

Apache JMeter and OpenText Core Performance Engineering (LoadRunner Cloud) are major players in the performance testing tools category. OpenText Core Performance Engineering holds an edge for large organizations with its robust features suitable for complex scenarios and scalability needs.
Features: JMeter is open-source, offering cost savings, a variety of plugins, and easy integration with various platforms. It is highly flexible, particularly for users with limited programming skills. LoadRunner Cloud supports a wide range of protocols, provides comprehensive testing solutions, and ensures high scalability for complex performance testing.
Room for Improvement: JMeter struggles with high load handling, leading to GUI freezes and report management challenges. Improvements could include better support for concurrent users and CI/CD integration. LoadRunner Cloud needs enhanced reporting capabilities, better support for agile models, and more competitive pricing.
Ease of Deployment and Customer Service: JMeter can be deployed on-premises or in the cloud but lacks official support, relying on community-driven assistance. LoadRunner Cloud offers a cloud-focused easy deployment process with a structured support system, which provides efficient help for users.
Pricing and ROI: JMeter is free and ideal for smaller projects due to its zero licensing costs, making it budget-friendly. LoadRunner Cloud, though costly, justifies its price with advanced features and support designed for large enterprises seeking thorough performance testing solutions.
With Apache JMeter, I have gained great statistics for performance and server metrics.
I have not seen a return on investment right now, as there is no improvement in Apache JMeter and reduction in cost, but I save time and reduce costs with Apache JMeter.
I have seen a return on investment with OpenText Core Performance Engineering (LoadRunner Cloud); the analysis part is quite helpful, which is quite more descriptive than the other products.
LoadRunner Cloud helps with risk elimination by reducing performance degradation in production, ensuring a better end-user experience.
The ROI is not necessarily cost savings. Sometimes a customer wants to use OpenText LoadRunner Cloud, or it's the only tool that will solve the problem depending on the application.
With AI models ChatGPT, troubleshooting issues has become very easy for us.
The support for Apache JMeter is excellent.
Apache JMeter has strong support through its vast Java-based community on platforms like Stack Overflow.
I faced issues with OpenText LoadRunner Cloud support when a problem took three to four months to resolve, which negatively impacted our project, especially when key team members were unavailable during leave periods.
It's important to note that OpenText has recently taken over Micro Focus.
The customer support for OpenText Core Performance Engineering (LoadRunner Cloud) is very good.
We do have some methods where we can distribute the complete load between multiple systems and then try to do our testing.
JMeter is highly scalable, easily handling increased loads through the use of multiple servers.
This restricts the number of users and necessitates increasing load agents or distributing the script across multiple machines.
It is very scalable, and on the cloud, it's even more scalable, potentially unlimited.
With load generators available, it is easily scalable to meet our needs.
The scalability of OpenText Core Performance Engineering (LoadRunner Cloud) is based on its cloud-native architecture, which demonstrates strong load handling capacity and stable protocol and script execution.
JMeter performs exceptionally well, especially in non-GUI mode, which supports high loads efficiently.
Several necessary features still need improvements, specifically in terms of reports and additional functionalities compared to other commercial tools.
Previous versions of Apache JMeter were a little unstable, but the new versions are very stable.
OpenText LoadRunner Cloud is extremely stable for our use case.
OpenText Core Performance Engineering (LoadRunner Cloud) is a stable tool that demonstrates strong performance reliability.
With AI becoming more prominent, they can implement features where it can generate code by itself based on the results or provide suggestions.
Currently, we need to use multiple separate JMeter instances to simulate reductions in load, which isn't ideal.
The tool needs improvements related to client-side metrics, integrating with tools like YSlow or HTTP Watch, and enhancing mobile testing capabilities.
In-depth analysis tools found in the standalone LoadRunner analysis, such as graph merging and setting granularity, would be beneficial.
The technical personnel are not able to fix issues quickly, which becomes problematic during critical situations.
The database schema and everything, if they generate on the fly, that will be quite helpful, rather than creating it on our own.
Using JMeter helps us avoid additional costs for high-load testing since it is open-source and allows for unlimited virtual users at no extra cost.
My experience with pricing, setup cost, and licensing is that the cost and license are free because Apache JMeter is open source.
It's a cost-effective solution.
OpenText LoadRunner Cloud pricing is flexible, offering a more affordable solution compared to the more expensive on-premise LoadRunner.
It's delivering functionality, but we also use JMeter, which is free.
As for the pricing of OpenText Core Performance Engineering (LoadRunner Cloud), I find it quite expensive compared to other products in the market.
JMeter facilitates scripting capabilities, which include options for Groovy scripts.
It's useful for both the person conducting the test and the higher management, like project managers or senior executives, who may not know about the test.
Despite being open source, it offers features comparable to paid tools.
OpenText Core Performance Engineering (LoadRunner Cloud)'s advanced analytics help identify performance bottlenecks because whenever we are executing the test scripts, it shows a good analytics view where we can simply check which APIs are not performing well.
The massive cloud scalability and fast design of OpenText Core Performance Engineering (LoadRunner Cloud) have helped me optimize performance, identify performance bottlenecks in my application, generate the traffic, and recognize performance reliability issues in my application.
OpenText LoadRunner Cloud can scale in a cloud-based environment to support up to ten thousand concurrent users without capacity loss, which is not possible with on-premise solutions on personal machines.
| Product | Mindshare (%) |
|---|---|
| Apache JMeter | 10.4% |
| OpenText Core Performance Engineering (LoadRunner Cloud) | 7.8% |
| Other | 81.8% |

| Company Size | Count |
|---|---|
| Small Business | 27 |
| Midsize Enterprise | 25 |
| Large Enterprise | 56 |
| Company Size | Count |
|---|---|
| Small Business | 6 |
| Midsize Enterprise | 9 |
| Large Enterprise | 30 |
Apache JMeter is a versatile, open-source tool designed for performance and load testing, widely recognized for its user-friendly interface and robust test automation capabilities. It supports a range of protocols and integrates seamlessly into various environments, making it ideal for high-load scenarios.
Apache JMeter stands out in performance testing for its ability to handle high transactions per second and perform distributed load testing effectively. Its open-source nature and cost-effectiveness are enhanced by its user-friendly GUI, which simplifies the testing process. Despite memory consumption concerns, Apache JMeter remains a top choice due to its large community support, comprehensive scripting capabilities, and easy integration with CI/CD pipelines, allowing for continuous automated testing. Its robust protocol support meets diverse testing needs.
What are Apache JMeter's key features?In industries like finance and banking, Apache JMeter is used extensively for performance validation to ensure system robustness under heavy user loads. It's integrated into CI/CD pipelines for automated testing processes, allowing organizations to simulate real-world scenarios and ensure high-performance standards.
OpenText Core Performance Engineering offers scalable and efficient load testing using a cloud-based architecture, eliminating the need for physical infrastructure and supporting a wide range of users and testing scenarios.
OpenText Core Performance Engineering supports seamless integration with popular tools and delivers real-time anomaly detection and performance insights. With an intuitive interface, it supports scripting protocols and provides tests for cloud-hosted and on-premise applications. The platform streamlines performance testing and infrastructure management, addressing the needs of diverse sectors like banking, retail, and IT. However, it requires enhancements in reporting, integration, documentation, and support for older scripts.
What are the key features?Organizations in banking, retail, and IT sectors implement OpenText Core Performance Engineering for performance testing, integrating it within CI/CD pipelines. It suits public server application testing and enterprise systems like SAP and Salesforce, meeting diverse industry demands for app stability and responsiveness testing.
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