Grafana: The Reference Architecture for Unified Observability

TL;DR

In modern distributed systems, it’s no longer enough to just know if a server is up or down. You need to understand why it’s slow. While AWS CloudWatch provides a solid view of the infrastructure, visibility often ends at the cloud boundary. Grafana breaks through these silos. It acts as a universal visualization layer, unifying data from hundreds of sources (Prometheus, SQL, logs, traces) into a single interface. Those who use Grafana gain true end-to-end observability, regardless of where the data resides.

1. The Architecture Principle: Bring Your Own Data (BYOD)

Proprietary monitoring tools (like CloudWatch or Datadog) usually consist of a database and a UI that are tightly integrated. You have to send your data to them (and pay for it) to view it.

Grafana takes a different approach: It separates visualization from data storage.

• Datasources: Grafana does not store metrics itself. It connects to existing databases (Prometheus for metrics, Loki for logs, Postgres for business data).
• Single Pane of Glass: A single dashboard can display CPU load from AWS in Panel A, orders from the SQL database in Panel B, and error logs from an on-premise cluster in Panel C.

2. Core Feature: Correlation Instead of Isolation

The biggest problem in debugging is context switching. When the CPU spikes, you need to check CloudWatch. When the app throws errors, you need to check the logs. When the database hangs, you need a SQL tool.

Grafana solves this through correlation.

• Seamless Linking: See a spike in the graph? One click shows you the exact logs (via Loki) or traces (via Tempo) at that time.
• Business Context: Grafana allows you to overlay technical metrics (latency) with business metrics (revenue per minute). This way, you can immediately see if a technical error has financial implications.

3. Dashboards as Code & GitOps

In the CloudWatch world, dashboards are often manually assembled (“ClickOps”). This is fragile. If someone accidentally deletes a widget, it’s gone. Grafana dashboards are pure JSON objects. They can (and should) be versioned in Git. Changes to dashboards go through the same review process as application code. With tools in the ayedo stack, a dashboard is automatically updated when you change the JSON file in Git.

4. Operational Models Compared: AWS CloudWatch vs. ayedo Managed Grafana

This is where it is decided whether observability is a strategic asset or a monthly tax.

Scenario A: AWS CloudWatch (The Cost Trap) CloudWatch is enabled by default but often insufficient for application monitoring.

• Custom Metrics Pricing: Sending custom metrics (e.g., “number of logged-in users”) to CloudWatch is extremely expensive ($0.30 per metric/month). With thousands of metrics from a Kubernetes cluster, costs explode.
• Vendor Lock-in: CloudWatch dashboards only work with AWS data. You can’t visualize data from an external database or another cloud provider without first importing it at a high cost.
• UX Limitation: The visualization capabilities are rudimentary compared to the flexibility of Grafana.

Scenario B: Grafana with Managed Kubernetes from ayedo In the ayedo app catalog, Grafana is the central hub for monitoring.

• Cost Efficiency: Since Grafana is mostly based on Prometheus (also in the ayedo stack), you essentially only pay for the storage space on disk for metrics. Collecting millions of “custom metrics” costs almost nothing extra.
• Data Freedom: Grafana is yours. You can export dashboards, swap datasources, and install plugins as you wish.
• Unified Alerting: Grafana offers a central alerting engine. You can define alerts based on complex logic (e.g., “if errors > 5% AND revenue < average”) and send them to Slack, PagerDuty, or Teams.

Technical Comparison of Operational Models

FAQ: Grafana & Observability Strategy

Does Grafana replace my CloudWatch? Grafana replaces the CloudWatch UI, but not necessarily the data. You can integrate CloudWatch as a datasource in Grafana. This is often the first step: Use Grafana to better display AWS data. The second step is usually to store application metrics directly in Prometheus to avoid CloudWatch costs.

Grafana vs. Kibana (ELK Stack): Which is better? Previously, it was: Grafana for metrics, Kibana for logs. Today, the lines are blurring. Since Grafana introduced Loki (log aggregation), many teams are switching entirely to Grafana to avoid maintaining two tools. Grafana is often more performant and easier for developers to use, while Kibana still has advantages in complex log analysis (security forensics).

How secure is Grafana? Very secure. In the ayedo stack, Grafana is placed behind an OIDC provider (e.g., Keycloak, Google Auth, or Azure AD). This means you don’t have to maintain local users. An employee who leaves the company and is deactivated in Active Directory immediately loses access to Grafana. Additionally, RBAC (Role Based Access Control) allows developers to see only their own dashboards, but not those of the finance department.

Do I need Prometheus for Grafana? Not necessarily, but it is the “gold standard” for Kubernetes. Grafana is just the frontend. It needs a backend. Prometheus (for metrics) and Loki (for logs) are the perfect partners as they are extremely efficient. However, Grafana can just as easily visualize data directly from MySQL, InfluxDB, or Elasticsearch.

Conclusion

Observability is more than just colorful graphs. It is the ability to understand complex systems. AWS CloudWatch offers a keyhole view into AWS infrastructure. Grafana, on the other hand, opens the gate wide. It enables a democratized data culture where developers, ops, and business teams look at the same truth—cost-efficiently, cross-platform, and without vendor lock-in. With the ayedo Managed Stack, you get this “Single Pane of Glass” fully integrated, so you can solve problems instead of configuring tools.