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Developers Can Leverage OpenTelemetry to Achieve Fuller Visibility

Michael Olechna
Guardsquare

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

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

The Latest

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 4 covers negative impacts of AI ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 3 covers barriers and challenges for AI ...