Kubernetes Observability

To get a full picture of your system’s health, you need to collect and connect data from three distinct categories.

Metrics

Metrics are numerical, time-series data points showing your system's health and performance over time. In Kubernetes, this includes data like CPU and memory usage, pod counts, container restarts, and network traffic. Metrics are perfect for dashboards, alerting on known problems like high CPU usage, and spotting performance trends [5].

Logs

Logs are time-stamped records of specific events, such as application errors, infrastructure changes, or web server requests. While metrics tell you that something is wrong, logs provide the context to understand why. They are essential for debugging specific problems and finding the root cause after an incident.

Traces

Traces show the complete journey of a single request as it travels through the different microservices in your system. For example, a trace can follow a "checkout" request from the frontend website, to the payment service, and finally to the database. Traces are critical for finding performance bottlenecks and debugging latency issues in distributed architectures [3].

Assembling Your Toolchain

An effective observability stack uses the right tool for each pillar and makes sure they work together. The following open-source tools are widely used for their power and flexibility in Kubernetes environments.

Metrics Collection: Prometheus

Prometheus is the industry standard for Kubernetes monitoring. It uses a "pull" model to collect metrics from your services, a method that pairs perfectly with Kubernetes service discovery [7]. Its powerful query language (PromQL) and alerting features make it a solid foundation for your metrics pipeline.

Log Aggregation: Loki and Fluentd

The combination of Loki and Fluentd offers a powerful and cost-effective logging solution [6].

Fluentd/Fluent Bit: This tool acts as a log collector. It runs on each node to gather logs from containers and send them to a central storage location.
Loki: Loki stores and indexes your logs. Its key advantage is that it only indexes log metadata (labels), not the full text. This approach, inspired by Prometheus, makes it highly efficient and affordable at scale.

Distributed Tracing: OpenTelemetry and Jaeger/Tempo

To trace requests, you need to instrument your code to generate data and have a backend to store it.

OpenTelemetry (OTel): OTel is the vendor-neutral standard for creating telemetry data (traces, metrics, and logs) from your applications [1]. Using OTel helps you avoid vendor lock-in and provides a consistent way to generate observability data.
Jaeger or Tempo: These are open-source backends designed to store and visualize the traces generated by OTel. They help engineers see the lifecycle of a request and quickly find sources of latency [2].

Visualization: Grafana

Grafana brings all this data together into a single, unified view. It lets you build dashboards that combine Prometheus metrics, Loki logs, and Jaeger/Tempo traces. This allows SREs to correlate information from all three pillars in one place, which speeds up investigations.

Bridging Observability and Action with Incident Management

Collecting data is only half the battle. The real value comes from using that data to resolve incidents quickly. Incident management platforms connect to your observability stack, turning alerts into a structured and automated response. They are essential SRE tools for incident tracking and resolution.

Automating Response with Rootly

An incident management platform like Rootly connects directly with your observability tools, receiving alerts from Prometheus or Grafana. When an alert fires, Rootly automates the manual tasks that slow teams down. For example, it can:

Automatically create a dedicated Slack channel for the incident.
Page the correct on-call engineer.
Pull in relevant runbooks and dashboards from your knowledge base.
Start a video call for immediate team collaboration.

By automating the process, platforms like Rootly have become some of the top SRE incident tracking tools because they free engineers to focus on solving the problem.

Example High-Performance SRE Stack

Combining these tools creates a complete, end-to-end solution for observability and response. This setup provides a powerful SRE observability stack for Kubernetes that gives your team full control.

Metrics: Prometheus for collection and storage, visualized in Grafana.
Logs: Fluentd for collection and Loki for aggregation, queried and visualized in Grafana.
Traces: OpenTelemetry for instrumentation with Jaeger or Tempo as the backend, visualized in Grafana.
Incident Management: Rootly to automate the incident response process triggered by alerts from the stack.

Conclusion

A high-performance observability stack is essential for any SRE team that manages Kubernetes environments. By combining best-in-class open-source tools for metrics, logs, and traces, you gain deep insight into your system's behavior. Integrating this stack with a powerful incident management platform like Rootly turns that insight into swift, automated action. This end-to-end solution reduces Mean Time to Resolution (MTTR) and frees up your engineers to build more resilient systems.

Ready to connect your observability stack to automated incident management? Book a demo of Rootly today.