The 3 Pillars of a Kubernetes Observability Stack

For many platform teams, incident response involves frantically switching between browser tabs: a Grafana dashboard for metrics, a Kibana window for logs, and a Jaeger UI for traces. Correlating a spike in CPU usage with a specific error log and a slow downstream service call is a manual, time-consuming process that slows down resolution. A modern Kubernetes observability stack solves this by integrating these data streams into a single, cohesive system. This guide will walk you through the three pillars of observability—metrics, logs, and traces—and explain how to combine them to create a unified view for faster, more effective troubleshooting across your entire fleet.

Key takeaways:

• Go beyond basic monitoring with the three pillars: Metrics tell you that a problem exists, logs provide the specific error context, and traces reveal why a request failed across distributed services. You need all three correlated to move from reactive alerts to proactive problem-solving.
• Adopt open-source standards, but plan for operational costs: While tools like Prometheus, Fluent Bit, and OpenTelemetry are the industry standard, integrating and managing them across a fleet of clusters is a significant engineering task. A successful strategy accounts for the operational overhead of managing data pipelines, storage, and configuration drift.
• Unify your observability stack to reduce troubleshooting time: The real power of observability comes from integrating your tools into a cohesive system. Using a platform like Plural to automate the deployment and management of your stack through a single pane of glass eliminates context-switching between UIs and allows your team to correlate data and find root causes faster.

What Is Kubernetes Observability (And Why You Need It)

Kubernetes observability is the discipline of instrumenting clusters so you can understand system behavior from first principles, not just surface-level health signals. Rather than relying on binary checks or static alerts, observability enables teams to ask ad-hoc questions about performance, reliability, and failure modes as they arise. This is critical in distributed systems where workloads are ephemeral, dependencies are dynamic, and failure patterns are rarely predictable.

In practice, observability connects application behavior with infrastructure state and network conditions to explain why a system behaves the way it does. Instead of only seeing that a Pod restarted, you can trace the precise sequence of events that triggered the failure. Platforms like Plural consolidate metrics, logs, and traces into a unified interface, making high-volume telemetry data usable by both platform and application teams. At scale, this integration is not optional—it is foundational to operating cloud-native systems reliably.

Taming Kubernetes Complexity

Kubernetes amplifies complexity by design. A single request may traverse multiple services, cross node boundaries, and interact with shared infrastructure components. Each hop introduces potential failure points, and the dynamic nature of scheduling makes manual debugging impractical.

Observability reduces this complexity by correlating signals across layers of the stack. Metrics reveal resource pressure, logs expose application context, and traces show request paths and latency accumulation. When viewed together, these signals allow teams to quickly narrow the search space and identify root causes. Centralizing this data is essential; without it, engineers are left stitching together partial views from disconnected tools.

Observability vs. Monitoring

Monitoring and observability are related but distinct concepts. Monitoring focuses on detecting known failure conditions. You define thresholds, collect metrics, and trigger alerts when expectations are violated. This answers the question of what is happening—for example, an elevated error rate or sustained CPU saturation.

Observability is about understanding why those symptoms occur. It enables investigation of unexpected behaviors without requiring preconfigured alerts. Monitoring might tell you a service is failing; observability gives you the evidence needed to reconstruct the causal chain that led to that failure. In mature Kubernetes environments, monitoring is necessary but insufficient on its own.

Debunking Common Observability Myths

A common misconception is that production-grade observability is prohibitively complex. While assembling and operating individual tools like Prometheus and Grafana independently can be operationally expensive, platforms like Plural abstract this complexity behind a consistent GitOps workflow. This lowers the barrier to adoption while preserving flexibility.

Another myth is that AI adds limited value to observability. In reality, automated correlation and pattern detection significantly reduce time spent on manual triage. Plural’s AI Insight Engine accelerates root cause analysis by correlating signals across infrastructure and applications, allowing engineers to focus on remediation instead of data gathering.

