Datadog Adds “Watchdog” Autonomous Monitoring | Datadog
Datadog Adds “Watchdog” Autonomous Monitoring

Datadog Adds “Watchdog” Autonomous Monitoring

  • Watchdog, a new machine learning based monitoring capability in Datadog, automatically identifies potential application and infrastructure performance issues

<ul> <li>Watchdog, a new machine learning based monitoring capability in Datadog, automatically identifies potential application and infrastructure performance issues</li> </ul>

July 12, 2018

2:00 PM UTC

Published by Business Wire


Martin Bergman

NEW YORK–(BUSINESS WIRE)–Datadog, the leading monitoring and analytics platform for modern cloud environments, today announced the availability of Watchdog, a machine learning based monitoring capability that automatically identifies hidden issues and anomalies in dynamic, cloud-based applications.

In traditional monitoring, engineers explicitly define the expected behavior of their application and set up dashboards and alerts to monitor for deviations from this behavior. However, due to the scale, elasticity, and complexity of modern cloud applications, issues often occur in unexpected places that engineers may not have thought to monitor explicitly.

Watchdog takes a radically different approach: it observes all performance data automatically and surfaces anomalous behavior that would have otherwise remained invisible to the application’s engineers. These observations help engineering organizations resolve existing performance problems, or head off emerging issues before they are noted by end users.

“As the complexity of applications explodes, the need for automated issue detection becomes a necessity for teams to build highly performant and reliable applications in the cloud,” said Homin Lee, Head of Data Science at Datadog. “Watchdog builds upon our years of research and training of algorithms on our customers’ data sets. This technology is unique in that it not only identifies an issue programmatically, but also points users to probable root causes to kick off an investigation.”

“We run complex applications which need to respond rapidly to frequent behavioral changes,” said Caedman Oakley, Director of Resilience Engineering at Castlight Health. “Having Watchdog automatically examine all our metrics will enable us to spot anomalies and take action before those anomalies become issues.”

“As application environments become more complex and the volume of operations data that teams collect grows, engineers require new technologies and tools to glean insight from that data,” said Nancy Gohring, Senior Analyst at 451 Research. “Advanced analytics systems that separate the important data from the noise can help DevOps teams get the most out of operations data so that they can quickly repair or prevent performance problems.”

General Availability

Watchdog was announced at Dash, Datadog’s conference for engineers who are building and scaling the next generation of applications, infrastructure, and technical teams. This capability is now generally available within Datadog for all customers on Datadog’s Enterprise APM plan.

Also Announced by Datadog at Dash

Datadog also announced Trace Search, a high cardinality application trace querying capability, and Logging without Limits™, new capabilities that completely change how logs are used by DevOps teams, at Dash in New York City today.

For more information, and to start a free 14-day trial, please visit:

Additional Resources:

Watchdog Blog Post:

About Datadog

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 by more than 8,000 enterprises including companies like Asana, AT&T, Evernote, Samsung, Seamless, and The Washington Post.