Coralogix Reviews

In my organization, particularly in Ericsson's telecom BSS domain, the primary use case of Coralogix is centralized log management and real-time monitoring of telecom applications, such as the BSS software products and infrastructure. We have multiple SDP and AIR components in our telecom stack, including application servers, network elements, microservices, and APIs. Coralogix acts as a single platform where all logs are aggregated, which makes troubleshooting much faster compared to using different systems. The platform also helps with monitoring live traffic and system behavior. We have configured alerts for critical scenarios such as service downtime, API failures, error rates, and spikes.

Using Coralogix has significantly improved the efficiency and structure of my daily work, especially in monitoring and troubleshooting. Previously, we relied on manual checks and customer complaints to identify different issues. Now, with real-time alerts in Coralogix, we get notified immediately when something goes wrong, such as API failures or abnormal error spikes. The approach has shifted from reactive troubleshooting to proactive monitoring. Previously, we had to log into multiple servers and manually check logs, which was time-consuming. Now, everything is centralized in Coralogix, and with powerful search and filtering, we can quickly pinpoint issues within minutes instead of hours.

One recent incident involved a production issue where one of our SDP application services started showing intermittent API failures during peak traffic hours. Subscribers in Singapore were experiencing delayed responses, and some transactions were failing. Initially, it was difficult to identify the root cause because multiple microservices and backend systems were involved. Using Coralogix, we were able to quickly narrow down the issue through real-time log monitoring, centralized log correlation, advanced filtering and search, and alerting dashboards. We observed through Coralogix dashboards that error rates suddenly increased for a particular service after a deployment. By filtering logs based on transaction IDs and timestamps, we traced the issue to a backend service timeout caused by a misconfigured connection pool. The biggest advantage was that we did not need to manually log into multiple servers to investigate. Everything was visible from a single platform, which saved considerable troubleshooting time. Because of Coralogix alerts, the operations team was informed immediately, and we resolved the issue much faster than our general earlier processes, significantly reducing troubleshooting time and providing faster root cause identification.

In my experience, a few features of Coralogix stand out and make it very effective for day-to-day operations. First, the centralized log management makes all logs from different systems, applications, servers, and microservices available in one place. This eliminates the need to check multiple servers and makes troubleshooting much faster. Second, the powerful search capability is very strong. We can filter logs based on time range, severity, transaction IDs, or specific keywords, which helps quickly identify issues in complex telecom environments such as our BSS products. Third, the real-time alerting allows us to configure alerts for critical events such as error spikes, service failures, and latency issues. This helps in immediate detection and faster response, reducing downtime.

Among all the features, the one that makes the biggest difference for our team is centralized log management with powerful search capability in Coralogix. In our environment, multiple systems are involved, including application servers, APIs, backend systems, and network components, all in production with customers and subscribers using them. Previously, troubleshooting meant logging into those servers and manually checking each log, which was time-consuming and inefficient. With Coralogix, all logs are available in one place. We can quickly search using filters such as transaction ID, particular timestamp, or error code. We can correlate logs across multiple services. This has drastically reduced our mean time to resolution. The real impact is faster issue identification and less manual effort.

Coralogix works well for our needs, but there are a few areas where improvements can be made. One area is querying performance for large-scale data sets. When we are dealing with very high log volumes, some complex queries take time to return results. Improving query speed and optimization would enhance the troubleshooting experience. Another point is the learning curve for advanced features. While basic usage is straightforward, advanced querying and dashboard configurations can take time for new users we are onboarding. We have faced this situation in our organization's domain frequently. More simplified UI options or guided templates would help new team members onboard faster. Additionally, dashboard customization flexibility needs improvement. Although dashboards are useful, having more flexibility in customization would make them even more powerful. An important point is cost optimization. Since log volume is high in our environment, better visibility and control over cost optimization would be beneficial.

These are minor improvements overall. Coralogix already provides strong capabilities for centralized logging and monitoring, but enhancing these areas would make it even more efficient for large-scale environments in our telecom servers. Improvements could include query performance, alert noise reduction, and ease of use for advanced features, especially for large-scale environments like ours.

I have been using Coralogix for more than four years.

In our experience, Coralogix has been quite stable and reliable for our day-to-day operations. We use it continuously for monitoring and troubleshooting, and we have not faced any major stability issues that impacted our work significantly. The platform is generally highly available. Log ingestion and processing are consistent, even during high traffic. Our charging system dashboards and alerts work reliably in real time. There have been occasional minor delays or latency during very high log volume, but nothing critical or long-lasting.

