On February 15, Datadog announced the general availability of Application Performance Monitoring (APM). Datadog’s APM platform complements its infrastructure monitoring capabilities and enables it to deliver a holistic set of monitoring solutions that absolves customers of the need to implement siloed application and infrastructure monitoring solutions. Amit Agarwal, Chief Product Officer at Datadog, remarked on the significance of the company’s application monitoring capabilities as follows:
Based on customer demand, we are blurring the distinction between infrastructure monitoring and application performance monitoring by offering both within Datadog. We want to enable enterprises to benefit from APM deployed broadly across all of their hybrid or private cloud infrastructure that is running code. Traditionally, companies with scaling infrastructure are only deploying APM on a small percent of their applications or machines in order to cut down on costs.
Here, Agrawal elaborates on how Datadog’s APM capabilities motivate customers to select APM solutions for a broader subset of their application portfolio in contrast to “a small percent of their applications or machines” in order to curb costs. Moreover, Datadog’s APM offering facilitates the deployment of application performance monitoring for applications deployed within both private and hybrid cloud environments. Conceived in response to customer requests, the Datadog APM solution gives customers not only the combination of application and infrastructure performance monitoring, but more importantly, insight into the intersection between infrastructure and applications and their ability to reciprocally influence one another.
Key features of the Datadog APM platform include the detection of anomalies via machine learning-based algorithms, flame graphs that identify the most frequently used code paths, customizable dashboards and the ability to track end user application requests across host machines and other related infrastructure and application components. Datadog APM empowers Datadog to disrupt the space of IT monitoring platforms by leveraging the company’s machine learning and artificial intelligence technologies to understand the reciprocity between the effect of applications on infrastructure, and conversely, the impact of infrastructure on applications. Importantly, the general availability of Datadog’s APM solution positions it strongly to go head to head with the likes of New Relic, Splunk and AppDynamics by taking advantage of its holistic analytics and advanced data visualization capabilities as illustrated by the dashboards below:
Expect Datadog to continue enhancing its APM solution and aggressively expanding market share in the application monitoring space now that its APM solution is generally available.
Datadog today announces the release of Anomaly Detection, a machine learning-based tool that empowers engineering teams to expeditiously identify abnormalities within dynamic cloud environments. Anomaly Detection draws upon statistical analytics to pinpoint notable deviations in application performance behavior from its normal operating state. Designed to identify abnormal behavior within cloud-based applications that indigenously experience significant variations with respect to the scale of their underlying infrastructure as well as the velocity and variety of incoming data, Anomaly Detection creates a constantly updated representation of the normal state of an application that can identify aberrant behavior while taking into account a cloud-based application’s organic growth and volatility. As such, the application helps engineers and application owners understand genuinely abnormal application behavior by considering factors such as business growth, seasonality and macroscopic trends in the space in which the application operates that may compel varying levels of application behavior and performance. A key advantage of Anomaly Detection is its ability to absolve engineers of the task of updating metrics and thresholds in response to the evolution of an application over time. Datadog’s Anomaly Detection platform dovetails with its existing Outlier Detection tool that delivers alerts when a server behaves differently from analogues within its cohort. The combination of Anomaly Detection and Outlier Detection means that Datadog continues to strengthen its position as a leader in the cloud monitoring space by virtue of its impressive technology for automating the detection of aberrant application behavior. The graphic below illustrates Anomaly Detection’s ability to identify an abnormal deviation from innocuous variations in application behavior as shown by the red section of the line, which deviates significantly from the normal, sinusoidal trajectory of the graph.
On September 21, Datadog announced the release of an Application Performance Management product to its core SaaS product for monitoring cloud infrastructures. Datadog’s Application Performance Management solution is designed to help developers understand and optimize application performance, with a specific focus on applications hosted in hybrid cloud, container or micro-services infrastructures. Datadog’s experience in infrastructure monitoring positions it strongly to enter an application performance management space featuring the likes of New Relic and AppDynamics. Moreover, the company’s expertise in monitoring cloud-based infrastructures renders it uniquely qualified to manage application performance on cloud platforms given the intersection between infrastructure and application performance as noted below by Datadog’s Chief Product Officer, Amit Agarwal:
Most APM tools on the market today are designed to troubleshoot coding issues in isolation. However, in modern cloud-scale applications, quickly resolving problems requires examining changes in both the infrastructure and code simultaneously.
