Datadog Announces Kubernetes Autoscaling to Help Teams Intelligently Optimize Resources and Cloud Costs

Rhea-AI Summary

Datadog (NASDAQ: DDOG) announced a new feature called Datadog Kubernetes Autoscaling, which automates resource optimization and scales Kubernetes environments based on real-time and historical data.

This new capability aims to reduce cloud costs by addressing the issue of idle resources, which account for 83% of container costs according to Datadog's State of Cloud Costs 2024 report.

By providing automated rightsizing of Kubernetes resources, it ensures optimal performance and ROI. The feature allows users to manually or automatically scale their workloads, balancing cost and performance issues effectively.

Datadog is the first observability platform to offer direct Kubernetes environment changes, providing a unified view of resource utilization and cost metrics to simplify operation for teams.

Positive

• Datadog introduces automated Kubernetes Autoscaling, enabling intelligent resource optimization.
• Reduces cloud costs by addressing idle resources, which make up 83% of container costs.
• Empowers users to balance cost and performance effectively through automated and manual scaling options.
• Datadog is the first platform to allow direct Kubernetes environment changes, providing full visibility into resource management.

Negative

• None.

Insights

Tech Expert

Datadog's new feature, Kubernetes Autoscaling, stands out for its potential to revolutionize cloud resource management. Kubernetes is a popular open-source platform used to manage containerized applications at scale, but it notoriously suffers from inefficiencies like overprovisioning of resources. Datadog's integration of autoscaling capabilities directly into its platform brings a smart, automated solution to a common problem, promising significant cost savings.

The feature's real-time and historical utilization metrics allow for precise adjustments, ensuring applications retain necessary resources without excess. This could mean reduced cloud costs and improved performance metrics that are critical for business operations. The ability to execute these changes directly from the Datadog platform simplifies the user experience and integrates seamlessly into existing workflows, enhancing operational efficiency.

Investors should watch how quickly businesses adopt these capabilities. If widely adopted, it may increase Datadog's market share and drive revenue growth, reflecting positively on stock performance.

Financial Analyst

The financial implications of Datadog's Kubernetes Autoscaling should not be underestimated. By addressing a pain point – the 83% of container costs associated with idle resources – the feature can deliver substantial cost savings. This is particularly relevant given the current emphasis on cost-efficiency in corporate IT budgets.

For stakeholders, the introduction of such a feature could mean better ROI for Datadog's clients, translating to higher customer satisfaction and retention rates. In the long-term, the efficiency gains and cost reductions could attract new customers, boosting Datadog's revenue streams. Additionally, being the first observability vendor to offer this capability provides a competitive edge, potentially leading to increased market share.

It's vital, however, to monitor the financial performance of this feature as it transitions from beta to full deployment. Success in this area could lead to strong earnings reports, positively impacting stock prices.

New feature provides increased control and automation for Kubernetes environments

NEW YORK, June 26, 2024 /PRNewswire/ -- Datadog, Inc. (NASDAQ: DDOG), the monitoring and security platform for cloud applications, today announced Datadog Kubernetes Autoscaling, a set of capabilities that intelligently automates resource optimization and can automatically scale customers' Kubernetes environments based on real-time and historical utilization metrics. With this announcement, Datadog is the first observability vendor to enable customers to make changes to their Kubernetes environments directly from the platform.

When deploying applications on Kubernetes, teams often choose to overprovision resources as a way to avoid infrastructure capacity issues from impacting end users. This can lead to a large amount of wasted compute and increased cloud costs. In fact, according to Datadog's recent report, State of Cloud Costs 2024, 83% of container costs were associated with idle resources. For this reason, it's critical organizations have a solution in place which can monitor resource usage and optimize infrastructure performance and computing costs while ensuring applications remain performant with enough resources to scale.

Datadog Kubernetes Autoscaling continuously monitors and automatically rightsizes Kubernetes resources. This leads to significant cost savings for an organization's cloud infrastructure and helps to ensure optimal application performance for workloads, improved user experiences and better ROI on container assets. Customers are able to identify workloads and clusters with a high number of idle resources, implement a one-time fix through intelligent automation or enable Datadog to automatically scale the workload on an ongoing basis. These new capabilities empower operators to decide the right balance on cost and user experience based on their risk profiles.

"Containers are a leading area of wasted spend because so many costs are associated with idle resources, but organizations also can't risk degrading performance or not having enough resources to scale. The key for businesses is to find a balance between control and automation where they can automate actions when they are ready," said Yrieix Garnier, VP of Product at Datadog. "Datadog Kubernetes Autoscaling provides this balance. By connecting automated Kubernetes rightsizing with real-time cost and performance data, Datadog is the only enterprise-grade, unified platform that provides end-to-end observability, security and resource management at scale for any Kubernetes-driven organization."

Datadog Kubernetes Autoscaling helps organizations balance flexibility, control and automation so that they can:

• Control Cloud Costs: Optimize cloud costs and improve application performance with automated resource scaling for Kubernetes workloads within the Datadog platform.
• Simplify, Democratize and Automate Resource Optimization: Teams can leverage a unified view and an intuitive UI that displays Kubernetes resource utilization and cost metrics, making it simple for any team member to understand and scale resources.
• Unify Monitoring and Resource Management: Datadog's unified, enterprise-grade platform gives organizations full visibility into how rightsizing impacts their workload and cluster performance, backed by high-resolution trailing container metrics, so teams can take action based on this rich context.

Datadog Kubernetes Autoscaling is now in beta. To learn more, please visit: https://www.datadoghq.com/blog/datadog-kubernetes-autoscaling.

About Datadog

Datadog is the observability and security platform for cloud applications. Our SaaS platform integrates and automates infrastructure monitoring, application performance monitoring, log management, user experience monitoring, cloud security and many other capabilities to provide unified, real-time observability and security for our customers' entire technology stack. Datadog is used by organizations of all sizes and across a wide range of industries to enable digital transformation and cloud migration, drive collaboration among development, operations, security and business teams, accelerate time to market for applications, reduce time to problem resolution, secure applications and infrastructure, understand user behavior and track key business metrics.

Forward-Looking Statements

This press release may include certain "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended including statements on the benefits of new products and features. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control, including those risks detailed under the caption "Risk Factors" and elsewhere in our Securities and Exchange Commission filings and reports, including the Quarterly Report on Form 10-Q filed with the Securities and Exchange Commission on November 7, 2023, as well as future filings and reports by us. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations or otherwise.

Contact
Dan Haggerty
press@datadoghq.com

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SOURCE Datadog, Inc.

FAQ

What is Datadog Kubernetes Autoscaling?

Datadog Kubernetes Autoscaling is a feature that automates resource optimization and scales Kubernetes environments based on real-time and historical utilization metrics.

How does Datadog Kubernetes Autoscaling help reduce cloud costs?

It continuously monitors and rightsizes Kubernetes resources, addressing idle resources which account for 83% of container costs, thereby reducing cloud expenses.

What key benefit does Datadog Kubernetes Autoscaling provide?

It ensures optimal application performance and ROI by automating the scaling of Kubernetes resources while balancing cost and performance.

When was Datadog Kubernetes Autoscaling announced?

Datadog Kubernetes Autoscaling was announced on June 26, 2024.

What makes Datadog Kubernetes Autoscaling unique?

Datadog is the first observability platform to enable direct changes to Kubernetes environments, providing a unified view of resource utilization and cost metrics.