Coralogix has been highly scalable and well-suited for a high-volume environment such as our charging products. As our system usage and log volume increased, Coralogix was able to handle the growth without requiring any major changes from our side. Our systems generate a huge amount of logs, especially during peak hours, and Coralogix has been able to ingest and process the data smoothly without major performance issues. Since it is a managed SaaS platform, scaling is handled by Coralogix itself. We do not need to worry about infrastructure, storage, or cluster management. Even as new services or microservices were added, the platform continued to perform reliably in terms of search, monitoring, and alerting.

Whenever we have raised issues or queries, the support team has generally responded in a timely manner and helped us resolve problems effectively. For most cases, especially critical issues, the response time has been quick, which is important in our charging environment in telecom. The support team has good technical knowledge and is able to understand log-related monitoring issues without much back and forth. During initial setup or when configuring alerts and dashboards, their guidance was useful. The support has been reliable and helpful for our day-to-day needs.

Before Coralogix, we were using a combination of traditional logging approaches and tools such as the ELK stack and ElasticSearch specifically. While ELK is powerful, we faced a few challenges in our environment. It had a high maintenance overhead, and managing infrastructure storage and scaling ElasticSearch clusters required significant effort. As log volume increased day by day, query performance and indexing became slower. Compared to modern observability platforms, it had very limited real-time alerting capabilities. It also had operational complexity and required dedicated effort for tuning and upkeep. We switched to Coralogix because of its SaaS model with no need to manage infrastructure, better real-time monitoring and alerting, and faster and more efficient log search with scalability without operational burden. Previously using the ELK stack, we moved to Coralogix due to high maintenance and scalability challenges, choosing it for its SaaS model, better performance, and real-time monitoring capabilities.

Before adopting Coralogix in our organization, our organization evaluated a few other observability and log management tools to find the best fit for our telecom environment. Some options were Splunk, Datadog, and the ELK stack. Tools such as Splunk can become quite expensive with high log volume. Coralogix provided a more cost-effective model for our use cases. In telecom, log ingestion is massive, and Coralogix handled high-volume logs efficiently without requiring us to manage infrastructure. Compared to ELK, Coralogix removed the need for managing cluster storage and scaling.

In our case, the procurement of Coralogix was handled at the organizational or management level. I am not directly involved in the purchasing process. However, it can be purchased either directly from the vendor or through platforms such as the AWS marketplace. From a user perspective, we mainly focus on using the platform for monitoring and troubleshooting rather than the procurement side.

We have definitely seen a positive return on investment from using Coralogix, both in terms of time savings and improved system reliability. Regarding mean time to resolution, which I have already discussed, previously one to two hours were required to resolve major issues. Now it takes around ten to twenty minutes, representing approximately a sixty to seventy percent reduction in resolution time. Less downtime means fewer service disruptions, which is very critical in our telecom services. Even a small outage can have significant business impacts. Another point is reduced service downtime for revenue protection. With faster detection and resolution, approximately thirty to forty percent reduction in downtime has been achieved over these years. In one incident involving an API failure during peak hours, Coralogix helped us quickly identify a backend timeout issue. Because we resolved it faster, we avoided prolonged service impact and potential customer complaints. Engineers spend less time manually checking logs across the system, with approximately forty to fifty percent of time saved in troubleshooting activities.

My experience with Coralogix pricing and licensing has been generally positive, especially considering the value it provides in terms of monitoring and troubleshooting. It follows a usage-based pricing model that mainly depends on log volume and data ingestion. This works well for us because it is scalable, and we can adjust based on our needs. Some challenges we faced include the fact that in our environment, log volume is very high, so cost management becomes important, and we need to be mindful about which logs to ingest and retain to avoid unnecessary costs. Sometimes, it requires fine-tuning log levels and retention policies. Overall, the pricing is reasonable for the value it delivers, but it needs proper optimization to be cost-effective at scale.

As far as I am aware, our organization's relationship with Coralogix is primarily as a customer using their platform for observability and log management. I am not aware of any additional business relationships such as partnerships, reselling, or strategic collaboration beyond that. My involvement is mainly on the technical and usage side, so procurement or partnership details would be handled by management. We are primarily customers of Coralogix and do not have any additional business relationship beyond that.

