From pillars to rings: How interconnected observability in Grafana Cloud optimizes performance and reduces telemetry waste
In observability, we’ve traditionally been taught to think in terms of pillars, namely logs, metrics, and traces (and more recently, profiles). But pillars are rigid and disconnected. They don’t reflect how modern systems actually work or how we troubleshoot in real time.
So let’s change that. Instead of pillars, think in terms of rings—rings that act as interconnected layers of observability, each building on the other, wrapped around a central concept of opinionated observability solutions.
In this blog, I’ll walk through how we combine metrics, logs, and traces in context to deliver stronger observability outcomes. By connecting these signals, we’re able to build opinionated solutions in Grafana Cloud—solutions built to optimize performance, minimize waste, and help you focus on the data that truly matters.
The inner core: The three telemetry rings
Metrics, logs, and traces are not separate tools; they’re connected lenses on the same system. But before we dig into how to use them in tandem, let’s quickly define what each signal does.
- Traces reveal the path a request takes through your system. With OpenTelemetry and tools like Grafana Cloud Traces (powered by Grafana Tempo), you can have a transaction-level view. Traces can tell you what happened, where it slowed down, and how it connects across services.
- Logs tell the granular story of events. When structured, centralized, and contextualized with tools like Grafana Cloud Logs (powered by Grafana Loki), they stop being noisy and start explaining why something happened.
- Metrics summarize the system’s current and historical state. Think CPU usage, request latency, error rates. Grafana Cloud Metrics (powered by Grafana Mimir) supports Prometheus, OTel metrics, and more, giving you the pulse of your system.
Each ring serves a unique role, but they are meant to be viewed together, giving your telemetry context to help you drive actions and outcomes.
You might be wondering: What about profiles?
While not traditionally counted among the three core pillars of observability, at Grafana Labs we see profiles as the emerging fourth signal. When used alongside metrics, logs, and traces, profiles add critical depth and help teams get even more value out of their observability stack.
That said, this blog will focus on how we use metrics, logs, and traces today since those are the signals organizations are most likely to start with and put in siloes. When you’re ready to take that next step on your maturity journey—and we highly recommend you do—check out this blog on Grafana Cloud Profiles to learn more on how profiles are becoming part of our out-of-the-box solutions.
Moving beyond the signals: What happens when you connect the rings
Collecting logs, metrics, and traces is not enough. Many teams stop here, believing they’ve “done observability” because they have dashboards and alerts. But this is just the beginning.
Yes, you can build your own dashboards and configure your own alerts. But these tools are raw materials—powerful but unrefined. To evolve beyond dashboards, we combine:
- Observability best practices like structured logging, low-cardinality metrics, distributed tracing, RED metrics, and golden signals
- Technology best practices like service discovery, container orchestration, cloud architecture patterns, Kubernetes readiness/liveness probes, etc.
When these are integrated and when you know not just what to observe, but why it matters and what to do with the insights, then you reach what we call:
Opinionated observability in context
An opinionated observability solution delivers automated, context-rich, cost-effective visibility by bundling:
- The technology being observed: The system context
- Operational best practices: Guidance on how to keep the system healthy and performant
- Observability best practices: Surfacing signal from noise, ingesting only what’s needed (and only when it’s needed), minimizing cost, and enabling automation wherever possible
When telemetry rings are connected, and observability is delivered in opinionated, contextual layers, the results are measurable:
- Faster resolution (lower MTTR): Root cause detection in minutes, not hours
- Greater operational efficiency: Fewer false alerts, tighter SLO compliance
- Lower cloud cost: Right-sizing resources with accurate, contextual data
This is not the future. This is happening today! Opinionated observability in context doesn’t just answer, “What’s happening?” It answers three questions every engineering team really needs to know:
- What should be observed? Curated dashboards, alerting rules, SLOs, and telemetry configuration aligned with known best practices (like SRE principles, golden signals, and cloud native patterns).