Finally, some teams assume observability tools are too specialized for broad use. A well-designed, integrated experience makes telemetry data accessible to developers and operators alike, without requiring deep expertise in every underlying system. When observability is treated as a shared capability rather than a niche skill, teams resolve incidents faster and operate with greater confidence.

The Three Pillars of Observability

Operating Kubernetes reliably requires more than collecting telemetry; it requires the ability to interrogate system behavior without knowing the failure mode in advance. Observability enables that by combining complementary signals that explain what is happening and why. A production-grade strategy is built on three interconnected data types: metrics, logs, and traces. Each answers a different class of questions, and none is sufficient on its own.

Metrics surface symptoms at scale, logs provide precise execution context, and traces reveal causal paths across distributed services. Correlating these signals allows teams to move from reactive firefighting to systematic diagnosis and optimization—critical in environments where workloads are ephemeral and dependencies are constantly shifting.

Metrics: Quantitative Signals at Scale

Metrics are time-series measurements that describe system behavior numerically—CPU utilization, memory pressure, request latency, error rates. They are the backbone of monitoring and the fastest way to detect anomalies across a fleet.

In Kubernetes, core components such as the kube-apiserver, kubelet, and etcd emit critical metrics. Additional state-level visibility comes from kube-state-metrics, which exposes metrics derived from Kubernetes objects like Pods, Deployments, and Nodes.

These metrics are typically scraped by Prometheus and stored in a time-series database. Historical retention enables trend analysis, capacity planning, and SLO-based alerting. Metrics are optimized for breadth and aggregation—they tell you that something is wrong, quickly and reliably.

Logs: High-Fidelity Event Context

Logs are immutable, timestamped records of discrete events produced by applications and system components. Where metrics indicate a failure, logs explain the conditions that produced it. In Kubernetes, applications write to stdout and stderr, and the node runtime surfaces those streams via the kubelet.

Because logs are generated across many nodes and short-lived Pods, they must be centralized. Lightweight agents such as Fluent Bit run on each node to collect, parse, enrich, and forward logs to a shared backend. Centralization enables cluster-wide search, correlation, and retention, which is essential for debugging, incident forensics, and security analysis.

Logs trade aggregation for precision. They provide the exact error messages, stack traces, and state transitions that metrics intentionally abstract away.

Traces: End-to-End Request Paths

Traces model the lifecycle of a single request as it propagates through a distributed system. Each operation in the path is captured as a span with timing and metadata, allowing you to understand where latency accumulates or failures originate.

Modern Kubernetes tracing is standardized around OpenTelemetry, which instruments applications and infrastructure to emit spans consistently. These spans are collected, processed, and exported—often via an OpenTelemetry Collector—to tracing backends such as Jaeger or Zipkin.

Traces answer questions that neither metrics nor logs can resolve alone: which downstream dependency caused a timeout, how retries amplified latency, or where contention emerged under load. In microservice-heavy architectures, they are the primary tool for understanding causal relationships.

Why You Need All Three

Each pillar optimizes for a different dimension: metrics for scale, logs for detail, and traces for causality. Using only one creates blind spots. Metrics without logs lack explanation. Logs without traces lack structure. Traces without metrics lack system-wide context.

A unified observability stack correlates these signals so engineers can pivot naturally—from an alerting metric, to the relevant logs, to the exact request trace—without switching mental models or tools. This correlation is what turns raw telemetry into operational insight and makes Kubernetes systems tractable at scale.

How to Use Metrics in Kubernetes

Metrics form the quantitative backbone of Kubernetes observability. They provide continuous, numerical signals that describe system behavior—CPU usage, request latency, error rates, and resource saturation. Nearly every Kubernetes component emits metrics, from the control plane to node-level agents, making metrics the fastest way to understand system health at scale.

