Monitoring and security platform for cloud applications, Datadog, has unveiled its latest feature, Datadog Kubernetes Autoscaling. This innovative set of capabilities would promise to intelligently automate resource optimization, allowing customers to scale their Kubernetes environments dynamically based on real-time and historical utilization metrics.
With this introduction, Datadog claims to becomesthe first observability vendor to enable direct adjustments to Kubernetes environments from within its platform.
When deploying applications on Kubernetes, many teams would resort to overprovisioning resources to preempt infrastructure capacity issues that could affect end users. However, this practice may often result in considerable wastage of computing power and inflated cloud costs. Datadog‘s recent report, ‘State of Cloud Costs 2024,’ highlights that a staggering 83% of container costs are linked to idle resources. This might underscore the necessity for a solution that can monitor resource usage, optimize infrastructure performance, and manage computing costs effectively while ensuring applications maintain adequate resources for scaling.
Kubernetes Resource Rightsizing Done Automatically
Datadog Kubernetes Autoscaling offers continuous monitoring and automatic rightsizing of Kubernetes resources. This would not only lead to substantial cost savings for cloud infrastructure but also ensure optimal application performance, enhance user experiences, and deliver better returns on container investments. Customers can pinpoint workloads and clusters with high idle resources and choose either a one-time corrective action through intelligent automation or allow Datadog to handle ongoing workload scaling automatically. These capabilities would empower operators to strike the right balance between cost efficiency and user experience, tailored to their risk profiles.
“Containers represent a significant area of wasted expenditure due to the costs associated with idle resources. However, organizations cannot afford to compromise on performance or resource availability for scaling,” said Yrieix Garnier, VP of Product at Datadog. “The challenge for businesses is finding the right balance between control and automation, enabling them to automate actions when they are ready. Datadog Kubernetes Autoscaling achieves this balance by linking automated Kubernetes rightsizing with real-time cost and performance data. As a result, Datadog is the only enterprise-grade, unified platform that offers comprehensive observability, security, and resource management at scale for any Kubernetes-driven organization.”
Optimizing Cloud Expenditures
Datadog Kubernetes Autoscaling equips organizations with tools to balance flexibility, control, and automation. This feature set would allow companies to:
- Control Cloud Costs – By optimizing cloud expenditures and enhancing application performance through automated resource scaling for Kubernetes workloads, Datadog would help maintain cost-efficiency within the platform.
- Simplify, Democratize, and Automate Resource Optimization – The unified view and intuitive user interface display Kubernetes resource utilization and cost metrics, making it easy for team members across different roles to understand and manage resources effectively.
- Unify Monitoring and Resource Management – Datadog’s enterprise-grade platform would provide full visibility into how rightsizing affects workload and cluster performance. With high-resolution trailing container metrics, teams are equipped with rich contextual data to make informed decisions.
In summary, Datadog Kubernetes Autoscaling may represent a significant advancement in resource management for Kubernetes environments. By providing automated, intelligent resource optimization, Datadog says it enables organizations to achieve cost savings, improve application performance, and enhance their overall operational efficiency.
