Improve application availability with ML-powered cloud operations

Benefits

Detect abnormal application behavioral

Detect abnormal application behavior using machine learning (ML) models informed by years of Amazon.com and AWS operational excellence.

Receive insights and contextual information

Receive insights and contextual information about anomalous behavior along with actionable remediation recommendations.

Automatically analyze application metrics

Automatically analyze application metrics, logs, and events to adapt to changing behavior and system architectures.

Use ML models to limit alarm noise

Use ML models to limit alarm noise so your team can focus on remediation and responses.

Use cases

Improve availability and performance of serverless applications

Identify early signs of operational issues for your serverless applications and remediate them before they impact your customers.

Reduce recovery time for Amazon RDS databases

Detect, assess, and remediate a wide variety of database-related issues in Amazon Relational Database Service (RDS).

Scale and maintain availability

Save time and effort with automatic updates to static rules and alarms so you can effectively monitor complex and evolving applications.

Proactively identify resource limits

Get alerts when exhaustible resources, such as memory, CPU, and disk space, will exceed the provisioned capacity.