While exact numbers can vary across teams, based on our current usage, we have observed clear improvements in a few key areas. I have already discussed mean time to resolution. Previously, one to two hours were easily required to identify and resolve issues. Now, it is twenty to thirty minutes in most cases, representing approximately a sixty to seventy percent reduction in resolution time. The second point is faster issue detection. Previously, issues were often detected after user complaints or manual checks. Now, real-time alerts detect issues within minutes, achieving approximately seventy to eighty percent faster detection. Downtime has also been reduced by thirty to forty percent in service terms. Additionally, team productivity has increased by forty to fifty percent due to less time spent on manual log checking across all servers.

Based on our experience, Coralogix is a powerful tool, but to get the best value out of it, I would suggest a few things. You should always plan your log strategy early and avoid ingesting everything blindly. Define which logs are critical and set proper log levels. This will help with both performance and cost optimization. Second, you need to set up meaningful alerts by configuring them carefully to avoid noise and focus on actionable alerts that really need attention. Too many alerts can reduce effectiveness. Additionally, you should use structured logging. If logs are well-structured in JSON format or with proper fields, searching and analysis become much easier and faster. Finally, build dashboards for key metrics by creating dashboards for important KPIs such as error rates, latency, and service health, which will help in quick monitoring and decision-making. I would rate this product an eight out of ten based on my overall experience.

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.

My main use case for Coralogix is that it's a great application performance monitoring tool. I use it to take care of the services through it. I set up the dashboard here for all the types of services and routes to check whenever we receive any kind of error response, time request, or issues.

The best features Coralogix offers include the ability for any organization, institution, or any kind of project owner to migrate their data to Coralogix. Coralogix has the best feature to do the migration. If we are using Grafana or any other type such as Elasticsearch, Splunk, or Datadog, we are easily able to migrate our data to Coralogix with their features. I really appreciate how they migrate directly by just exporting and importing it.

My experience with the migration process was really straightforward and quick as compared to other tools.

I want to mention that as compared to other application performance monitoring tools, this APM tool specifically has a really good looking dashboard that is very attractive and very easy to use.

After implementing Coralogix, I noticed specific outcomes and improvements. Whenever we try to fetch the data or check the monitoring logs, the spikes, the bars, and the graphs open very quickly. The latency is really very low and it opens everything very fast. It works very fast, which I really appreciate. This makes a good impact on our organization.

I think Coralogix can be improved by setting up some AI type of tool inside it which can help new users. Whenever they face any kind of issue or troubleshooting problem, I know that they already share a user guide whenever we try to set up and install Coralogix and use it for the first time. However, I would prefer an AI feature as per current trends. AI is coming into most tools, so I would prefer the same for Coralogix.

I have used Coralogix for more than four years.

Coralogix is really stable.

Coralogix's scalability is working well. It is totally scalable and not an issue.

Throughout the three to four years I have been using Coralogix, I didn't get any chance to connect directly with the customer support team. However, one of my friends used to talk to the customer care support. His personal official ID got crashed when he was trying to log in after a month. He tried to contact the customer care team by their toll-free number, and they helped him out very easily by connecting with him.

I did not use a different solution after Coralogix.

I see a return on investment in time saving. Fewer employees is a scenario that mostly comes under the NOC team. Fewer employees might not be the solution which can help with this because it's an application performance monitoring tool. For monitoring, we always need multiple employees.

I am not aware of the pricing, setup cost, and licensing for Coralogix, as this comes under the business analyst, marketing team, and pre-sales team. I am from the technical line.

Before choosing Coralogix, I did not evaluate other options. In the first option, I already had Coralogix available, so I chose it after the demo.

Coralogix has not impacted my organization in a bad manner. We are the customer here who uses Coralogix in our day-to-day work, and Coralogix is doing great. It's working very well. We had no such kind of issue with it. I would give Coralogix a rating of 10 on a scale of 1 to 10. I give it a 10 because it works really fast and mostly because of its attractive look. I would advise others looking into using Coralogix to just try to use it for once. You will really appreciate their attractive dashboard and the way they show the performance of the application. Overall, I rate this product a 10 out of 10.

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.

My main use case for Coralogix is to see logs. I initially used it for a logging system, so every user can see the logs. Logs are published via AWS to Coralogix, and I set up log-related alerts and observability to analyze metrics using dashboards. These are the major areas.

