Datadog Releases Anomaly Detection To Facilitate Identification Of Abnormal Behavior In Cloud-Based Applications

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.



Datadog Announces Release Of Application Performance Management Solution Tailored For Cloud, Microservices and Container-based Apps

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.

Cloudyn Raises $11M In Series B Funding For Hybrid Cloud Monitoring & Performance Optimization

Cloud monitoring and optimization vendor Cloudyn today announced the finalization of $11M in Series B funding. Carmel Ventures led the capital raise in conjunction with previous investors Titanium Investments and RDSeed. The funding will be used to expand Cloudyn’s marketing and sales operations toward the end of improving market share and brand recognition and value. Cloudyn has increased revenue by a factor of three for each of the past three consecutive years and currently monitors over 200,000 virtual machines with its cloud monitoring software. Unlike other infrastructure monitoring tools, Cloudyn specializes in hybrid cloud monitoring and optimization and differentiates by way of its ability to support the monitoring of on-premise, private clouds in addition to deployments on public clouds. Cloudyn’s funding raise builds on its October announcement regarding its ability to “SmartSize” deployments that assesses the optimal scale of a deployment by taking into account variables such as instances, workloads, applications and its expected growth trajectory. The combination of Cloudyn’s SmartSizing-based cloud deployment functionality and its ability to monitor private and public cloud deployments from a unified pane of glass enables it to provide customers with prescriptive analytics about the optimal mix of private and public cloud infrastructures for their deployments.  With an extra $11M in the bank, expect Cloudyn to continue increasing market share for cloud monitoring and optimization as the proliferation of hybrid cloud deployments in the enterprise leads to an intensified need for hybrid cloud monitoring platforms.

Cloudyn Announces Support For Microsoft Azure For Cloud Performance And Cost Analytics

Cloud monitoring leader Cloudyn today announced its support for the Microsoft Azure cloud. Cloudyn’s support of Azure means complements its existing support for Amazon Web Services, Google Cloud Platform and OpenStack, thereby rendering it compatible with an even wider range of hybrid cloud infrastructures. Cloudyn specializes in performance management across a variety of cloud infrastructures by means of a unified user interface. In addition to performance management, Cloudyn also delivers cost tracking and optimization services that allow customers to control costs by optimizing resource and infrastructure allocations across different infrastructures and applications. The following screenshot of Cloudyn’s management dashboard illustrates the cost-tracking functionality of the Cloudyn platform:

Cloudyn’s data visualization functionality provides customers with nuanced drill-downs of cost by entity, cost savings opportunities and trend analytics of cost over time. By supporting Microsoft Azure, Cloudyn provides customers with the ability to understand how an Azure deployment alters the cost and performance equation in a hybrid cloud infrastructure that might alternatively include an on-premise OpenStack deployment as well as use of Amazon Web Services. Cloudyn CEO Sharon Wagner commented on the significance of the company’s support for Azure as follows:

We’re thrilled to add support of Microsoft Azure to our set of cloud platforms. Our customers’ interests in Azure adoption have grown rapidly. From the onset, we’ve been committed to helping our customers ease into the multi-cloud model as well as successfully manage it. Now we’ll be able to further help them cut down on cloud spend while avoiding cloud sprawl.

In a phone interview with Cloud Computing Today, Wagner noted that all performance metrics collected from the cloud infrastructures that Cloudyn supports reside on the same table, giving Cloudyn the unique capability to perform cross-cloud performance and cost comparisons. Cloudyn’s ability to deliver rich and nuanced analytics for hybrid cloud deployments renders it a powerful tool for contemporary cloud deployments, the vast majority of which necessarily embrace some combination of private and public cloud environment. Today’s announcement of its support for Azure represents yet another important milestone for Cloudyn, particularly as it seeks to add even more cloud platforms to the list of infrastructures it supports for performance and cost analytics.

AppFirst Delivers Sub-Millisecond Data Capture For Application And Infrastructure Monitoring

AppFirst recently revealed an enterprise-grade version of its monitoring platform for applications and infrastructures. The AppFirst platform takes its place in the realm of “appops” applications that provide analytics and performance optimization services not only with respect to servers and virtual machines, but also regarding application performance and its intersection with infrastructure. The AppFirst platform creates a time series that enhances the insight had by application owners and IT administrators regarding application and infrastructure performance. Based on the theory that you cannot control what you cannot see, AppFirst aims to expand the capture of events such as “every application call, system event, log file entry, configuration change, third party application or custom code event, and data from thousands of plug-ins.” The AppFirst platform delivers a sub-millisecond timeline of events that it subsequently transforms into actionable business intelligence as illustrated below:

The graphic above illustrates topologies and interdependencies within an environment that morph as the infrastructure and constituent applications themselves change in relation to incoming data feeds and user behavior. Importantly, AppFirst introduces little to no latency to existing applications and can operate within on premise, cloud and hybrid infrastructures via an unobtrusive installation. In conjunction with the news of this week’s release, AppFirst also revealed a partnership with Accretive that enables it to deliver predictive analytics regarding the Big Data collected by its platform with respect to application and infrastructure performance. The partnership with Accretive empowers AppFirst customers to identify trends related to analytics such as cost, performance, types of users and resource consumption. The key point worth noting about AppFirst’s technology platform, however, is the granularity of the data collected by its proprietary data aggregation technology that, in conjunction with its data visualization functionality, and predictive analytics, deliver a degree of insight into application and infrastructure performance not provided by vendors who limit their scope to machine data analytics. Moreover, AppFirst’s ability to manage on premise, public and private cloud environments renders it a key player within the space of platforms dedicated to monitoring infrastructure and application performance in hybrid cloud environments.

Kaseya Launches SaaS Monitoring Tool For Hybrid Clouds And Distributed Computing Environments

Today, Kaseya announces the general availability of Kaseya Traverse, its SaaS cloud monitoring solution for on premise, private cloud and public cloud environments. The uniqueness of Kaseya Traverse consists of its ability to “traverse” a multitude of cloud infrastructures while delivering centralized, integrated reporting for the entire ecosystem in question. Whereas proprietary cloud monitoring solutions such as CloudWatch by Amazon Web Services deliver performance reporting and monitoring solutions specific to their own, native cloud infrastructure, Kaseya Traverse can be configured to monitor a heterogeneous cloud environment marked by the coexistence of several hosting technologies and platforms. The Kaseya solution provides a diverse range of performance monitoring and analytics on hardware, networks, applications and usage patterns as illustrated by the dashboard below:

Kaseya leverages an architecture designed for distributed analytics, data processing and data gathering that fittingly corresponds to the task of monitoring the infrastructures of dispersed, heterogeneous IT environments. The platform features SLA monitoring, issue identification and resolution with respect to application performance and machine learning-based analytics that identify true anomalies in traffic or usage related patterns as opposed to organic variations and cycles. Given that the current state of enterprise cloud computing almost invariably features some combination of on premise, private cloud and public cloud deployments, Kaseya Traverse is likely to be well received by customers that are seeking a centralized monitoring, reporting and analytics solution in contrast to an amalgamation of discrete reporting applications. Moreover, its ease of deployment as a SaaS application and distributed computing capabilities render it a particularly attractive cloud monitoring tool insofar as its architecture is designed with the specific needs of heterogeneous cloud computing environments in mind.