Catchpoint

We use Catchpoint for proactive monitoring of application performance, availability, and user experience. It helps us detect and resolve issues before they impact users, optimize service reliability, and monitor third-party dependencies. Our use case includes synthetic monitoring, real user monitoring (RUM), API monitoring, and cloud/network performance tracking, ensuring seamless digital operations.

• Reduced Downtime & Faster Incident Resolution.
• Improved User Experience & Retention.
• Cost Savings Through Performance Optimization.

New Relic and Datadog

Atlassian Jira, Nagios XI, OpsGenie

In our organization, we use them for synthetic monitoring . Across all of our apps and data centers. We are global and have a many apps and Catchpoint is a great part of our performance monitoring systems.

• ROI
• ongoing features to meet needs
• expansion of footprint

We use Catchpoint to conduct transaction/playwright tests, and monitor the performance, by using different nodes across all over the world. We care about around-the-world coverage, and the performance differences. This is how the Catchpoint meets our use case, and we utilize nodes locations, and detail waterfall chart to understand the performance overall.

• fewer outages
• very easy to identify the issue of performance bottleneck

Dynatrace

Dynatrace, Jenkins

Site performance across 23 D2C consumer sites in the US and on-demand content performance metrics. We use Catchpoint to give us directional data on tech and content development in order to optimize the customer experience and maximize conversion and engagement.

• Positive impact on customer engagement and conversion
• Positive impact on site performance/speed across all company verticals

Glassbox, Bazaarvoice

We use Catchpoint to perform synthetic checks of different (SasS) platforms in different regions. The business problem that Catchpoint addresses is the observability of a SaaS application from different regions, providing us with a lot of details such as availability, web response time, latency in different regions. If a problem/alert happens, it notifies the specified teams that will start to investigate why the problem/alert was triggered, which helps to minimize the time to get the right people to work and fix a production problem.

• Positive impact: to identify the web response time per region, and in which URL takes the most to load. That is super important when identifying performance in the UI and fix them.
• Positive impact: to identify a big latency in one region which was just used a canary to observe its usage/behaviour first, and after a few hours we could see it was not going as expected.

Datadog, New Relic and Checkly

Datadog