NEW YORK--(BUSINESS WIRE)--Datadog, the essential monitoring service for modern cloud environments, today announced Forecasts, a new feature that predicts when performance and stability issues will occur within cloud applications. While traditional monitoring solutions alert IT operations and engineering teams after a problem has already impacted user-experience and revenue-generating operations, Forecasts leverages artificial intelligence to alert teams days, weeks, or months in advance.
In highly distributed and dynamic cloud environments, planning for future performance issues and avoiding downtime can be difficult and labor-intensive. By applying machine-learning algorithms to massive amounts of data, Datadog can generate predictive analytics on everything from application performance to custom business metrics. This will reduce uncertainty and increase efficiency for businesses, putting the focus on actionable insights instead of troubleshooting problems after customers have already been impacted.
“Today, DevOps teams often receive critical alerts after their customers have been negatively impacted,” said Brad Menezes, Datadog’s Product Manager for artificial intelligence and machine learning. “Our forecasting algorithms have been fine-tuned based on trillions of data points across hundreds of thousands of servers daily. We can predict where a metric will be in the future, taking into account historical patterns, and notify users with plenty of time to prevent any negative impact. This directly translates into dollars saved and better user experiences – something every organization needs.”
Datadog has a dedicated team of product, engineering, and data-science professionals working on integrating artificial intelligence into its monitoring platform. Last year, the company also introduced Anomaly Detection for identifying abnormal changes in cloud behavior in real-time. Forecasts is now available for all Datadog customers and is available for a free trial here: https://www.datadoghq.com/
“Datadog already collects a ton of data from everything from the virtual machines and containers to the applications running on top of the infrastructure,” said Calvin French-Owen, CTO and Co-Founder of Segment. “It’s a big step for the platform that they can now extract actionable insights based on machine-learning applied to that data.”
“Enterprises running modern, cloud-based applications are collecting a growing volume of operations data,” said Nancy Gohring, senior analyst at 451 Research. “Advanced analytics techniques like machine learning are crucial to enabling businesses to gain intelligence from that big data set. Monitoring tools that harness sophisticated machine learning algorithms to identify trends and pinpoint the cause of problems ultimately help business deliver more performant applications.”
“We rely on Datadog’s sophisticated alerting abilities to quickly detect and troubleshoot odd behavior within cloud-infrastructure,” said Kreece Fuchs, Vice President at Trek10. “Their new forecasting feature will allow us to more accurately predict when events will negatively impact our customers.”
Datadog is a monitoring service for hybrid cloud applications, assisting organizations in improving agility, increasing efficiency, and providing end-to-end visibility across the application and organization. These capabilities are provided on a SaaS-based data analytics platform that enables Dev, Ops and other teams to accelerate go-to-market efforts, ensure application uptime, and successfully complete digital transformation initiatives. Since launching in 2010, Datadog has been adopted more than 5000 enterprises including companies like Asana, eBay, PagerDuty, Stripe, Samsung, Target, The Washington Post, and Zendesk.