When I debug our system, I filter based on the service names that I have already published from the ingress logs. I have also published some MDC tags that are visible, so I filter based on the tags or a particular message or trace ID. This is how I do log analysis. For setting up alerts, I use an alerting dashboard where I set the queries for specific logs. If a log or log-related query appears more than five times within a specific timeframe, such as the last five minutes or one hour, I can raise an alert for errors like 4xx errors.

I have mainly used Coralogix for high scale logging, and I remember the unified observability feature where log metrics and traces can be seen together. I have not used it properly recently as we have moved to a different tool, but they started using AI-powered voice anomaly detection and stream data analytics, which I found useful.

I just remember that they started setting it up, and within four or five days, we started flowing logs into the Coralogix dashboard.

Coralogix was stable most of the time, but I would not say always.

The only improvement I remember is that the cost aspect is a bit more tedious. My company often did not store older logs due to the high cost. There were multiple tiers for querying high-priority logs and archiving low-priority ones. Sometimes, while filtering based on tags, it broke, but it was usually fixed. It would also be beneficial if I could see Kafka-related logs in the system, as we were using a separate system for Kafka.

I remember the OpenTelemetry configuration was not working properly in Coralogix. I am not sure if it is working now or not.

I have around ten years of experience working in my current field.

Coralogix was stable most of the time, but I would not say always.

Customer support was good. There is a chat option on the UI for immediate connection with the team, and if assistance is needed, they set up Zoom calls to help you. I would rate customer support nine out of ten.

I have worked on multiple logging systems, and I would say Coralogix was the best among those. I find Coralogix to be one of the best tools. Currently, I am using Kibana, which is not as user-friendly and lacks fast aggregations compared to Coralogix, which worked really well.

I remember that they started setting it up, and within four or five days, we started flowing logs into the Coralogix dashboard. The fast onboarding process allowed users to start working on it without issues, enabling everyone to see centralized alerts and logs during any issues with upstream or downstream services.

I cannot specify a return on investment, as every company assesses these things based on their tools. However, I find Coralogix to be one of the best tools.

Cost was the only major reason for switching from a different solution.

Coralogix is one place for all logs and analysis. I have not used such a tool. Coralogix is way ahead of its time compared to others I have seen in different companies. My company often did not store older logs due to the high cost. There were multiple tiers for querying high-priority logs and archiving low-priority ones.

Coralogix is a tool you will not regret using.

My main use case for Coralogix is a streaming platform where we log data to store in an S3 bucket for streaming purposes. When we require specific data, we store it in an S3 bucket, including S3 replication. This is how we utilize it.

A specific example of how I use Coralogix in my streaming platform is for analyzing data and addressing data fidelity concerns. We have also used AI features for this purpose. For logging aggregation, we also use it on Kubernetes, which is part of our day-to-day work.

The best features include processing data in streams and enabling alerting through real-time analytics. I also appreciate the cost-efficient storage with AI-powered insights.

Out of real-time analytics, cost-efficient storage, and AI-powered insights, the most valuable for my team has been the cost-efficient storage. We generally store data on some of my customers' clouds, avoiding expensive storage markups. That is why we use it for cost-cutting storage.

Coralogix has positively impacted my organization as we use S3 buckets for logging purposes. We previously used DataDog and Prometheus for that, but later we switched to Coralogix for log management, tracing, security, and AI features.

I believe there is no improvement needed for Coralogix. For our use case, things are working well right now.

I have been using Coralogix for around 2.5 years.

Coralogix is stable.

The scalability of Coralogix is also working well.

The customer support provided by Coralogix is good.

We previously used DataDog and Prometheus for metrics, but we switched to Coralogix because of the clear visibility and the metrics perspective. The readability and usability are very good. Additionally, there were features such as storing data in customer clouds that are better than other logging and metrics platforms.

Before choosing Coralogix, we used DataDog and Prometheus for metrics and switched because there was a clear understanding of metrics and logging with a cost-efficient aspect. We can store data in the customer's cloud without needing separate storage, which allows us to reduce costs. These are the aspects that we appreciate about Coralogix.

With Coralogix, we have saved money and time. These are the key benefits we have observed.

My experience with pricing, setup cost, and licensing is that the overall cost is good. The setup is also very understandable and straightforward. Overall, things are working well right now.

The advice I would give to others looking into Coralogix is that it is a simple platform to use. The integration is also very good. Additionally, you can see the logging and metrics clearly. It is simple to use, and there is a cost-efficient aspect to the storage. These are the features that I value in Coralogix. I would rate this product a 9.