When collected and analyzed consistently, metrics enable dashboards for real-time visibility, alerts for known failure modes, and automation such as Horizontal Pod Autoscaling. Operationally, using metrics effectively involves three concerns: choosing the right signals, collecting them reliably, and presenting them in a way operators can act on. At fleet scale, managing this toolchain manually becomes costly, which is why platforms like Plural package metrics infrastructure as a first-class capability.

Key Kubernetes Metrics to Track

A useful metrics strategy spans multiple layers of the Kubernetes stack. Control plane components such as the kube-apiserver, kubelet, and kube-proxy expose performance and reliability signals. To understand the state of Kubernetes objects themselves, you also need kube-state-metrics, which converts object status into consumable metrics.

Commonly tracked metrics include:

• Node metrics: CPU, memory, disk, and network utilization per node
• Pod metrics: CPU and memory usage relative to requests and limits
• Control plane metrics: API server request latency and error rates
• Workload metrics: Desired vs. available replicas for Deployments and DaemonSets

These metrics answer foundational operational questions such as whether nodes are resource-constrained or whether a rollout is failing. They also serve as the input for alerts and SLOs that allow teams to detect issues early rather than react to outages.

Collect Metrics with Prometheus

Prometheus is the de facto standard for metrics collection in cloud-native environments. It uses a pull-based model, periodically scraping HTTP endpoints exposed by Kubernetes components and applications. The collected data is stored in a purpose-built time-series database optimized for aggregation and range queries.

In Kubernetes, Prometheus integrates with service discovery to automatically find scrape targets such as nodes, Pods, and the API server. A single Prometheus instance is often sufficient for small environments. At scale, however, organizations typically layer on systems like Thanos or Cortex to aggregate metrics across clusters, increasing operational complexity. Plural reduces this burden by shipping Prometheus as a pre-configured, GitOps-managed application that can be deployed consistently across an entire fleet.

Visualize Metrics with Grafana

Metrics are only useful if humans can interpret them. Grafana sits on top of Prometheus to provide query, visualization, and dashboarding capabilities. It allows teams to correlate signals—such as CPU usage alongside request latency—to identify trends and anomalies quickly.

While Grafana excels at exploratory analysis, jumping between standalone dashboards and tools introduces friction during incidents. Plural addresses this by embedding Kubernetes metrics directly into its platform, presenting a unified operational view without requiring teams to manage separate Grafana instances or authentication layers. This integrated approach shortens the path from detection to diagnosis and keeps metrics actionable in day-to-day operations.

Making Sense of Kubernetes Logs

Metrics surface symptoms, but logs explain causes. As the second pillar of observability, logs are timestamped records of discrete events emitted by applications and system components. They provide the execution-level context required for root cause analysis, post-incident forensics, and security investigations. In Kubernetes—where Pods are ephemeral and workloads are highly distributed—a coherent logging strategy is a hard operational requirement, not an optional enhancement.

Logs capture application errors, stack traces, control plane events, and audit signals. The challenge is not generation but durability and accessibility: logs must survive Pod restarts, node failures, and rescheduling events. A production-grade logging pipeline transforms this high-volume event stream into a queryable, correlated data source that engineers can rely on during incidents.

Understanding the Kubernetes Logging Architecture

Kubernetes logging starts with container stdout and stderr. Each container writes logs to these streams, and the kubelet on each node captures them and persists them as local files. Operators can retrieve these logs using kubectl logs, which is sufficient for quick, local debugging.

This default model breaks down quickly at scale. Logs are bound to the Pod and node lifecycle; when a Pod is evicted or a node fails, its logs are lost. There is no native aggregation, retention, or historical search. As a result, debugging after the fact—or correlating events across services—is effectively impossible without external infrastructure.

Centralizing Logs with Fluentd and Fluent Bit

To make logs durable and usable, clusters deploy node-level logging agents as a DaemonSet. These agents run on every node, tail container log files, enrich entries with Kubernetes metadata, and forward them to a centralized backend.

