Google Kubernetes Engine (GKE) Monitoring: Expert Guide

Since its launch in 2015, Google Kubernetes Engine (GKE) has become the go-to container orchestration platform for developers worldwide. Its ease of use, tight monitoring and integration with other Google Cloud Platform (GCP) services have made it a popular choice for organizations of all sizes.

Despite its popularity, GKE can be a resource-intensive platform, which can lead to unpredictable costs. That’s why it’s essential to have a Kubernetes monitoring solution in place to track your expenses and help you save money.

In this article, we’ll explain why GKE cost monitoring is so important and show you three metrics you need to track to keep costs under control. But first, let’s properly define Google Kubernetes Engine.

What is GKE?

Google Kubernetes Engine is a managed container orchestration service that runs on the GCP. It lets you create and manage containerized applications using Google’s infrastructure. GKE is based on the open-source Kubernetes project originally developed by Google. It takes care of all the heavy lifting for you, so you can focus on developing your application.

GKE is a popular choice for organizations that want to containerize their applications but don’t want to manage the underlying infrastructure. It’s also a good choice for companies that are already using other GCP services, as GKE integrates well with other GCP products.

Some benefits of GKE include:

• Security: GKE handles all the security patches for you and offers additional features like GCP’s Identity and Access Management (IAM) for granular control over who can access what.
• Scalability: GKE automatically scales your clusters up or down, so you only pay for what you use.
• Integration: GKE integrates with other GCP services like Stackdriver for logging and monitoring, Cloud Storage for storing application data, and BigQuery for analyzing that data.
• Portability: GKE applications can be easily migrated to other GCP products or on-premises solutions.
• Extensibility: GKE can be extended with add-ons like Istio for service mesh, Knative for serverless, and Cloud Run for GCP-managed containers.

If you’re not using GKE, you’re missing out on the benefits of Kubernetes and GCP’s managed environment. GKE gives you a simple way to deploy and manage production-ready Kubernetes clusters.

Why GKE monitoring is so important

GKE monitoring is the process of tracking your GKE costs and usage to optimize your GCP spending. It is essential for three main reasons:

1. Its clusters can scale up or down automatically, leading to unexpected costs.
2. It integrates with other GCP services, meaning that you might end up using more GCP resources than you realize.
3. It offers a wide range of features, which can be challenging to track without a monitoring solution.

Without GKE cost monitoring, it is very easy to overspend on your GCP bill. In fact, most organizations that don’t use a monitoring solution end up overspending by 60-70%. 

By understanding how your GKE resources are used, you can make informed decisions about where to allocate your budget. GKE monitoring can also help you optimize your cluster for cost and performance.

Metrics to monitor

There are three GKE cost metrics you need to track to keep your costs under control.

1. Cluster Performance

GKE clusters can be scaled up or down automatically based on the needs of your application. This means that you might be using more resources than you actually need, which can lead to higher costs. 

To avoid this, it’s essential to monitor the performance of your GKE cluster. You can do this by tracking the following metrics:

• CPU utilization: This metric tells you how much CPU your cluster uses. If you find that your CPU utilization is low, it may be worth scaling down your cluster.
• Memory usage: This metric tells you how much memory your cluster uses. If you find that your memory usage is low, it may be worth scaling down your cluster.
• Pod count: This metric tells you how many pods are running in your GKE cluster. If you find that your pod count is high, it may be worth scaling it up.

2. Pod Performance

GKE pods can also be scaled up or down automatically based on the needs of your application. This means that you might be using more resources than needed, leading to higher expenses. 

To avoid this, monitor the performance of your GKE pods. You can do this by tracking the following metrics:

• CPU utilization: This metric tells you how much CPU a pod uses. If you find that your CPU utilization is low, it may be worth scaling your pods down.
• Memory usage: This metric tells you how much memory each pod usesd. If you find that your memory usage is low, consider scaling down your GKE pods.

3. GCP Resource Usage

GKE integrates with other GCP services, which means that you might use more cloud resources than you realize. To get a complete picture of your resource usage, you need to track it together with cost data coming from your monthly cloud bill.

Connect your GKE cluster to a free monitoring solution

CAST AI or Container Analysis and Scheduling Tool for Applications Infrastructure is the best GKE monitoring solution on the market. It provides you with the ability to:

• View your GKE costs in one place: CAST AI gives you a consolidated view of your costs, so you can see where your money is going.
• Monitor your expenses in real-time: Track your GKE costs in real-time to catch problems early and avoid overspending.
• Understand where your GKE costs come from: CAST AI provides you with detailed insights into your GKE costs and supports cost allocation efforts for greater accountability and transparency.
• Build a FinOps culture at your company – easily share cost data via Prometheus to standard industry tools like Grafana to make cost monitoring easier for engineers.

CAST AI is simple to set up and requires no credit card or billing information. Just connect your GKE cluster, and you’re ready to go. And if you use its optimization features, you can save up to 60-70% on your cloud spend. Try CAST AI today and see your costs in 5 minutes.