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Catchpoint Tracing Offers Deep Visibility for Critical Cloud-Centric Applications from the User Experience Across the Internet to the Code

New capability provides DevOps and IT Operations teams the unique ability to see code tracing data in context of anything in the Internet Stack that may impact the user experience journey

Catchpoint unveiled Catchpoint Tracing, a capability allowing DevOps and IT Operations teams to seamlessly integrate traces into their other Internet Stack observability data, enabling them to make faster decisions in an ever-changing, increasingly complex and fragile Internet ecosystem. 

Catchpoint Tracing - coupled with the insights derived from IPM - enables full end-to-end visibility. Teams can visualize request journeys through backend application components and gain code-level insights into cloud-first applications. Unlike traditional Application Performance Monitoring (APM) tools that monitor from the inside out, Catchpoint’s Internet Performance Monitoring (IPM) platform with Tracing takes an outside-in perspective of digital experience, offering a holistic view that extends from the Internet to the application and its dependencies (access a full comparison here). This unique Internet-centric approach enables operations teams to understand the entire end-to-end path of the user journey and diagnose application errors or performance latency in seconds. 

Key benefits of Catchpoint Tracing include: ■ Diagnosis of failed requests by tracing across the application, through the architecture, to the component level. ■ Troubleshooting of application performance for upstream or downstream dependencies (whether native or not). ■ Balance innovation velocity with reliability to meet the twin needs of developers who are focused on product innovation and ITOps teams striving for system resilience. ■ Optimize costs by monitoring only those components of the Internet and Application Stacks that matter. ■ Native support for OpenTelemetry to easily integrate with other observability frameworks. ■ Simplicity in deployment with automated instrumentation and a simple pricing model. 

“Legacy APM vendors provide some cloud synthetic testing capabilities in conjunction with Tracing, but they all lack the ability to visualize the customer journey and identify issues across the Internet Stack,” said Dritan Suljoti, Chief Product and Technology Officer and co-founder, Catchpoint. “Catchpoint Tracing with OpenTelemetry runs within the same Internet Performance Monitoring Platform as our Synthetics, RUM and BGP products and can monitor across the Internet Stack from the end user’s perspective globally. We then automatically correlate any issues so that IT teams can rapidly troubleshoot and identify the root cause faster, accelerating MTTR.” 

Catchpoint Tracing allows DevOps and IT Operations teams to address the complexities of modern application delivery and offer exceptional digital experiences to enterprise customers. Tracing safeguards business revenue by helping these teams gain a 360-degree view into how code impacts user experience across their entire journey. Thus, enabling them to deliver fast, resilient experiences, irrespective of size, complexity and distributed hosting environments. 

Catchpoint Tracing supports OpenTelemetry (OTel) to easily enable integration with other Observability frameworks. By offering the ability to uncouple your tracing telemetry, Catchpoint joins OTel on our shared vision for effective observability by enabling easy interoperation with other open source software projects in the telemetry and observability ecosystem. Our tracing data seamlessly integrates with your Internet Stack telemetry. 

Additional capabilities include: ■ Automated tracing with no code change ■ Support for cloud providers (e.g., AWS, GCP, and Azure) ■ Support for application platforms (e.g., AWS ECS, EKS, Lambda, and Kubernetes) ■ Support for multiple runtimes (e.g., Java, Python, Node.JS, .NET, and Go + OpenTelemetry) ■ Trace requests down to event-driven flows (e.g., Kafka, AWS SQS, AWS Kinesis) ■ Seamless integration with Internet Stack telemetry (e.g., Synthetics and Internet Synthetics) Catchpoint Tracing is now available to all customers.

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Catchpoint Tracing Offers Deep Visibility for Critical Cloud-Centric Applications from the User Experience Across the Internet to the Code

New capability provides DevOps and IT Operations teams the unique ability to see code tracing data in context of anything in the Internet Stack that may impact the user experience journey

Catchpoint unveiled Catchpoint Tracing, a capability allowing DevOps and IT Operations teams to seamlessly integrate traces into their other Internet Stack observability data, enabling them to make faster decisions in an ever-changing, increasingly complex and fragile Internet ecosystem. 

Catchpoint Tracing - coupled with the insights derived from IPM - enables full end-to-end visibility. Teams can visualize request journeys through backend application components and gain code-level insights into cloud-first applications. Unlike traditional Application Performance Monitoring (APM) tools that monitor from the inside out, Catchpoint’s Internet Performance Monitoring (IPM) platform with Tracing takes an outside-in perspective of digital experience, offering a holistic view that extends from the Internet to the application and its dependencies (access a full comparison here). This unique Internet-centric approach enables operations teams to understand the entire end-to-end path of the user journey and diagnose application errors or performance latency in seconds. 

Key benefits of Catchpoint Tracing include: ■ Diagnosis of failed requests by tracing across the application, through the architecture, to the component level. ■ Troubleshooting of application performance for upstream or downstream dependencies (whether native or not). ■ Balance innovation velocity with reliability to meet the twin needs of developers who are focused on product innovation and ITOps teams striving for system resilience. ■ Optimize costs by monitoring only those components of the Internet and Application Stacks that matter. ■ Native support for OpenTelemetry to easily integrate with other observability frameworks. ■ Simplicity in deployment with automated instrumentation and a simple pricing model. 

“Legacy APM vendors provide some cloud synthetic testing capabilities in conjunction with Tracing, but they all lack the ability to visualize the customer journey and identify issues across the Internet Stack,” said Dritan Suljoti, Chief Product and Technology Officer and co-founder, Catchpoint. “Catchpoint Tracing with OpenTelemetry runs within the same Internet Performance Monitoring Platform as our Synthetics, RUM and BGP products and can monitor across the Internet Stack from the end user’s perspective globally. We then automatically correlate any issues so that IT teams can rapidly troubleshoot and identify the root cause faster, accelerating MTTR.” 

Catchpoint Tracing allows DevOps and IT Operations teams to address the complexities of modern application delivery and offer exceptional digital experiences to enterprise customers. Tracing safeguards business revenue by helping these teams gain a 360-degree view into how code impacts user experience across their entire journey. Thus, enabling them to deliver fast, resilient experiences, irrespective of size, complexity and distributed hosting environments. 

Catchpoint Tracing supports OpenTelemetry (OTel) to easily enable integration with other Observability frameworks. By offering the ability to uncouple your tracing telemetry, Catchpoint joins OTel on our shared vision for effective observability by enabling easy interoperation with other open source software projects in the telemetry and observability ecosystem. Our tracing data seamlessly integrates with your Internet Stack telemetry. 

Additional capabilities include: ■ Automated tracing with no code change ■ Support for cloud providers (e.g., AWS, GCP, and Azure) ■ Support for application platforms (e.g., AWS ECS, EKS, Lambda, and Kubernetes) ■ Support for multiple runtimes (e.g., Java, Python, Node.JS, .NET, and Go + OpenTelemetry) ■ Trace requests down to event-driven flows (e.g., Kafka, AWS SQS, AWS Kinesis) ■ Seamless integration with Internet Stack telemetry (e.g., Synthetics and Internet Synthetics) Catchpoint Tracing is now available to all customers.

The Latest

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...