- Why is it broken? Correlation across signals—latency spikes, error rates, degraded SLOs—connected back to trace paths and logs that explain the root cause, not just the symptom.
- What should you do about it? Recommendations, remediations, and playbooks triggered in context not by humans scanning a dozen tabs, but by pre-wired workflows built into the solution.
This model is already elevating the standard observability experience through Grafana Cloud solutions: Kubernetes Monitoring, Cloud Provider Observability, Application Observability, and Frontend Observability (powered by our knowledge graph).
Real examples of opinionated observability in context
Kubernetes Monitoring
Our Kubernetes Monitoring solution provides out-of-the-box dashboards, alerts, and ML recommendations mapped to industry best practices for cluster health, pod behavior, and service performance.
It doesn’t just ingest data. It understands Kubernetes, visualizes thresholds, and stitches together logs and metrics across clusters, pods, nodes, workloads, and namespaces. This gives platform teams a complete contextual view without having to build it from scratch.
How it answers the three big questions:
- What should be observed? CPU, memory, restarts, network and storage usage
- Why is it broken? Contextual drilldowns into correlated logs
- What should you do about it? Alerts and dashboards tied to recommended remediations
Cloud Provider Observability
Each cloud monitoring solution—whether for AWS, Microsoft Azure, or Google Cloud—is tailored to the provider’s native services. Grafana Cloud connects to APIs and telemetry pipelines to surface logs and metrics from managed services for compute, storage, network, and many more.
The value lies in the pre-mapped service context so you don’t have to build your own dashboards or alerts. These solutions come preloaded with visuals, thresholds, and correlations that reflect the real architecture of your cloud deployments.
How it answers the three big questions:
- What should be observed? Cloud native service metrics and health checks
- Why is it broken? Pre-wired alerting and logs surfaced next to metrics
- What should you do about it? Runbook guidance based on cloud-specific anomalies (e.g., autoscaling failures, rate limit issues)
Grafana Cloud Knowledge Graph
Knowledge Graph (formerly Asserts) adds a critical layer to the observability stack: automated insight into service health, performance, and reliability using SLO-based analysis. Unlike dashboards that show everything, Knowledge Graph surfaces only the relevant context when something’s wrong, helping you focus on what matters most.
By analyzing telemetry, our knowledge graph identifies issues, correlates them to service dependencies, and explains them in plain language. It leverages opinionated thresholds, service boundaries, and historical patterns so you get answers, not alerts.
How it answers the three big questions:
- What should be observed? Signals, not noise, for every service
- Why is it broken? Automatic root cause analysis based on dependency mapping
- What should you do about it? Direct recommendations and explanations in real time
The final ring: an AI assistant that understands your systems
Surrounding the telemetry rings is a new, emerging ring of AI-powered observability. Grafana Assistant is built for observability workflows in Grafana Cloud, helping teams manage complex systems with automated insights, contextual alerts, and intelligent monitoring recommendations.
It interprets telemetry signals in human-readable language, then it can recommend what to do next. This means:
- Fewer war rooms
- Less time combing through dashboards
- More confidence in what you’re seeing and why it matters
Of course, AI will only be as good as the data and structure we feed it, which is why opinionated observability solutions are the perfect foundation. They provide clean, curated data that is intentionally tailored to surface the right signals at the right time.
Get ready for what’s next
The future of observability isn’t just about seeing your systems. It’s about understanding them and acting with confidence.
Sooner rather than later, this will be the default: telemetry connected by design, observability enriched with best practices, and AI interpreting it all with clarity.
We’re constantly expanding and improving Grafana Cloud. To keep tabs on all the latest updates, check out our What’s New from Grafana Labs page. And dig into our full list of solutions to find the one that fits your needs.
The rings are in motion. The next era of observability is connected, contextual, and AI-powered.
Grafana Cloud is the easiest way to get started with metrics, logs, traces, dashboards, and more. We have a generous forever-free tier and plans for every use case. Sign up for free now!
