[zhangwins]

Orbiter anomaly detection is for any DB (e.g. Postgres, Snowflake) and metrics that business/product teams tend to track such as transaction conversion %, user growth, add item to basket %, etc.

Amazon Cloudwatch anomaly detection is for AWS resources & apps, and covers infra metrics like resource utilization, app performance, ops health.

In terms of the anomaly detection capabilities -- both are using similar machine learning processes to detect metric issues automatically!

P.S. If you get curious about the details of our solution, we have a 2 minute video demo ;) Cheers! https://www.youtube.com/watch?v=R7P_M6j0P2A

[knightelvis]

Thanks for replying. That's a good demo. However, I don't necessarily agree Cloudwatch is only for infra metrics. Theoretically, you could send any metrics to CW and leverage the anomaly detection feature. Given it aggregates data over time and you could lost granularities of your data, that's probably not a good idea for business centric data. Then I found AWS QuickSight (https://aws.amazon.com/quicksight/features-ml/?nc=sn&loc=2&d...) which seems to have a similar feature parity?