Amazon Lookout for Metrics

Automatically detect anomalies within metrics and identify their root causes

Analyze 100 free metrics in the first 30 days

with the AWS Free Tier

Reduce false positives and use machine learning (ML) to accurately detect anomalies in business metrics.

Diagnose the root cause of anomalies by grouping related outliers together. Summarize root causes and rank them by severity.

Seamlessly integrate AWS databases, storage services, and third-party SaaS applications to monitor metrics and detect anomalies.

Automate customized alerts and actions when anomalies are detected.

How it works

Amazon Lookout for Metrics uses ML to detect and diagnose anomalies within business and operational data.

Diagram showing how Amazon Lookout for Metrics uses ML to continuously monitor your metrics and create an impact summary of anomalies.
What is Amazon Lookout for Metrics? (1:20)
Why Lookout for Metrics?
Detecting unexpected anomalies is challenging since traditional methods are manual and error prone. Lookout for Metrics uses ML to detect and diagnose errors within your data, with no artificial intelligence (AI) expertise required.

Use cases

Monitor business performance

Identify unusual variances in subscriptions, conversion rates, and revenue, so you can stay on top of sudden changes.

Improve customer experience

Detect metric spikes and dips to better understand customer-related issues, churn rates, and install or purchase rates.

Optimize your digital ad spend

Automatically understand when your campaign is overspending, underperforming, or encountering errors, without the need for manual intervention.

Boost user engagement

Optimize user engagement by understanding changes in new users, app installs, in-app purchases, and retention.

How to get started

Explore Lookout for Metrics

Learn how you can detect anomalies with greater accuracy.

Get started with a free account

Analyze up to 100 metrics for free for one month.

Start using Lookout for Metrics

Learn more about efficiently detecting and diagnosing metrics.


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