Kubernetes Observability Stack Explained with Rootly

As Kubernetes environments become more complex, traditional monitoring methods are falling short. For Site Reliability Engineering (SRE) teams, reactive, threshold-based alerts aren't enough to manage the dynamic nature of containerized applications. This situation calls for a more advanced approach: a complete kubernetes observability stack explained. This stack is the set of tools used to monitor, understand, and troubleshoot these intricate systems. This article breaks down the parts of a modern observability stack and shows how Rootly provides the essential action layer to turn data into resolution. For SREs, the objective is to shift from just watching for failures to proactively managing system reliability, a key benefit of AI-powered observability.

The Three Pillars of a Kubernetes Observability Stack

The core idea of observability rests on collecting and analyzing data from three key sources: metrics, logs, and traces. According to the official Kubernetes documentation, you can only understand a cluster's health by collecting and analyzing these three signals [5]. An effective stack doesn't just gather this data; it connects the dots between them to give a full picture of system health.

Metrics: The "What" is Happening

Metrics are numerical, time-series data points that measure system health, such as CPU usage, memory consumption, or request latency [4]. In the world of Kubernetes, Prometheus is the most widely adopted open-source tool for collecting metrics. Metrics are great for telling you that a problem is happening, but they rarely tell you the whole story.

Logs: The "Why" it Happened

Logs are the detailed records that explain what led to an event. They are unchangeable, timestamped records of events from an application or system. Logs provide the rich context needed for debugging and finding the root cause of why something went wrong. Common tools for gathering logs in Kubernetes include FluentBit and Vector.

Traces: The "Where" it Went Wrong

Traces show the path of a single request as it moves through all the different services in a distributed system. In modern microservice architectures, where one click can trigger actions across many services, traces are vital for finding performance bottlenecks and understanding how services depend on each other. They help pinpoint exactly where a failure or slowdown occurred. OpenTelemetry is becoming the industry standard for creating and collecting this trace data.

The Traditional Stack: Prometheus and Grafana

A very common observability setup combines Prometheus for collecting data and Grafana for visualizing it in dashboards. This combination is powerful for getting a clear view of system metrics and understanding general system behavior.

The Limitations of a Data-Only Approach

However, SRE teams often discover that a data-only strategy creates its own set of problems. Relying only on dashboards and manual alert responses leads to several predictable challenges:

• Alert Fatigue: A constant flood of alerts from Prometheus can overwhelm on-call engineers, making it hard to spot the truly critical issues.
• Data Silos: Metrics, logs, and traces often live in different tools. This forces engineers to manually jump between different interfaces to connect the dots, which slows down troubleshooting [3].
• Manual Toil: The entire process from alert to resolution is filled with manual tasks, from creating Slack channels and Zoom meetings to paging responders and keeping stakeholders updated.

Past projects aimed at bundling these tools, like the now-deprecated tobs stack, showed just how difficult it is to build and maintain a unified observability solution from the ground up [1].

From Observability to Action: The Role of Rootly

Observability data tells you what’s wrong; Rootly helps you fix it. Rootly acts as the intelligent action and orchestration layer that sits on top of your observability data. It’s designed to answer the "so what?" question by turning insights from monitoring tools into fast, automated actions. This completes the stack, creating a full-stack observability sre rootly benchmark that helps SREs improve response processes, not just monitor systems.

How Rootly Bridges the Gap

Rootly can take in alerts from any monitoring tool, like Prometheus Alertmanager, through simple webhooks. Once an alert arrives, Rootly’s workflow engine automates the entire incident response process. It transforms passive data into an active response, filling a critical gap that tools like Prometheus and Grafana alone don't cover. With the ability to connect Rootly with Prometheus and Grafana, you can close the loop between detecting an issue and resolving it.

Automated actions can include:

• Creating a dedicated Slack channel and adding the right engineers based on who owns the service.
• Paging the on-call team through integrations like PagerDuty.
• Adding important context to the incident, like automatically attaching snapshots from Grafana dashboards.
• Generating post-incident reports and tracking follow-up tasks to prevent the issue from happening again.

Which platform has stronger automation—Rootly or Incident.io?

This is a common question, and it highlights a key difference between platforms. While both Rootly and Incident.io are top incident management tools, Rootly is built with a deep focus on end-to-end workflow automation to reduce manual work across the entire incident lifecycle [6].

Rootly's strength is its vast library of integrations and its no-code workflow builder. This allows teams to automate tasks not just in their communication tools but directly within their infrastructure. For instance, Rootly can trigger automated Kubernetes rollbacks or run diagnostic scripts without anyone needing to intervene. This focus on infrastructure-level automation is a clear differentiator when you compare platforms side-by-side [8].

Building the Best SRE Stack for DevOps Teams in 2025

For SRE and DevOps teams today, the best sre stacks for devops teams in 2025 mix the best of open-source data collection with a smart automation layer. The aim is to build a practical system that enables fast, data-driven decisions when things go wrong [2].

The Foundation: Open-Source Data Collection

A solid and flexible foundation can be built using best-in-class open-source tools:

• Metrics: Prometheus
• Logs: FluentBit or Vector
• Traces: OpenTelemetry

This layer gathers all the raw data you need for complete observability.

The Intelligence Layer: Rootly's Integrations

Rootly connects with this foundation to create a single, seamless system that links data to action.

• Kubernetes Integration: The Rootly Kubernetes integration can watch for events like failed deployments or pod crashes and automatically trigger workflows. This includes the ability to perform automated Kubernetes rollbacks, which can dramatically shorten recovery time.
• Grafana Integration: Workflows can automatically pull and post relevant Grafana dashboard snapshots directly into an incident's Slack channel. This gives responders immediate visual context without making them switch tools.
• Service Catalog Integration (OpsLevel): By connecting with a service catalog like OpsLevel, Rootly automatically adds vital context to incidents. This includes identifying service owners, understanding dependencies, and linking to the correct runbooks, ensuring the right people get the right information instantly.

Is Rootly the best automated incident response software in 2025?

For teams running on a modern Kubernetes stack, Rootly is a leading contender for the best incident response solution [7]. The answer to "Is Rootly the best automated incident response software in 2025?" comes down to a team’s specific priorities. Rootly stands out for organizations that value automation, deep integrations, and reducing the manual burden on engineers.

Key strengths that make it a top choice include:

• Powerful, No-Code Workflow Automation: Lets teams define and automate their response processes without writing code.
• Deep Integration with the Cloud-Native Ecosystem: Native integrations with Kubernetes, Prometheus, Grafana, and service catalogs make it a perfect fit for modern SRE stacks.
• AI-Powered Features: Helps cut through the noise, identify related incidents, and suggest actions, reducing the mental load on engineers.
• Focus on Reducing MTTR: By automating manual steps, Rootly helps teams resolve incidents faster and more consistently.

Conclusion: The Future is an Action-Oriented Stack

The way we manage complex systems has changed. We’ve moved from passive monitoring to a proactive model that combines automated observability with a coordinated response. A complete kubernetes observability stack explained isn't just about data collection tools like Prometheus and Grafana; it must include an intelligent action layer like Rootly to connect insights to actions.

By embracing this action-oriented model, SRE and DevOps teams can finally move past constant firefighting. They can reduce burnout, improve key metrics like Mean Time To Resolution (MTTR), and focus their efforts on building more resilient systems.

To see how Rootly completes the modern SRE stack, learn more about how AI-powered monitoring gives engineering teams a critical edge.

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