laravel-backup VS Grafana

https://github.com/jeremykenedy/laravel-logger#authentication-middleware-usage https://github.com/beyondcode/laravel-dump-server https://github.com/barryvdh/laravel-debugbar https://github.com/laravel-shift/blueprint https://github.com/spatie/laravel-backup https://github.com/spatie/laravel-permission https://github.com/spatie/laravel-activitylog https://github.com/realrashid/sweet-alert https://github.com/rappasoft/laravel-livewire-tables https://github.com/yajra/laravel-datatables https://github.com/Labs64/laravel-boilerplate https://github.com/creativetimofficial/argon-dashboard-laravel https://github.com/the-control-group/voyager https://github.com/beyondcode/laravel-er-diagram-generator

8. spatie/laravel-backup

4. spatie/laravel-backup https://github.com/spatie/laravel-backup

In my case I'm dumping + zipping the entire database at the application level. In my case is as simple as adding a library [1], scheduling the job and transferring to AWS S3 (my main application is on DigitalOcean)

[1] https://github.com/spatie/laravel-backup

Database backups via php artisan backup:run

Monitor and Maintain: Use monitoring tools like Prometheus or Grafana to track the AI agent’s performance, handle scaling, manage updates, and address any security issues.

Only time will tell what I'll do with this cluster. However, I've been thinking of adding Prometheus and Grafana to have some metrics and nice graphs to check on the cluster.

Monitoring tools such as Prometheus and Grafana can provide real-time insights into how your rate limiting is performing and where adjustments may be needed.

The final question is where. During development, it is very convenient to write any type of message to the debug console. However, in production or staging environments, it is very useful to have online systems to send messages, such as Sentry or Grafana, or, where that's not possible, a mechanism for saving them to the file system (or both and other options!).

Deployment and monitoring Once the tests are successful, you can pull the necessary artifacts (commonly a docker image) to the production server and deploy the updates. You can also build a monitoring dashboard using your favorite tools (Grafana, Streamlit, etc.) to gauge application metrics or employ model monitoring for deployed machine learning models. Use the following commands to unpack the code from your remote registry to your deployment server and deploy it.

Grafana is open-source and also has a managed version. You can query both metrics and logs. It has a very active community. You can set up and try Grafana on your local machine easily using Docker.

Grafana

Prometheus and Grafana are two popular open-source tools that are often used together for monitoring and visualization.