My main use case with Coralogix has been to troubleshoot, narrow down the problem, understand the logs, and identify errors.

For troubleshooting or analyzing logs, we usually employ two methods. The first method is sorting by time, where we trigger an action and see in real-time if it appears or not. The second one involves using services. We know that some actions will trigger specific services, so we filter it by services and, after triggering it, we check if there is an error or a log trace to see in which service the error is happening.

The best features of Coralogix are the real-time logging and the filtering, which are what we use the most.

The real-time log feature helps me in my day-to-day work when I am trying to test some APIs; I expect to see the logs in real-time as I trigger them. The filtering helps us understand where the bottlenecks and deadlocks are, so when we send a request to a specific service, we filter it and monitor it. This way, we narrow down the problem; if it passes the services successfully, we know the issue lies elsewhere, not in that service.

Coralogix has positively impacted our organization by providing us with a clearer data flow, which allows us to analyze data better and find errors easier using the smart logs it offers.

Coralogix has many features, but we usually use only these two, and the syntax has not been so straightforward. It was a bit difficult to write specific queries, so I have templates of specific queries where I just change the ID or the service. It is nice to see the whole picture, but it requires getting used to the program.

Coralogix can be improved by simplifying it. Perhaps, with AI now, it could allow users to write in plain text and then create the query automatically. The main pain issue for me with Coralogix was that the syntax was a little tricky. Although Coralogix has so many features, I mainly used only a few due to difficulties in knowing how to write them. It was also chained, making it a little tricky. Therefore, I created templates for easier usage. The potential of Coralogix is huge, but we used it this way since no one knew how to use it correctly. We once had a session explaining all the features, but we took very little from it as it was difficult for us. With many microservices, it was sometimes tricky to understand where the data goes. I often activated multiple services to track the flow and would sometimes need extra time to grasp it. I believe now, with AI, it can be improved, allowing for easier queries created from plain text.

Coralogix is stable.

The scalability is very fine as we add more and more microservices.

The customer support at Coralogix is great. We have used them several times, and when we had trouble finding something, they guided us on how to redefine our query to obtain the results we needed.

I would rate the customer support a ten, as they helped us effectively.

Before choosing Coralogix, I evaluated other options, including Splunk, which I find very similar to Coralogix. However, I believe Coralogix is more powerful than Splunk.

My experience with pricing, setup cost, and licensing has been transparent since I am only the engineer using it.

I can confidently say that I experienced faster troubleshooting after using Coralogix.

When I did not find the problem in my Azure logs, which are often trivial issues, I typically use the heavy cannon like Coralogix. It sometimes took me time to investigate, but I usually find solutions there. For me, it was not time-saving but more a solution-saver, which is very important.

We are using different types of logs concurrently, such as Azure.

I would rate Coralogix an eight.

Coralogix serves as my main observability tool, similar to Kibana and Datadog. It provides observability and monitoring of all troubleshooting for all infrastructures and systems in real time. I can analyze and perform root cause analysis in real time, and it helps with logs and real-time access to logs, whether they are structured or unstructured types. It covers all databases, applications, and application-specific logs such as traces. I can use that alongside all metrics, security data, and dashboards. There are certain dashboards available, so most of the use cases we have been using this.

On a day-to-day basis, the use cases for Coralogix include integrating dashboards, integrating Slack and Teams notifications for all opportunities. Alongside, I get the logs in real time and can store whatever is necessary. I can archive certain logs as well, and it has security information and event management, the SIEM capabilities. It can integrate with Prometheus and Grafana, the open-source technologies available, and the open telemetry technologies that are available in the market today.

A specific scenario where Coralogix helped me is when we log into services and systems, and most of the time certain nodes in the cluster, one of the nodes gets down. When one of the nodes is down, I need to log into certain logs or check the services to determine which services are down, and based upon the services, I need to go to the specific log section, take out the logs and see. During the timestamp, I have to analyze the logs along with the timestamp and see what is happening. I need to see the post-event analysis and post-event logs to determine why the server or machine is down, what the cause is, and any specific issues. Coralogix helps with all real-time logs, pattern, and real-time analysis, providing an overview of what is happening. Instead of directly logging into the server, I just go to the Coralogix dashboard and see the logs and the machine, and I perform my RCA, whether I am the system engineer or the system administrator.

One of the best features that Coralogix offers is that it is integration friendly. I can seamlessly work with different cloud providers including AWS, Azure, and GCP. I can monitor Kubernetes or Docker platforms as well, and I can integrate with the DevOps chain including Jenkins and all infrastructure code, Terraform, or Ansible.

Coralogix has positively impacted my organization by providing a centralized console to monitor the dashboard, giving me rich flexibility to see different sorts of data that is spread across the logs, metrics, or traces, which are the typical pillars of the observability tool. I have the interface where I can use the drag-and-drop feature, and I can create different types of charts. Mainly, I have the line charts and time series ones that I generally use in many use cases, gauges, tables, pie charts, or markdown widgets. These are the ones generically available, and I can switch between the visualization types. I am getting the underlying query in that and can import and export dashboards built upon the JSON format. I can have my own APIs integrated with my dashboards as well, such as with Terraform, which is useful for scaling across my environments. Regarding root cause analysis, mainly what I can do is correlate across all of the layers because the main logs that I work on are storage-related, including CIFS, NFS, SAN traffic, and the metrics including storage, throughput, or VM resource usage. Being able to view logs, metrics, or traces available, I get all of these in one place, and I can do root cause analysis much quicker.

I feel there should be room for improvement with Coralogix. Since we are in the present-day generation, I believe there can be more AI integration. Many AI tools are out in the market, and the focus should be on integrating AI with Coralogix as well as expanded capabilities for all notification centers or notification routing. I would appreciate more integrations with different vendors for notification and support.

In terms of documentation, I think there can be more user-friendly documentation that stresses more on day-to-day issues.

I have been using Coralogix for more than two to three years.

In my experience, Coralogix is stable as I have not faced any downtime or reliability issues.

Handling scaling with Coralogix is good, as it is easy to scale up or down as my needs change. Coralogix handles growth and increased workloads smoothly regarding scalability.

My experience with customer support is neutral, as I have not needed help from their support team. Once I deal with them for any issues, I will know more about their support.

Neutral

I did previously use a different solution, specifically evaluating all the solutions available in the market alongside Coralogix. We did use Datadog as well, which is similar to Coralogix. At times we prefer going with partners requiring high-end visibility and more integration and use cases; during those times, customers ask for Datadog. However, when they are more price-sensitive, we typically opt for Coralogix.

My experience with pricing, setup cost, and licensing for Coralogix is that everything was straightforward without challenges.

I have seen a return on investment with Coralogix, particularly in terms of time saved. There is no need to struggle over things; I have to get the things in place, and because it is a straightforward deployment, especially since most of my environments are in the cloud and a mix of on-premises, it has been straightforward.

To monitor and manage costs associated with Coralogix, I analyze my trend, looking at how the data is being ingested. Generally, it is charged based on what we store, and therefore there are certain measures we take internally to keep this aligned and reduce our costs to a certain phase.

Before choosing Coralogix, I evaluated other options, particularly Datadog.

My advice for others looking into using Coralogix is that I would definitely recommend it if they are price-sensitive and need an observability tool with all of the capabilities in the observability chain, covering logs, metrics, and traces. With Coralogix in place, users can get most of the things sorted for developers and operations teams. Typically, the support or operations team will have more analysis into issues or what is happening in the systems or infrastructure, allowing them to analyze at a faster scale rather than directly logging into machines. I would rate this product an 8 out of 10.