Here, Agarwal remarks on the importance of examining application-related issues within the broader context of the infrastructure on which applications run. Application-related performance bottlenecks or other “coding issues,” Agarwal notes, should no longer be understood separately from their associated infrastructure components and instead require a framework that facilitates a holistic analysis of their root causes with attention to both the application and infrastructure layers. Given the dynamism specific to cloud computing infrastructures as evinced by instances, clusters and containers that are variously launched, spun down or assume different relationships with one another, Datadog’s specialization in cloud infrastructure monitoring and analytics positions it to take a leadership position in the application monitoring space, particularly given the proliferation of cloud-based applications and the concomitant inability of legacy application performance management frameworks to understand how the dynamic quality of cloud infrastructures variously impacts cloud application performance. Separate from its experience with cloud infrastructure monitoring, Datadog brings rich data visualization capabilities to the conversation about application performance management in addition to battle-tested analytics that take advantage of time-series analyses, correlative analytics and predictive analytics as illustrated below:
With $94.5M in Series D funding raised in January, and a whopping $148M in total funding raised to date, expect Datadog to richly build out its application performance management capabilities in conjunction with the progressive expansion of the adoption of its cloud monitoring platform. Meanwhile, Datadog’s release of its application performance management solution signals the intensification of a larger battle in the industry to build and refine a comprehensive infrastructure and application monitoring framework capable of managing the radical heterogeneity of infrastructures, applications, databases and orchestration frameworks specific to contemporary computing.
New York-based Datadog recently announced the finalization of $94.5M in Series D for its SaaS cloud monitoring products and services. Led by ICONIQ Capital, the funding will be used to accelerate product development as well as expand the company’s global operations. Since its launch in 2010, Datadog has garnered significant traction in the cloud monitoring space by notching up the likes of Netflix, Spotify, Airbnb and Twilio within its roster of customers. The Series D funding raise builds upon Datadog’s December announcement of its integration with OpenStack. Datadog’s integration with OpenStack allows customers to leverage its SaaS application to understand the health of OpenStack deployments, with a focus on monitoring the compute and networking components of an OpenStack deployment. Existing investors Index Ventures, OpenView Ventures, Amplify Partners and Contour Ventures also participated in the funding raise, alongside other investors. The Series D funding raise brings the total capital raised by Datadog to roughly $148M.
Datadog today announced the integration of its cloud monitoring platform with OpenStack. As a result of the technology integration, customers can use Datadog’s technology to understand the health of OpenStack-based cloud deployments. The integration between Datadog and OpenStack is especially significant because OpenStack “is quickly becoming a preferred for private cloud environments” as noted by Amit Agarwal, Chief Product Officer of Datadog, in a press release. OpenStack administrators stand to benefit from Datadog’s out of the box capability to create dashboards that variously aggregate, synthesize and visualize the results of its metrics. Furthermore, OpenStack users can take advantage of Datadog’s advanced analytics for outlier detection that identify anomalies in host machine performance toward the end of preventing service degradation. The integration focuses on the Nova and Neutron components of the OpenStack platform, namely, the compute and networking components of OpenStack. More specifically, Datadog’s integration with OpenStack compute enables customers to access real-time data about the workload within a specific OpenStack environment, the number of VMs and instances, metrics about hypervisor performance and additional KPIs related to infrastructure performance such as RAM and CPU usage. Meanwhile, Datadog’s integration with OpenStack’s network management components features data collection about network performance in addition to Keystone, OpenStack’s Identity and Access Management service platform. The integration between Datadog and OpenStack gives the OpenStack community access to a powerful monitoring and data aggregation platform while Datadog stands to benefit by adding yet another major cloud platform to its venerable roster of supported cloud environments. As such, today’s announcement represents yet another step forward with respect to the onward march of contemporary cloud monitoring platforms and their ability to synthesize data from a multitude of cloud environments with increasing sensitivity and sophistication.
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