Metaplane makes it very easy to be identified when there are issues with your data environment. It uses AI to learn what patterns are typical for your data so that it can notify you of discrepancies without you having to set thresholds for every test.

The best part of Metaplane is its ML algorithm for understanding the shape and trends of existing data. This feature saves tremendous time in setting up meaningful alerts and notifications. For example, instead of you trying to manually figure out what the right daily record count is supposed be, Metaplane's algorithm automatically learns what the right count should be. In my experience, this algorithm is right 99% of the time, and on the rare occasions where adjustments need to be made, corrective feedback can be easily given to the algorithm with a few clicks in the UI.

Metaplane allows us to define custom metrics such as CPU utilization, Cache size of dB and API endpoints response time, which are critical for our application overview. We can easily track dependencies between our infrastructure resources with its Traceability dashboard. Its logs are fine-grained in such a way that it's easy to comprehend our data observability demands during customer transactions.