My use case for Elastic Observability is observability, as we upload our customers' data, including logs, and when there is an issue, we can analyze what went wrong.
Assistant Vice President at QualityKiosk Technologies Pvt. Ltd.
Reseller
Top 5
Sep 11, 2025
A typical use case for Elastic Observability is a centralized logging system. That's what it does. My relationship with Elastic is as a reseller and implementer.
My experience with Elastic Observability is quite extensive, as that is how everything started for me. Initially, Elastic started with logs, but then observability came, followed by security on top of the basic features. The whole thing that ties everything together is that they used to have a lot of tools that are very lightweight; for example, you can deploy an agent and get logs from any platform, whether Windows, Linux, or others. Yet, there was one thing missing with those kinds of tools, and that is central management. Previously, if you wanted to change the path of a file or the port where you wanted to listen for logs, someone had to log in or SSH into the box to make those changes. However, with the Elastic agent, all the configurations are maintained within Elastic. All you need to do is buy a dummy agent, and you can control the policies, deciding which rules you want to push to, say, a group of Linux nodes. All the Linux nodes should have these listeners to read the log messages, whatever they are. Everything comes out of the box, and there's a great UI displaying all the agents in tabs with many filters to control them. APM works similarly; it uses the same agent but includes instrumentation files for various languages. For C++, they have certain modules and JAR files for Java, so you just need to instrument your code to see the function calls, APIs, and more on the screen.
Senior Technical Sales at a tech vendor with 1,001-5,000 employees
Real User
Top 20
Jan 14, 2025
The first time I encountered Elk was sometime around 2018. During that period, I was involved with a customer who had a very strict requirement. They had their own data center and a cloud data center, and they wanted to ensure comprehensive monitoring. This included not only applications from an application perspective but also network, code-level analysis, end-user experience, and more. This was why the customer considered Elk as an observability solution, which one of their consultants proposed. They requested a proof of concept from both vendors, and I was the consultant who conducted a POC while also evaluating Elk. The customer selected both solutions, choosing Riverbed for monitoring network infrastructure and end-user experience and Elastic for log analytics and their cloud services. I continued extensive analysis in healthcare and recently collaborated on a project with a bank in South Africa involving both Elk and Riverbed.
Chief Cloud Architect at a tech services company with 11-50 employees
Real User
Top 20
Nov 8, 2024
Elastic Observability correlates different sources and teams to provide a single, unified, achievable goal for businesses. We offer Elastic Observation and security as part of our managed services to our customers.
We have our workload in the cloud, and we take the data from the workload, put it in a data lake, and then analyze it. We keep track, build down anomalies, and so on.
Enterprise Architect at a mining and metals company with 10,001+ employees
Real User
Top 5
Sep 13, 2024
I use Elastic more for monitoring integration. For instance, I have OpenShift tools implemented in my company, and I have some workloads to integrate, expose APIs, and perform transformations from a source to the target application. I am using Elastic to monitor integration capabilities and track messages.
Principal Reliability Engineer at a retailer with 10,001+ employees
Real User
Top 20
Jan 29, 2024
We use the solution to collect logs. It also helps us with application performance monitoring. We use it for centralized logs and visualizing them with Grafana.
Chief Revenue Officer at a media company with 11-50 employees
Real User
Sep 28, 2023
As we have access to all the features offered by Elastic Observability, we utilize it for APM, to provide support and manage our infrastructure, and even leverage it for our CRM needs.
Chief Operating Officer at Integra Micro Software Services, Bangalore
Real User
Top 20
Jul 3, 2023
We use Elastic Observability for APM, basically. Right now, it is used only for application performance monitoring. In short, it is only the APM module that Observability is looked upon for by all our customers.
We use Elastic Observability for system monitoring, server monitoring, and application monitoring. I'm working on a project wherein I use the solution for capacity planning.
Managing Director at a tech vendor with 10,001+ employees
Real User
Jun 22, 2022
I use this product in projects that we do for other companies. We use the most updated version of the solution. We're using Elastic to get information for several points of observability and several projects and solutions. We're using it broadly in lots of systems. For each solution, we're defining the observability points and the data we want to capture in each point. We're deploying Elastic as the tool to capture the data in each of these points in these transactions, and then putting that in the database. It allows us to analyze not only the number of transactions and quantities, but also the business content of each payload of the transactions in order to have business KPIs, not just technical KPIs. We have more than 300 data capture points in several systems. This has been used by an IO monitoring team. We have two types of users: technical guys that are monitoring the stability of the systems where this tool is used, to see if we are having issues on the operation. This is the IO management team, and there are around 40 users. The second category is people related to business that are actually using this to capture business information, like the amount of transactions, credit sales, the average value of each operation, and things like that. In that sense, there are about 100 people looking at business dashboards. The use is much heavier with the first group. They are tuning systems and deploying new data capture points, etc. Although there are more people in the second group, they are using it more to get the information and use it for tech and business decisions, but they are not heavy users in that sense.
SDE-IV at a tech company with 1,001-5,000 employees
Real User
Oct 26, 2021
We basically use Elastic APM for our metrics to look at our performance. Whenever people say that there's a latency of more than a certain amount, then we just open this APM and see why exactly the latency is high. We can choose that data set, and then we can go deeper.
