AWS Machine Learning Blog

Transitioning off Amazon Lookout for Metrics 

Amazon Lookout for Metrics is a fully managed service that uses machine learning (ML) to detect anomalies in virtually any time-series business or operational metrics—such as revenue performance, purchase transactions, and customer acquisition and retention rates—with no ML experience required. The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch, Amazon CloudWatch, AWS Glue Data Quality, Amazon Redshift ML, and Amazon QuickSight.

After careful consideration, we have made the decision to end support for Amazon Lookout for Metrics, effective October 10, 2025. In addition, as of today, new customer sign-ups are no longer available. Existing customers will be able to use the service as usual until October 10, 2025, when we will end support for Amazon Lookout for Metrics.

In this post, we provide an overview of the alternate AWS services that offer anomaly detection capabilities for customers to consider transitioning their workloads to.

AWS services with anomaly detection capabilities

We recommend customers use Amazon OpenSearch, Amazon CloudWatch, Amazon Redshift ML, Amazon QuickSight, or AWS Glue Data Quality services for their anomaly detection use cases as an alternative to Amazon Lookout for Metrics. These AWS services offer generally available, ML-powered anomaly detection capabilities that can be used out of the box without requiring any ML expertise. Following is a brief overview of each service.

Using Amazon OpenSearch for anomaly detection

Amazon OpenSearch Service features a highly performant, integrated anomaly detection engine that enables the real-time identification of anomalies in streaming data as well as in historical data. You can pair anomaly detection with built-in alerting in OpenSearch to send notifications when there is an anomaly. To start using OpenSearch for anomaly detection you first must index your data into OpenSearch, from there you can enable anomaly detection in OpenSearch Dashboards. To learn more, see the documentation.

Using Amazon CloudWatch for anomaly detection

Amazon CloudWatch supports creating anomaly detectors on specific Amazon CloudWatch Log Groups by applying statistical and ML algorithms to CloudWatch metrics. Anomaly detection alarms can be created based on a metric’s expected value. These types of alarms don’t have a static threshold for determining alarm state. Instead, they compare the metric’s value to the expected value based on the anomaly detection model. To start using CloudWatch anomaly detection, you first must ingest data into CloudWatch and then enable anomaly detection on the log group.

Using Amazon Redshift ML for anomaly detection

Amazon Redshift ML makes it easy to create, train, and apply machine learning models using familiar SQL commands in Amazon Redshift data warehouses. Anomaly detection can be done on your analytics data through Redshift ML by using the included XGBoost model type, local models, or remote models with Amazon SageMaker. With Redshift ML, you don’t have to be a machine learning expert and you pay only for the training cost of the SageMaker models. There are no additional costs to using Redshift ML for anomaly detection. To learn more, see the documentation.

Using Amazon QuickSight for anomaly detection

Amazon QuickSight is a fast, cloud-powered, business intelligence service that delivers insights to everyone in the organization. As a fully managed service, QuickSight lets customers create and publish interactive dashboards that include ML insights. QuickSight supports a highly performant, integrated anomaly detection engine that uses proven Amazon technology to continuously run ML-powered anomaly detection across millions of metrics to discover hidden trends and outliers in customers’ data. This tool allows customers to get deep insights that are often buried in the aggregates and not scalable with manual analysis. With ML-powered anomaly detection, customers can find outliers in their data without the need for manual analysis, custom development, or ML domain expertise. To learn more, see the documentation.

Using Amazon Glue Data Quality for anomaly detection

Data engineers and analysts can use AWS Glue Data Quality to measure and monitor their data. AWS Glue Data Quality uses a rule-based approach that works well for known data patterns and offers ML-based recommendations to help you get started. You can review the recommendations and augment rules from over 25 included data quality rules. To capture unanticipated, less obvious data patterns, you can enable anomaly detection. To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL. AWS Glue Data Quality collects statistics for columns specified in rules and analyzers, applies ML algorithms to detect anomalies, and generates visual observations explaining the detected issues. Customers can use recommended rules to capture the anomalous patterns and provide feedback to tune the ML model for more accurate detection. To learn more, see the blog post, watch the introductory video, or see the documentation.

Using Amazon SageMaker Canvas for anomaly detection (a beta feature)

The Amazon SageMaker Canvas team plans to provide support for anomaly detection use cases in Amazon SageMaker Canvas. We’ve created an AWS CloudFormation template-based solution to give customers early access to the underlying anomaly detection feature. Customers can use the CloudFormation template to bring up an application stack that receives time-series data from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) streaming source and performs near-real-time anomaly detection in the streaming data. To learn more about the beta offering, see Anomaly detection in streaming time series data with online learning using Amazon Managed Service for Apache Flink.

Frequently asked questions

  1. What is the cutoff point for current customers?

We created an allow list of account IDs that have used Amazon Lookout for Metrics in the last 30 days and have active Amazon Lookout for Metrics resources, including detectors, within the service. If you are an existing customer and are having difficulties using the service, please reach out to us via AWS Customer Support for help.

  1. How will access change before the sunset date?

Current customers can do all the things they could previously. The only change is that non-current customers cannot create any new resources in Amazon Lookout for Metrics.

  1. What happens to my Amazon Lookout for Metrics resources after the sunset date?

After October 10, 2025, all references to AWS Lookout for Metrics models and resources will be deleted from Amazon Lookout for Metrics. You will not be able to discover or access Amazon Lookout for Metrics from your AWS Management Console and applications that call the Amazon Lookout for Metrics API will no longer work.

  1. Will I be billed for Amazon Lookout for Metrics resources remaining in my account after October 10, 2025?

Resources created by Amazon Lookout for Metrics internally will be deleted after October 10, 2025. Customers will be responsible for deleting the input data sources created by them, such as Amazon Simple Storage Service (Amazon S3) buckets, Amazon Redshift clusters, and so on.

  1. How do I delete my Amazon Lookout for Metrics resources?
  1. How can I export anomalies data before deleting the resources?

Anomalies data for each measure can be downloaded for a detector by using the Amazon Lookout for Metrics APIs for a particular detector. Exporting Anomalies explains how to connect to a detector, query for anomalies, and download them into a format for later use.

Conclusion

In this blog post, we have outlined methods to create anomaly detectors using alternates such as Amazon OpenSearch, Amazon CloudWatch, and a CloudFormation template-based solution.

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About the Author

Nirmal Kumar is Sr. Product Manager for the Amazon SageMaker service. Committed to broadening access to AI/ML, he steers the development of no-code and low-code ML solutions. Outside work, he enjoys travelling and reading non-fiction.