HeadSpin vs. Splunk Application Performance Monitoring (APM)

We have been using HeadSpin for our web and mobile application testing. It is an easy-to-use solution that provides detailed and in-depth analytics from the testing results. We also get a wide range of live networks and devices to perform the testing from HeadSpin. It has an inbuilt artificial intelligence engine that helps us to get real-time scenarios simulated, which is very helpful for finding performance issues and improving the overall quality of the applications.

Good for below cases 1. There is a front end and need to correlate data with front end data 2. multiple microservices and need to check the health of each system 3. correlate data from various sources 4. Application performance is a key to be captured 5. application performance is a key metric.

Incentivized

• Keep track of each browser we have tested and when we lastly updated them.
• Remove any deprecated code or plugins that may cause issues with other browsers.
• Add browser information to our site’s meta tags so that the site displays correctly in different browsers.
• Test our website on all browsers to ensure compatibility.

• Detect and Troubleshoot, which allows team to eliminate errors.
• Dashboard creates a one stop location for environment health
• Provides visibility, access to logs and user data real time.

Incentivized

• UI needs to be improved
• Graphs on the dashboard are a bit laggy and need detailing for a better understanding of the user

Incentivized

• I wish Splunk Application Performance Monitoring could integrate with packet capture and analysis tools and provide the integrated analysis results on each tier of the application

Incentivized

Splunk is a great tool for log mining. It is less time-consuming and easy to use. It provides high security, it has customisable dashboards. Furthermore, it acts as a search head, and it gives real-time status. It has the ability to collect data from any source and multiple sources, and it can correlate them.

Incentivized

Though LambdaTest was cheaper, it did not offer the deal devices and we wanted to be 100% sure that our app offers a perfect experience to all users. Simulators and emulators are not the technology we wanted to rely on.

It is good that the correlation analysis and the cause of the failure can be analyzed more extensively through the collection of many data. It is good because it provides a user interface (usability) well and can be used conveniently.
It is good because it can utilize anomaly detection through various AI-based ML models.

Incentivized

• Simplified the approach to test the mobile application on multiple devices from different vendors. This helped us to ensure the stability of the application in different environments.
• In-depth analysis to understand the loopholes in the application design function. This helped us to improve the application design and overall user experience.

Incentivized

• It gave us more quicker alerts in a organize way to distribute to specific audience.
• Helped us to improve our uptime by getting pro-active alerts via emails
• More collaboration in a DevOps culture

Incentivized