Elastic Observability offers a comprehensive suite for log analytics, application performance monitoring, and machine learning. It integrates seamlessly with platforms like Teams and Slack, enhancing data visualization and scalability for real-time insights.Elastic Observability is designed to support production environments with features like logging, data collection, and infrastructure tracking. Centralized logging and powerful search functionalities make incident response and performance...
My use case for Elastic Observability is observability, as we upload our customers' data, including logs, and when there is an issue, we can analyze what went wrong.
A typical use case for Elastic Observability is a centralized logging system. That's what it does. My relationship with Elastic is as a reseller and implementer.
My experience with Elastic Observability is quite extensive, as that is how everything started for me. Initially, Elastic started with logs, but then observability came, followed by security on top of the basic features. The whole thing that ties everything together is that they used to have a lot of tools that are very lightweight; for example, you can deploy an agent and get logs from any platform, whether Windows, Linux, or others. Yet, there was one thing missing with those kinds of tools, and that is central management. Previously, if you wanted to change the path of a file or the port where you wanted to listen for logs, someone had to log in or SSH into the box to make those changes. However, with the Elastic agent, all the configurations are maintained within Elastic. All you need to do is buy a dummy agent, and you can control the policies, deciding which rules you want to push to, say, a group of Linux nodes. All the Linux nodes should have these listeners to read the log messages, whatever they are. Everything comes out of the box, and there's a great UI displaying all the agents in tabs with many filters to control them. APM works similarly; it uses the same agent but includes instrumentation files for various languages. For C++, they have certain modules and JAR files for Java, so you just need to instrument your code to see the function calls, APIs, and more on the screen.
The first time I encountered Elk was sometime around 2018. During that period, I was involved with a customer who had a very strict requirement. They had their own data center and a cloud data center, and they wanted to ensure comprehensive monitoring. This included not only applications from an application perspective but also network, code-level analysis, end-user experience, and more. This was why the customer considered Elk as an observability solution, which one of their consultants proposed. They requested a proof of concept from both vendors, and I was the consultant who conducted a POC while also evaluating Elk. The customer selected both solutions, choosing Riverbed for monitoring network infrastructure and end-user experience and Elastic for log analytics and their cloud services. I continued extensive analysis in healthcare and recently collaborated on a project with a bank in South Africa involving both Elk and Riverbed.
Elastic Observability correlates different sources and teams to provide a single, unified, achievable goal for businesses. We offer Elastic Observation and security as part of our managed services to our customers.
We have our workload in the cloud, and we take the data from the workload, put it in a data lake, and then analyze it. We keep track, build down anomalies, and so on.
I use Elastic more for monitoring integration. For instance, I have OpenShift tools implemented in my company, and I have some workloads to integrate, expose APIs, and perform transformations from a source to the target application. I am using Elastic to monitor integration capabilities and track messages.
We use the solution to collect logs. It also helps us with application performance monitoring. We use it for centralized logs and visualizing them with Grafana.
Our clients use the product for monitoring and alerting.
As we have access to all the features offered by Elastic Observability, we utilize it for APM, to provide support and manage our infrastructure, and even leverage it for our CRM needs.
We use the product to monitor our infrastructure.
We use Elastic Observability for APM, basically. Right now, it is used only for application performance monitoring. In short, it is only the APM module that Observability is looked upon for by all our customers.
We use Elastic Observability for system monitoring, server monitoring, and application monitoring. I'm working on a project wherein I use the solution for capacity planning.
We are using Elastic Observability for monitoring.
We usually use the solution in our production environment to monitor production on Rancher. I'm a DevOps engineer.
I use this product in projects that we do for other companies. We use the most updated version of the solution. We're using Elastic to get information for several points of observability and several projects and solutions. We're using it broadly in lots of systems. For each solution, we're defining the observability points and the data we want to capture in each point. We're deploying Elastic as the tool to capture the data in each of these points in these transactions, and then putting that in the database. It allows us to analyze not only the number of transactions and quantities, but also the business content of each payload of the transactions in order to have business KPIs, not just technical KPIs. We have more than 300 data capture points in several systems. This has been used by an IO monitoring team. We have two types of users: technical guys that are monitoring the stability of the systems where this tool is used, to see if we are having issues on the operation. This is the IO management team, and there are around 40 users. The second category is people related to business that are actually using this to capture business information, like the amount of transactions, credit sales, the average value of each operation, and things like that. In that sense, there are about 100 people looking at business dashboards. The use is much heavier with the first group. They are tuning systems and deploying new data capture points, etc. Although there are more people in the second group, they are using it more to get the information and use it for tech and business decisions, but they are not heavy users in that sense.
We are using Elastic APM primarily for central logging.
We basically use Elastic APM for our metrics to look at our performance. Whenever people say that there's a latency of more than a certain amount, then we just open this APM and see why exactly the latency is high. We can choose that data set, and then we can go deeper.
We use it for monitoring the application performance and development.
The primary use case for our organization is handling login events. We also utilize it for some big data use cases.