What do you like best about Coralogix?
What I like most about Coralogix is how easy it makes day-to-day debugging and monitoring. I mostly use it for logs, dashboards, alerts, and investigating production issues. The search and filtering are very useful, especially when I need to quickly narrow things down by service, environment, request ID, or error type.
The dashboards are also helpful for keeping track of latency, error rates, and overall service health. Performance has been good even when searching through a large amount of logs, which matters a lot during incidents.
The UI is generally good, although some of the advanced queries and configurations take a little time to learn. Once you get used to it, regular usage is pretty straightforward. Integrations with the rest of our monitoring setup have also worked well.
From a pricing and ROI point of view, the biggest benefit for me is the engineering time it saves. Faster debugging and having logs, metrics, and alerts in one place reduces the amount of time spent investigating issues. Cost can go up with higher log volume, so that is something teams need to manage, but overall the value is good if the platform is configured properly.
Onboarding was fairly smooth, and the documentation covers most of what you need to get started. Support has also been helpful when we needed clarification around setup or configuration.
The AI features are a nice addition as well. They are useful for getting more context around an issue or finding patterns faster, although I still mainly rely on normal log investigation.
Overall, Coralogix has been a solid observability tool for us, especially for production debugging and monitoring. Review collected by and hosted on G2.com.
What do you dislike about Coralogix?
One thing I dislike about Coralogix is that some of the more advanced features can take time to learn. Basic log search and dashboards are easy enough, but once you start working with more complex queries, parsing, alert configurations, or advanced dashboards, the UI can sometimes feel a bit overwhelming.
Cost is another area that needs attention. Since pricing is closely tied to the amount of data being ingested and retained, it can become expensive if log volumes grow quickly. Teams need to be careful about what they send, how long they retain it, and how their pipelines are configured. Better visibility into cost impact while configuring ingestion would be useful.
Sometimes finding the exact setting or configuration you need also takes more clicks than expected. There are a lot of capabilities in the platform, which is good, but that also means the experience can feel a little crowded in certain areas.
The AI features are useful, but I do not find them equally helpful in every situation yet. For complex production issues, I still rely more on manually checking logs, metrics, and traces rather than depending completely on AI-generated insights.
Documentation is generally good, but for some advanced or less common use cases, it can take a bit of searching to find the exact information you need. Support helps in those cases, but having more practical examples for advanced configurations would make the experience better.
Overall, these are mostly usability and cost-management improvements rather than major problems. The platform is powerful, but making some of the advanced workflows simpler and giving users more control and visibility around cost would improve the experience. Review collected by and hosted on G2.com.
