Developers Can Leverage OpenTelemetry to Achieve Fuller Visibility

Michael Olechna
Guardsquare
Learn more about SmartBear

Observability is currently a hot topic. Businesses and consumers are increasingly relying on digital apps for everyday functions, which means every company needs a high performing app or website. When you take a minute to evaluate why, the numbers quickly make sense. In 2025, the number of mobile users worldwide is projected to reach 7.49 billion. And as digital adoption continues to grow, so does users' quality expectations. Each one of those users, including developers, is expecting a frictionless, high-quality experience. As end-user experiences become more connected with an organization's bottom line, a solution to catch performance hiccups becomes necessary. Hence the adoption of front-end observability through initiatives like digital experience monitoring. And who better to execute this initiative than the developers writing the code. But there's a problem with traditional observability tools tailored for DevOps, SRE, and IT teams. Developers need a tool that can be portable and vendor agnostic, given the advent of microservices. It may be clear an issue is occurring; what may not be clear is if it's part of a distributed system or the app itself. Enter OpenTelemetry, commonly referred to as OTel, an open-source framework that provides a standardized way of collecting and exporting telemetry data (logs, metrics, and traces) from cloud-native software. Prior to the onset of OpenTelemetry, there was a lack of standardization when collecting and instrumenting telemetry data. When it came to code instrumentation, there was significant variation. Due to this variation, the result was a lack of data portability and a burden on the developer to maintain large, complex instrumentation libraries. This doesn't just add significant time and effort on the developer's part. This directly impacts visibility into app performance, potentially leading to a negative end user experience. It also creates vendor lock-in and inefficiencies that can be costly for an organization, further affecting business revenue. As the market shifts toward developer-first observability, the need for a solution like OTel becomes readily apparent — explaining its rapid rise in popularity since its launch in 2019. OTel gave developers a way to ingest, view, and export telemetry data. The best part (or one of many)? It's vendor agnostic. This unified method of collecting data makes it easier for modern development teams to get a clearer, more complete picture of their apps' health and performance. The platform also provides a rich set of APIs and SDKs that are also vendor agnostic. With full control of their data, development teams can quickly instrument cloud-native apps and get started with ease. When drilling down into specific benefits, perhaps the most important feature is OTel's versatility. In addition to being vendor agnostic, the platform supports a wide range of vendors, both commercial and open source. This is key to developers being able to leverage their telemetry data long-term because they have the ability to take it with them. Should they choose to change vendors, it's as easy as exporting their OTel data to their new vendor. This eliminates the manual and time intensive process of data re-instrumentation. When discussing use cases for these benefits, three specific examples immediately come to light. The first is faster identification of performance bottlenecks. By examining telemetry data in OTel, teams can determine performance bottlenecks by tracking the time it takes to execute individual operations. Leveraging this information provides critical context to help solve application performance issues and optimize app performance. The second use case is troubleshooting problems. OTel provides a single source of truth for all telemetry data in a distributed system. Thus, development teams can track the flow of execution through their systems by examining OTel data. Developers can track down the root cause of the issue for faster resolution and ensure they are treating the cause, not a symptom. The third use case, data control, relates to one of the key benefits — OTel's versatility. OpenTelemetry is designed to work and integrate with various observability tools and platforms. This includes backends and popular tracing systems like Jaeger, as well as other metrics and logging solutions. Again, this puts data control back in the hands of developers. They can select the tools they are comfortable with or continue using what's already in their workflow, while maintaining a clear view of their app's telemetry data. By adopting OpenTelemetry, developers gain fully contextualized visibility into their distributed applications. In turn, they're able to identify performance bottlenecks faster, get down to the root cause to debug issues, optimize their resource utilization, and improve the overall reliability and user experience of their software systems.

Michael Olechna is Product Marketing Manager at Guardsquare

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Developers Can Leverage OpenTelemetry to Achieve Fuller Visibility

Michael Olechna
Guardsquare
Learn more about SmartBear

Observability is currently a hot topic. Businesses and consumers are increasingly relying on digital apps for everyday functions, which means every company needs a high performing app or website. When you take a minute to evaluate why, the numbers quickly make sense. In 2025, the number of mobile users worldwide is projected to reach 7.49 billion. And as digital adoption continues to grow, so does users' quality expectations. Each one of those users, including developers, is expecting a frictionless, high-quality experience. As end-user experiences become more connected with an organization's bottom line, a solution to catch performance hiccups becomes necessary. Hence the adoption of front-end observability through initiatives like digital experience monitoring. And who better to execute this initiative than the developers writing the code. But there's a problem with traditional observability tools tailored for DevOps, SRE, and IT teams. Developers need a tool that can be portable and vendor agnostic, given the advent of microservices. It may be clear an issue is occurring; what may not be clear is if it's part of a distributed system or the app itself. Enter OpenTelemetry, commonly referred to as OTel, an open-source framework that provides a standardized way of collecting and exporting telemetry data (logs, metrics, and traces) from cloud-native software. Prior to the onset of OpenTelemetry, there was a lack of standardization when collecting and instrumenting telemetry data. When it came to code instrumentation, there was significant variation. Due to this variation, the result was a lack of data portability and a burden on the developer to maintain large, complex instrumentation libraries. This doesn't just add significant time and effort on the developer's part. This directly impacts visibility into app performance, potentially leading to a negative end user experience. It also creates vendor lock-in and inefficiencies that can be costly for an organization, further affecting business revenue. As the market shifts toward developer-first observability, the need for a solution like OTel becomes readily apparent — explaining its rapid rise in popularity since its launch in 2019. OTel gave developers a way to ingest, view, and export telemetry data. The best part (or one of many)? It's vendor agnostic. This unified method of collecting data makes it easier for modern development teams to get a clearer, more complete picture of their apps' health and performance. The platform also provides a rich set of APIs and SDKs that are also vendor agnostic. With full control of their data, development teams can quickly instrument cloud-native apps and get started with ease. When drilling down into specific benefits, perhaps the most important feature is OTel's versatility. In addition to being vendor agnostic, the platform supports a wide range of vendors, both commercial and open source. This is key to developers being able to leverage their telemetry data long-term because they have the ability to take it with them. Should they choose to change vendors, it's as easy as exporting their OTel data to their new vendor. This eliminates the manual and time intensive process of data re-instrumentation. When discussing use cases for these benefits, three specific examples immediately come to light. The first is faster identification of performance bottlenecks. By examining telemetry data in OTel, teams can determine performance bottlenecks by tracking the time it takes to execute individual operations. Leveraging this information provides critical context to help solve application performance issues and optimize app performance. The second use case is troubleshooting problems. OTel provides a single source of truth for all telemetry data in a distributed system. Thus, development teams can track the flow of execution through their systems by examining OTel data. Developers can track down the root cause of the issue for faster resolution and ensure they are treating the cause, not a symptom. The third use case, data control, relates to one of the key benefits — OTel's versatility. OpenTelemetry is designed to work and integrate with various observability tools and platforms. This includes backends and popular tracing systems like Jaeger, as well as other metrics and logging solutions. Again, this puts data control back in the hands of developers. They can select the tools they are comfortable with or continue using what's already in their workflow, while maintaining a clear view of their app's telemetry data. By adopting OpenTelemetry, developers gain fully contextualized visibility into their distributed applications. In turn, they're able to identify performance bottlenecks faster, get down to the root cause to debug issues, optimize their resource utilization, and improve the overall reliability and user experience of their software systems.

Michael Olechna is Product Marketing Manager at Guardsquare

Hot Topics

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The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

Beyond Buy vs. Build: The New Playbook for Data Pipelines

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

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