My main use case for Coralogix is inspecting the application logs and system logs, such as logs from Kubernetes.

In my day-to-day work with Coralogix, we have some alerts, and when alerts are triggered, we can take a look at any incoming issue from the application. I also use Coralogix for debugging issues in the application by reviewing the Coralogix logs.

I find that Coralogix is very useful in our day-to-day work.

The best features that Coralogix offers include the ability to see in one place all the logs related to the application from end-to-end. Having all the logs in one place with Coralogix saves time and makes troubleshooting much easier than previously.

Coralogix has positively impacted my organization by allowing us to save time in troubleshooting problems and in troubleshooting a new feature or new code that we are writing.

I see room for improvement in Coralogix regarding the cost, as they can reduce the costs for the license. Because of the cost, we cannot see logs for more than thirty days. The main concern regarding improvements is the cost.

I have been using Coralogix for three years.

Coralogix is quite stable.

The scalability of Coralogix is great since the infrastructure is sitting on the cloud.

Customer support for Coralogix is great, and it is one of the best supports I have experienced.

I previously used an on-premises solution that did not have a solution at all. I was checking the logs from each application and syslog on the var log files, but nothing like what Coralogix offers.

I have seen a return on investment as it is time-saving for debugging since this costs a lot over a period of time.

My experience with pricing, setup cost, and licensing is that it is pretty easy and not an issue at all. I did not need to negotiate for the pricing, and the implementation licensing was very easy.

Before choosing Coralogix, I evaluated other options including Splunk, but it was very expensive, and I also looked into Sumo Logic.

My advice for others looking into using Coralogix is that I would suggest using Coralogix as it is a great tool that can help your engineers with alerting and allows you to see all the logs in one place, which is beneficial. I rate this product an 8 overall.

The use case for my organization is that we need a vendor to host our metrics.

The most valuable feature of Coralogix is that it is a very good vendor for metrics. There are a few competitors in the market, such as Datadog. However, Coralogix has ease of use and the ability to use OpenTelemetry agent, which is open source. This makes onboarding or changing a vendor in the future easier.

Coralogix benefits my organization by providing visibility to what is happening in our services and infrastructure. It is essentially an observability, metrics, and logging tool to monitor our infrastructure.

Change might not be the correct word, but with every service, there is always room to improve. They are improving their services daily and deploy new features. When we had missing features that we needed, they developed and published them quickly.

The features we were missing in the past were related to the way we see our metrics and aggregate our data. It was not a problem or an issue, but rather features we needed to view our data in specific ways. Once they realized these features were missing, they developed them rapidly.

I would appreciate seeing more AI integration aspects.

I have been using Coralogix for a year and a half.

I have worked with the customer service and support for Coralogix a couple of times. My experience with them is that they resolved my issues quickly. They were very responsive and thoroughly communicative.

Positive

Coralogix monitors our infrastructure and our services send data, or the Coralogix agents scrape data from our applications. That is how we integrate Coralogix with our applications.

Their pricing is competitive and their product is stable. We have not experienced any stability issues thus far. Integrating AI is the most interesting aspect currently, and they could potentially integrate AI deeper into their product to show us more insights.

I have used Datadog in the past as a competing product. Datadog is a successful and very good product, but the pricing is very high, which is one of its downsides.

On a scale of one to ten, I would rate the support as nine and Coralogix overall as nine, with one being worst and ten being best.

My main use case for Coralogix is to trace logs and debug the telemetry that we have across the microservices. Whenever our microservices get deployed on production or in stage, we emit these logs to Coralogix, and whenever something goes wrong, I go to Coralogix and debug it based on the applications and the services, looking after all the traces to find out what went wrong.

I sometimes also perform log searches based on certain keyword pattern matching with certain logs across different traces. We put that keyword in the complex search, and then it gives us all the logs that are included in it.

There were multiple scenarios where we have different microservices connected to each other, and if we want to know at what place and which microservice is failing and what the issue is, we search based on the request ID and get all the logs from all different microservices, then figure out where it is failing.

The best features Coralogix offers include the search feature, and the regex search is quite good. The period, network, and all those filters about why the same logs or trace has been connected across different services and microservices help tremendously.

The AI feature is also quite good for writing down the queries and those things. Coralogix improves the resolution speed because we have these logs integrated with our native AI agents, so it automatically fetches the data from Coralogix, and then we have it on Slack to resolve or identify the issue quite quickly.

