Build an SRE Observability Stack for Kubernetes in 2025

For Site Reliability Engineers (SREs), building an effective observability stack for Kubernetes is more critical than ever. As systems grow in complexity with microservices and dynamic infrastructure, traditional monitoring is no longer sufficient. The focus in 2025 is shifting from simply collecting data to enabling intelligent, automated action. This guide will walk you through how to build a modern sre observability stack for kubernetes, combining foundational data collection with an intelligent incident management layer to enhance reliability and reduce toil.

The Challenge: Why Traditional Kubernetes Monitoring is Falling Short

The increasing complexity of cloud-native environments presents significant challenges for SRE teams. According to the SRE Report 2025, toil levels for SREs have recently increased after years of reduction, partly due to the intricacies of modern systems [4]. A traditional observability stack often suffers from several common pain points:

• Alert Fatigue: A high volume of low-priority or duplicate alerts quickly desensitizes on-call engineers, causing them to miss critical signals.
• Data Silos: Metrics, logs, and traces often reside in separate, disconnected systems. This requires engineers to manually correlate data across different tools during an incident, which slows down the response.
• Manual Toil: SREs spend too much time on repetitive tasks like diagnosing common issues, managing incident communication, and executing remediation steps, pulling them away from proactive engineering work.

The Three Pillars: Foundational Data Collection for Kubernetes

A complete observability stack is built on three pillars: metrics, logs, and traces. These form the essential data-gathering foundation, and several open-source tools have become the standard for this purpose.

Metrics: Prometheus as the Standard

Prometheus is the de facto standard for collecting time-series metric data in Kubernetes environments [6]. It excels at scraping high-cardinality data from cluster components, nodes, and applications, providing a real-time view of system health. This data is often paired with Grafana, which provides powerful dashboards and visualizations to help SREs understand performance trends and identify potential issues.

Logs: FluentBit and Vector for Aggregation

Log aggregation is essential for debugging applications and understanding event sequences. Tools like FluentBit and Vector are popular choices for this task. They are lightweight, high-performance log collectors and processors that can gather logs from all pods and nodes in a cluster, parse them, and forward them to a centralized storage backend for analysis.

Traces: OpenTelemetry for Distributed Tracing

In a microservices architecture, traces are crucial for understanding the complete lifecycle of a request as it travels across different services. OpenTelemetry has emerged as the industry standard for generating and collecting distributed traces, providing end-to-end visibility into request flows. This unified approach helps SREs pinpoint bottlenecks and errors within complex, distributed systems [5].

From Data to Action: The Missing Intelligence Layer

Simply collecting metrics, logs, and traces is not enough. The real challenge is making sense of this vast amount of data and acting on it quickly during an incident. This is where AI-powered monitoring, or AIOps, comes in as the next evolutionary step.

AIOps leverages machine learning to proactively identify anomalies, correlate events, and automate analysis, moving beyond the reactive nature of traditional monitoring. This shift is gaining momentum; a recent forecast shows that the use of AI monitoring capabilities has jumped from 42% in 2024 to 54% in 2025, as more organizations move from experimentation to live deployment [3]. By comparing real-time data against historical patterns, an AI-powered monitoring system can provide proactive insights that help SREs prevent outages before they happen.

Rootly: The Intelligent Action Layer for Your Kubernetes Stack

Rootly is the intelligent orchestration and incident management software that sits on top of your data foundation. It bridges the gap between observability insights and automated action, solving the "so what?" problem that often comes with dashboards and alerts. By integrating with your existing observability tools, Rootly centralizes and automates the entire incident lifecycle.

Automating Incident Response to Reduce Toil

Rootly automates the procedural work associated with incident response, making it one of the best tools for on-call engineers. Instead of manually creating communication channels, paging team members, and documenting timelines, Rootly does it all. For example, it can automatically trigger a Kubernetes rollback when a failed deployment is detected, drastically reducing Mean Time to Recovery (MTTR). This kind of automated remediation for Kubernetes frees up engineers to focus on root cause analysis rather than manual firefighting. With its native Kubernetes integration, Rootly can watch for events and pull critical context directly from the cluster, giving responders the information they need without having to switch contexts.

Designing Smart Escalation to Combat Alert Fatigue

Alert fatigue is a significant source of burnout and missed incidents. Rootly addresses this by enabling smart escalation policies and intelligent alert routing. You can define alert urgency based on the payload, create on-call schedules, and set up multi-level escalation paths to ensure the right engineer is notified about the right issue at the right time. This aligns with the broader observability trend of adopting smarter data management to reduce noise and cut costs, with some organizations seeing savings of 60-80% by being more selective about the data they collect and alert on [1].

Putting It All Together: Your 2025 SRE Stack Blueprint

Building a modern observability stack is a clear, multi-step process that combines data collection with an intelligent action layer.

• Step 1: The Data Foundation.
  • Collect metrics with Prometheus.
  • Aggregate logs with FluentBit or Vector.
  • Gather traces with OpenTelemetry.
• Step 2: The Intelligence & Action Layer.
  • Integrate your data sources with Rootly.
  • Configure workflows to automate incident creation, communication (for example, Slack channels), and paging.
  • Set up automated remediation actions like Kubernetes rollbacks for deployment failures.
• Step 3: Unify and Secure Your View.
  • A modern stack should also consider security. Observability data can provide critical security insights, helping you identify anomalies like crypto-mining containers or unusual network traffic that could signal a DDoS attack. Bridging observability and security provides a more holistic view of system health and resilience [7].

Conclusion: The Future is Automated and Action-Oriented

A modern sre observability stack for kubernetes is not just about data collection; it's about intelligent automation and rapid response. By combining a solid data foundation (metrics, logs, and traces) with an intelligent action layer, SRE teams can effectively manage complex systems, reduce toil, and improve reliability.

Tools like Rootly are no longer optional but essential for SREs who want to move from reactive firefighting to proactive engineering. This two-layer approach empowers teams to build more resilient, self-healing systems in 2025 and beyond by embracing the shift from traditional monitoring to AI-augmented incident management.

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