The two most common agents are Fluentd and Fluent Bit. Fluent Bit is optimized for low resource usage and is frequently used as a forwarder. Fluentd offers more advanced parsing, filtering, and routing when complex processing is required. Together, they decouple log collection from workload lifecycles, ensuring event data is retained even as infrastructure churns.

Log Aggregation and Analysis Backends

Once logs are centralized, they must be indexed and queried. Common backends include Elasticsearch, OpenSearch, and Grafana Loki. Elasticsearch and OpenSearch provide powerful full-text search and analytics for complex investigations. Grafana Loki takes a metadata-first approach, indexing labels instead of full log content, which reduces storage costs and performs well for most operational queries.

These systems turn raw logs into actionable evidence. Plural simplifies the operational burden by packaging log collection, storage, and visualization into a unified observability stack. With logs available alongside metrics and traces in the Plural console, engineers can pivot directly from an alerting signal to the exact events that caused it—without switching tools or losing context.

Tracing Requests Across Your System

In Kubernetes, a single user request often fans out across multiple microservices, data stores, and external dependencies. Metrics summarize behavior and logs capture individual events, but neither can reconstruct the full execution path of that request. Distributed tracing fills this gap by modeling the end-to-end lifecycle of a request as it traverses your system.

Tracing is critical for diagnosing latency and understanding service dependencies. When an API response is slow, traces show exactly where time is spent—whether in a downstream service call, a retry loop, or a database query. By correlating spans with logs and metrics, tracing turns isolated signals into a coherent causal narrative. Without it, performance debugging in a microservices architecture devolves into educated guesswork.

The Fundamentals of Distributed Tracing

Distributed tracing models a request as a trace, composed of multiple spans. Each span represents a unit of work—an HTTP call, a cache lookup, a database query—and records timing, metadata, and relationships to other spans. Parent-child relationships between spans define the request graph, making dependencies and critical paths explicit.

This structure allows you to answer questions that are otherwise hard to resolve: which dependency dominates latency, where retries occur, or how failures propagate across services. Traces are especially valuable in Kubernetes, where dynamic scheduling and horizontal scaling obscure static assumptions about request flow.

Collect Traces with OpenTelemetry

Generating traces requires explicit instrumentation. OpenTelemetry has become the standard approach, providing vendor-neutral APIs, SDKs, and protocols for emitting spans consistently across languages and runtimes.

Applications emit spans using OpenTelemetry libraries, which are then received by an OpenTelemetry Collector. The collector handles batching, sampling, enrichment, and export, and can run as a node-level agent or a centralized service. This architecture decouples instrumentation from backend choice and gives platform teams fine-grained control over trace volume and routing.

Analyze Traces with Jaeger and Zipkin

Collected traces must be stored and visualized to be useful. Common open-source backends include Jaeger and Zipkin. These systems index trace data and provide UIs for searching and visualizing traces as timelines or flame graphs.

These visualizations make performance bottlenecks immediately apparent. Engineers can inspect individual spans, examine metadata, and correlate trace IDs with logs to understand both where a problem occurred and why. When integrated with metrics and logs in a unified platform like Plural, tracing completes the observability loop, enabling fast, evidence-driven debugging across the entire Kubernetes fleet.

Build Your Observability Stack with Open-Source Tools

A production-grade observability stack does not require proprietary platforms or vendor lock-in. The Kubernetes ecosystem has converged on a mature set of open-source tools that cover metrics, logs, and traces end to end. These projects are widely adopted, battle-tested, and designed to work together. The challenge is no longer tool availability, but operational complexity.

Plural addresses this by packaging these open-source components into a curated catalog and managing them via GitOps. Your observability stack is deployed, upgraded, and audited the same way as application workloads, reducing drift and operational overhead while preserving full control over the underlying tools.

