Datadog enables you to watch, monitor, analyze, and report on the health of your infrastructure and services in any cloud at scale. However, one common concern about Datadog is that it quickly becomes expensive.
There’s more to Datadog cost optimization than reducing your log times or ingesting fewer logs. The good news is that optimizing your Kubernetes infrastructure can score significant cost savings on your Datadog bill.
Understanding Datadog’s pricing model
When budgeting for infrastructure and services, it’s crucial to understand how monitoring tools like Datadog charge for their services.
Datadog’s subscription-based pricing model typically revolves around two main strategies: host- and container-based pricing. Let’s take a closer look at each to explore cost optimization opportunities.
Host-based pricing
With host-based pricing, Datadog charges you based on the number of hosts running on the Datadog Agent. A host can be any physical or virtual machine.
This model is pretty straightforward for traditional, non-containerized environments. However, it can become more complex when dealing with containerized environments like Kubernetes, where multiple containers may run on a single host. This means that from the start, you need to pick the right nodes and be opinionated about segregation (which is difficult to accomplish without a solution like dynamic node pools).
Container-based pricing
To accommodate modern containerized applications, Datadog offers a container-based pricing model. It covers comprehensive metrics, events, and state data related to your containers, including deployments, pods, and nodes.
This approach charges based on the number of containers you monitor, making it particularly suitable for Kubernetes environments. This pricing strategy may open the door to Datadog cost optimization.
Rightsize Kubernetes clusters to reduce your Datadog costs
Rightsizing your clusters and reducing the number of CPUs are typical tasks that are part of Kubernetes cost management. However, the cost savings you can achieve by cutting CPUs translate into your Datadog expenses as well.
Rightsizing – changing CPU/RAM requests – is all about tailoring the resources allocated to your workloads to match their actual usage.
Then there’s autoscaling. Since DataDog’s pricing is based on the number of hosts or containers being monitored, reducing these numbers through rightsizing leads to lower monitoring expenses. You can achieve substantial cost savings by carefully adjusting the number of hosts or containers in your cluster and ensuring that it’s not overprovisioned.
Optimizing your Kubernetes environment improves performance and helps you save money on Datadog. By focusing on efficient resource allocation, you can keep both your infrastructure and monitoring costs under control.
At this point, you might be thinking that…
Rightsizing clusters manually is time-consuming
It’s true that micromanaging your cluster resources takes a lot of time. But you can delegate tasks like selecting compute resources and rightsizing your clusters to an automation solution.
We created CAST AI to help Kubernetes teams manage their clusters without worrying about running this part of their infrastructure, helping them to provision just enough resources for optimal performance and price.
The CAST AI automation platform chooses, provisions, and decommissions instances based on dynamic workload demands. A specialized autoscaler adjusts capacity in real time to accommodate demand fluctuations with zero downtime. On top of that, CAST AI constantly compacts applications into fewer resources and deletes empty nodes.
Book a personalized demo to see how CAST AI can help reduce your Datadog costs.