Time is being saved in tracing logs or debugging any production issues or incidents.

Coralogix can be improved by cleaning up the UI, as it is too cluttered. If the search speed could also be improved, that would be helpful.

I chose seven for my rating because it is good for traces and logs, but I still feel that it can improve its UI and the search feature.

I have been using Coralogix for more than two years.

Coralogix is quite stable.

Coralogix is quite scalable. It is distributed, so we have never felt any issue with Coralogix's scalability.

Customer support is quite good.

Earlier, we were using Sumo Logic, and we migrated from Sumo Logic to Coralogix.

We purchased Coralogix through the AWS Marketplace, but I have less knowledge about the infrastructure and those things. I don't have information about the pricing, setup cost, and licensing, because the infrastructure and those things are handled by a different team.

Time is being saved in tracing logs or debugging any production issues or incidents, but I don't have any handy numbers because I am not the right person to look into the infrastructure or the metrics of Coralogix.

I don't have information about the pricing, setup cost, and licensing, because the infrastructure and those things are handled by a different team.

I didn't evaluate other options before choosing Coralogix. It was our team who did that.

Others looking into using Coralogix can trust it and can adopt it because it is good enough to use, with costs also being less than competitors. I gave this product a rating of seven.

My main use case for Coralogix was that we transferred from DataDog because DataDog was expensive at the time, and we wanted something more cost-effective. The VP of R&D thought Coralogix would be a great solution to replace DataDog. We used it for observability, logs of services, traceability of services that reach from certain endpoints, and everything related to metrics. We also used it in the queries and their Grafana managed dashboards, so we could view all of our Kubernetes workloads, whether it was RabbitMQ brokers panels or Kafka. We integrated it with so many things.

A specific example of how I used Coralogix in my daily work is that I opened Coralogix when developers had issues with their services, such as having 500 error codes or 400 error codes. We would observe the logs and see the logs that were transferred to Coralogix for the service, which were enriched with the data of the name of their service, the tags, and many other things that we did with the help of OpenTelemetry agents that transferred the data to Coralogix. We also sent log groups of specific RDSs that we managed via AWS, so it was better for metrics gathering and logs observability.

The best features Coralogix offers include its very nice tool and the usage of AI, which was very useful. The AI could describe the log that was received, providing a window that explains what the log actually says, what the issues are, the impact, and many things that help to summarize the log. The console is pretty fine; you can navigate and see many things that you want.

The AI feature impacted my work by helping me to reduce my time wasted on exploring what the error means and going to Google to search for it or maybe going inside the pods of the services to see what the issue actually is. The AI summarizes the data, enriches it, and provides a better view of what I actually need to handle.

Coralogix has positively impacted my organization by handling the responsibility for the developers to track their services and see what is actually going on there in terms of logs of their services, whether it is info, debug, error, or warnings. It gave a better view of things, and you can query against things that you want to see in terms of logs. The Grafana-managed dashboards allow you to actually see the metrics of your specific workloads. I believe it provides better observability and visibility. Coralogix is a nice tool that helps developers, DevOps, and SRE track services and see their health status.

After implementing Coralogix, we noticed improvements such as integrating it with our Squadcast, which is an alerting system that actually alerts when an incident arises. We used the alerting system of Coralogix and explained via PromQL, which is a query language that helped us write alerts based on queries. When we saw a pod's CPU utilization metrics exceed the predefined limit, we wanted to alert our SRE and DevOps team so they can open and see the alert URL and log URL. Via Coralogix, we could examine the logs and see the actual issue. It was very nice to integrate it with Squadcast and have SMS and calls when an alert is received.

This setup helped our team respond to incidents more quickly, and I think we improved our incident response time. The SMS and phone calls are received immediately; once the alert is triggered, it triggers Squadcast, which notifies me with SMS and then calls me.

Coralogix can be improved by having better documentation to help new people onboard into this platform and understand the systems, including how they can integrate their cloud provider to better understand how Coralogix and the cloud provider work in sync. If you really want to understand it, I think you need to find someone who has worked with it for at least a couple of years.

There are other improvements needed for Coralogix, such as their Helm charts and Terraform providers. I remember back at that time, the Terraform providers were without any support from the engineering team, and they were outdated and had many bugs.

I have been using Coralogix for at least two years.

I do not have any specific advice for others looking into using Coralogix. My overall review rating for Coralogix is eight out of ten.