The Prometheus Ecosystem

For metrics, Prometheus is the de facto standard in Kubernetes environments. Its pull-based scraping model aligns well with dynamic service discovery, making it resilient to Pod churn and cluster changes. Prometheus excels at real-time alerting and short- to medium-term analysis.

At scale, however, standalone Prometheus instances become limiting. Long-term retention, high availability, and cross-cluster queries require additional components. Projects like Thanos and Cortex extend Prometheus with object storage, global query layers, and horizontal scalability. These systems enable fleet-wide visibility but introduce operational complexity that Plural abstracts through standardized, GitOps-managed deployments.

The ELK Stack and Modern Alternatives

For logs, the traditional choice has been the ELK stack: Elasticsearch, Logstash, and Kibana. In Kubernetes, logs are typically collected by Fluentd or Fluent Bit and forwarded to the backend for indexing and search.

While powerful, ELK can be resource-heavy to operate. This has driven adoption of alternatives such as OpenSearch, a fully open-source fork, and Grafana Loki. Loki takes a different approach by indexing only log metadata rather than full text, dramatically reducing storage costs while remaining effective for most operational queries. Plural allows teams to standardize on the backend that best fits their scale and budget without changing collection pipelines.

Why OpenTelemetry Is the New Standard

OpenTelemetry has become the unifying layer across observability. Instead of instrumenting applications separately for metrics, logs, and traces, OpenTelemetry provides a single, vendor-neutral specification and set of SDKs. Instrument once, export anywhere.

The OpenTelemetry Collector acts as a control plane for telemetry data. It receives signals, applies sampling and enrichment, and routes data to backends such as Jaeger or Zipkin for traces, Prometheus-compatible systems for metrics, and Loki for logs. This decoupling future-proofs your observability strategy and avoids tight coupling to any single vendor or storage engine.

Plural builds on OpenTelemetry as a first-class primitive, allowing teams to standardize instrumentation while remaining flexible in backend choice. The result is an observability stack that is open, composable, and operable at scale—without sacrificing developer velocity or operational rigor.

How to Integrate Your Observability Tools

Running separate tools for metrics, logs, and traces is table stakes, but leaving them disconnected creates operational silos. During incidents, engineers end up correlating a latency spike in Grafana with an error in Kibana and a slow span in Jaeger by hand. This context switching is inefficient and directly increases mean time to resolution.

Effective observability treats telemetry as a single narrative. Metrics, logs, and traces should flow through an integrated system where pivots between signals are first-class operations. This is the difference between watching dashboards and understanding causality. Platforms like Plural operationalize this approach by providing a single pane of glass for deploying, wiring, and viewing observability components so they work together by default.

Build a Unified Observability Pipeline

A unified pipeline ingests metrics, logs, and traces through consistent collection paths and forwards them to centralized backends for storage and analysis. Instead of managing separate agents and configurations for each signal, teams standardize ingestion and enrichment, which reduces drift and operational overhead.

In practice, this means using a common collection layer—most commonly an OpenTelemetry Collector—to receive telemetry from applications and infrastructure, apply sampling and metadata enrichment, and export to the appropriate backends. With a unified pipeline, investigations start with complete context already available, rather than assembling evidence during an outage.

Plural simplifies this by packaging and managing these pipelines via GitOps, ensuring consistency across clusters and environments without bespoke configuration.

Correlate Metrics, Logs, and Traces

Collection alone is not enough; correlation is where observability delivers value. Correlation enables engineers to move fluidly between signals. An alert fires on elevated latency (metric), which links directly to traces captured during the same window. A slow span identifies the responsible service, and from there, logs provide the exact error or timeout that caused the degradation.

This workflow transforms isolated telemetry into causal analysis. It is what separates observability from monitoring. Correlation relies on shared context—trace IDs, consistent labels, timestamps—which is why unified pipelines and standardized instrumentation are critical in Kubernetes environments.

