Setting up dashboards using a traditional monitoring system or a dashboard framework requires a lot of tedious work. For each SQL query or a JSON endpoint the work must be repeated to create proper metric names or a format accepted by a dashboard widget.
Monique.io replaces the work that must be done by a programmer with AI. Monitoring 50 SQL/JSON/text endpoints requires writing 50 lines of code in case of Monique.io (about one workday) and about 750 lines of code in case of a traditional system (about three workweeks).
If you set up a traditional monitoring system or an APM platform, some parts of your system will be monitored. However, this is just a tip of an iceberg — the most meaningful things happen in the application layer. APIs, microservices, databases, logs require custom monitoring.
Since it's so easy to push data from these sources into Monique.io, you will end up having a lot more meaningful information compared to using traditional tools. It's good to know what's really going on with your product.
Traditional monitoring systems have good support for alerting on CPU usage and other system metrics. But if you want to set up checks on SQL results or API responses, you will find it either hard or impossible — these tools are not meant for such use cases.
The limitations of the traditional monitoring systems are often worked around by developing various in-house scripts that parse some data and check the health of services. However, the plethora of scripts, each doing its job in a different way, saving state to ad-hoc files, leads to a setup that is very hard to manage.
Monique.io brings structure to the custom monitoring. The health-check results are grouped as reports that can be automatically visualized on a dashboard and queried through the API, which also supports storing intermediate state needed for the health checks.