In today’s cloud-native world, applications no longer run in a single cluster. Organizations adopt multi-cluster architectures for scalability, resilience, and geographic distribution. But with this power comes complexity and the need for robust Kubernetes observability.
Imagine managing clusters across multiple regions, each hosting critical microservices. Without a unified observability setup, troubleshooting becomes a needle-in-a-haystack challenge. This is where multi-cluster monitoring proves essential for maintaining performance and uptime.
“More clusters, more chaos. Here’s how to stay ahead.”
Monitoring a single cluster is hard, and scaling to multiple clusters is exponentially tougher. Each cluster generates logs, metrics, and traces, creating an overwhelming data flood. Without the right observability tools, you risk missing key insights and reacting too late.
Open-source solutions like Prometheus, Grafana, Thanos, and OpenTelemetry offer cost-effective, flexible observability for Kubernetes environments, helping detect anomalies and ensure reliability across multi-cluster setups.
“From costly to cost-effective. Our multi-cluster monitoring solution.”
After evaluating multiple Prometheus alternatives and observability stacks, we implemented a high-availability, open-source monitoring setup that could scale with our infrastructure while keeping costs in check. Here’s how we did it.
This large-scale setup demanded a robust kubernetes observability stack that could handle high data volume without compromising performance.
“The truth about paid monitoring. Are you paying for a black box?”
Paid observability platforms like Datadog and New Relic provide seamless multi-cluster visibility but come with high costs.
Given our cluster size of 120+ nodes, the cost of these platforms became prohibitive. Instead of paying a premium for a one-size-fits-all solution, we chose open-source Prometheus alternatives to build a tailored observability stack.
"Prometheus, Grafana & Loki: The ultimate observability trio ?”
Prometheus + Grafana + Loki form a powerful open-source solution, but they come with their own challenges:
Prometheus, designed as a monolith with local storage, is not ideal for scalable setups. However, integrations like Thanos or Cortex can enable scalability by:
While Thanos or Cortex enable sharding and unified querying, they also add complexity. As your infrastructure scales, resource consumption increases, leading to higher operational overhead and potential performance bottlenecks. Additionally, managing long-term storage and ensuring high availability requires extra configurations, making maintenance more demanding.
So, what’s the alternative?
“Our winning stack: VictoriaMetrics + Grafana + Loki !!”
VictoriaMetrics comes with built-in clustering and is fully backward compatible with all Prometheus endpoints. It emerged as the top performer in our resource and performance benchmark, which was crucial for scaling.
VictoriaMetrics overview
VictoriaMetrics, similar to Prometheus, offers built-in clustering capabilities, making it an ideal choice for our monitoring needs. It comprises:
Loki overview
Loki provides three deployment strategies, and for our setup, we opted for the Simple Deployment strategy to efficiently handle clustering. For storage, we use Google Cloud Storage (GCS) to ensure scalability and durability.`
Loki deployment consists of three main components:
Logging agent: Fluent Bit
For log collection, we chose Fluent Bit as the logging agent. Each log line is enriched with custom labels (Cluster: cluster 1)before being pushed to the master cluster, ensuring efficient filtering and retrieval.
This setup provides a streamlined, scalable logging solution while leveraging Loki’s simplicity and GCS’s reliability.
“Building a high-availability monitoring stack—here’s how we did it”
Our production monitoring setup consists of three Kubernetes clusters:
“VictoriaMetrics vs. Prometheus + Thanos: The ultimate performance face-off”
VictoriaMetrics demonstrated:
1.5M TS//15S
100K SAMPLES/S
| Thanos | VictoriaMetrics | |
|---|---|---|
| CPU | 4.01 cores | 0.86 cores |
| Memory | 21 GiB | 8.93 GiB |
| Bytes/sample* | 4.72 B | 0.91 B |
Looking to build a high-performance, open-source observability stack that scales across clusters?
Partner with KeyValue to design, implement, and optimize your Kubernetes monitoring using tools like VictoriaMetrics, Grafana, and Loki.
👉 Let’s talk about building visibility that grows with your infrastructure.
“Monitoring at scale? Open-source is the way forward."
By leveraging VictoriaMetrics, Grafana, and Loki, we successfully implemented a high-performing, scalable monitoring solution tailored to our needs. This setup not only ensures optimal performance but also aligns with cost and scalability requirements, making it a sustainable choice for growing infrastructure demands.
In a world where multi-cluster architectures are becoming the norm, open-source tools like these empower organizations to achieve enterprise-grade observability without the enterprise-grade price tag. So, whether you’re a startup or a large enterprise, it’s time to embrace open-source monitoring and take control of your multi-cluster environment.
Let’s turn complexity into clarity—one cluster at a time.
For anyone looking to dive deeper into the specifics of implementing this setup, a detailed VictoriaMetrics tutorial can guide you through the setup process and best practices for scaling your monitoring system across multiple clusters. Remember that mastering monitoring best practices is key to ensuring the smooth operation of your infrastructure as you grow.
Sample Config for multi-cluster monitoring setup: [multi-cluster-monitoring].
Kubernetes observability is the ability to monitor, analyze, and understand the performance of applications and infrastructure across clusters. It combines metrics, logs, and traces to provide real-time visibility into system health.
The four pillars of observability are metrics, logs, traces, and events. Together, they help detect issues, analyze root causes, and maintain reliability in distributed systems like Kubernetes.
Yes. Grafana is a leading open-source observability platform that visualizes metrics, logs, and traces from sources like Prometheus, Loki, and VictoriaMetrics, offering unified dashboards for performance monitoring.
Monitoring tracks known system metrics and alerts on failures, while observability helps engineers explore unknown issues by analyzing system data. Observability gives deeper insight into why problems occur.
The best tool depends on scale and use case, but open-source stacks like VictoriaMetrics, Loki, and Grafana provide powerful, cost-effective Kubernetes observability compared to paid platforms.