Using eBPF for Deeper, Lower-Overhead Collection

Traditional observability often depends on application-level instrumentation or sidecars, which can add operational and performance overhead. A newer approach leverages eBPF (extended Berkeley Packet Filter) to collect telemetry directly from the Linux kernel.

eBPF programs run safely in kernel space and can observe system calls, network traffic, and scheduling behavior without modifying application code or loading kernel modules. For Kubernetes observability, this enables automatic capture of metrics, logs, and traces with minimal overhead and broad coverage across workloads. Because it operates below the application layer, eBPF can provide visibility even for legacy or third-party binaries.

As eBPF-based tooling matures, it is increasingly used to complement traditional instrumentation, especially for network and performance analysis. Integrated platforms like Plural can incorporate these data sources alongside OpenTelemetry-based pipelines, giving teams deep visibility without sacrificing operational simplicity.

Best Practices for Your Observability Stack

Deploying observability tools is only the first step. To get meaningful value from your stack, you need a strategy for managing it effectively. This involves defining clear goals, securing your data, and automating routine tasks to ensure your system remains efficient, secure, and scalable as your environment evolves. Adopting these best practices will help you move from simply collecting data to generating actionable insights that improve system reliability and performance.

Set Clear Objectives and Smart Alerts

An effective observability strategy begins with clear goals. Before you dive into dashboards, define what acceptable performance looks like for your applications by setting Service Level Objectives (SLOs). These objectives provide the context needed to interpret your data. Without them, you risk drowning in information and suffering from alert fatigue, where constant, low-impact notifications cause teams to ignore critical warnings. As one guide on Kubernetes observability best practices notes, "You don't need to fix every tiny issue if it doesn't affect your main goals." Focus on creating smart alerts tied directly to your SLOs. An alert should be actionable and signify a real threat to user experience or system health, making your on-call rotations more effective.

Address Security and Compliance

Your observability stack collects sensitive operational data, including logs that may contain user information and metrics that reveal system vulnerabilities. Securing this data is critical. It’s a common misconception that Kubernetes handles security for you; as industry experts point out, "Kubernetes is simply a way to build a solution, not a security platform." You must build security and compliance into your observability architecture from the ground up. Start by implementing strong Role-Based Access Control (RBAC). Plural simplifies this by integrating with your existing identity provider, allowing you to manage access using the same user and group definitions across your fleet. You can define granular permissions within each cluster to ensure engineers only see the data relevant to their roles, which is essential for meeting compliance standards like GDPR and HIPAA.

Automate and Maintain Your Stack

In a dynamic Kubernetes environment, manual management of your observability stack is not sustainable. Automation is key to maintaining consistency, controlling costs, and ensuring your tools can scale with your workloads. By codifying the configuration of tools like Prometheus and Grafana and managing them through a GitOps workflow, you create a single source of truth that simplifies updates and prevents configuration drift. Automation also plays a crucial role in resource optimization. As one report highlights, organizations can "significantly reduce cloud waste and improve resource utilization with automation." Plural’s GitOps-based continuous deployment automates the entire lifecycle of your observability tools, from provisioning infrastructure with Plural Stacks to deploying and configuring agents, helping you turn insights into fast, automated actions.

How Plural Simplifies Kubernetes Observability

Building a robust observability stack from open-source tools is a significant engineering effort. You have to select the right components, integrate them, manage their data pipelines, and secure access across your organization. This process is complex and often distracts platform teams from their core mission of enabling developers. Instead of wrestling with YAML files and disparate UIs, you need a streamlined way to deploy, manage, and consume observability data across your entire Kubernetes fleet.

Plural provides a unified platform to manage the complete lifecycle of your observability stack. By leveraging a GitOps-based workflow and an integrated application marketplace, Plural automates the deployment of tools like Prometheus, Grafana, and Loki. This approach transforms observability from a complex, manual setup project into a repeatable, self-service capability. The result is a powerful, secure, and scalable observability solution that is managed through a single pane of glass, giving your teams the insights they need without the operational overhead. Plural handles the integration, so you can focus on what the data is telling you.

Get a Single Pane of Glass with Integrated Dashboards

To diagnose an issue in a typical Kubernetes environment, an engineer might first check a Grafana dashboard for metric anomalies, then pivot to a separate logging tool like Kibana to find related error messages, and finally open Jaeger to trace the problematic request. This constant context-switching between different UIs makes it difficult to correlate data and quickly identify the root cause.

Plural solves this by embedding a Kubernetes dashboard directly into its console, providing a single interface for all observability data. By gathering and connecting information from the control plane, add-ons, and your applications, Plural creates a complete, contextualized view of your system's health. This unified dashboard visualizes metrics, logs, and traces together, allowing your team to move seamlessly from a high-level overview to granular details without ever leaving the platform.

Automate Deployment and RBAC Integration

Setting up a complete observability stack often involves manually installing multiple components with Helm charts and then configuring them to work together—a process that is both time-consuming and prone to error. Plural automates this entire workflow. Using the Plural marketplace, you can deploy a full, pre-integrated observability stack with a few clicks, ensuring a consistent and reliable setup across all your clusters.

More importantly, Plural integrates this stack with Kubernetes-native security controls. Access to the embedded dashboard is managed through Kubernetes Impersonation, meaning all permissions are defined using standard ClusterRole and ClusterRoleBinding objects. This allows you to leverage your existing identity provider via OIDC for a true SSO experience. Your existing user and group definitions directly control who can see what, ensuring that sensitive observability data is protected without requiring a separate access management system.

Related Articles

• Master Kubernetes Observability: Logs, Metrics & Traces
• How to Monitor Kubernetes Clusters Effectively
• Kubernetes Monitoring: The Ultimate Guide (2024)

Frequently Asked Questions

Isn't observability just a new name for monitoring? Not quite. Think of it this way: monitoring tells you when something is wrong, like a spike in CPU usage. It's based on predefined dashboards and alerts for problems you already know can happen. Observability is the next step; it gives you the rich, detailed data you need to ask new questions and understand why that CPU spike occurred. It’s about having the ability to explore your system's behavior to solve novel problems you couldn't predict.

Can I get by with just metrics and logs, or do I really need traces too? You can certainly start with metrics and logs, and they will help you identify that a problem exists and what error occurred. However, in a distributed system with many microservices, they won't show you the full story. Traces are what connect the dots. They follow a single request from start to finish across all services, showing you exactly where bottlenecks or failures happen. Without traces, finding the root cause of latency is often a process of educated guesswork.

Setting up and integrating all these open-source tools seems like a lot of work. Is there an easier way? You're right, manually deploying and configuring tools like Prometheus, Grafana, and Loki for each cluster is a significant operational burden. This is precisely the problem we solve at Plural. Our platform automates the entire process through a GitOps workflow. You can deploy a complete, pre-integrated observability stack from our application catalog with a consistent, repeatable process, which frees up your team to focus on analyzing data instead of managing tooling.

How do I prevent my observability data from becoming too expensive to store and manage? Controlling costs is a critical part of any observability strategy. It starts with being intentional about the data you collect and how long you keep it. Implementing smart data sampling for traces and setting clear retention policies to archive or delete older logs can significantly reduce storage needs. Choosing cost-effective tools, like Grafana Loki which indexes metadata instead of full log content, also makes a big difference. The goal is to capture high-value data without letting costs spiral out of control.

How can I control who sees what in our observability tools? Securing your observability data is essential, especially since logs can contain sensitive information. The best approach is to implement strong Role-Based Access Control (RBAC). Plural simplifies this by integrating directly with your existing identity provider for a single sign-on experience. Access to our embedded dashboard is managed through native Kubernetes RBAC, allowing you to use standard ClusterRoleBindings to grant permissions to specific users or groups. This ensures your engineers can only view the data relevant to their roles, helping you maintain security and compliance.