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A Guide to OpenTelemetry - Part 3: The Advantages

Pete Goldin
APMdigest

One of the reasons OpenTelemetry is becoming so popular is because of the many advantages. In A Guide to OpenTelemetry, APMdigest breaks these advantages down into two groups: the beneficial capabilities of OpenTelemetry and the results users can expect from OpenTelemetry. In Part 3, we cover the capabilities.

Start with: A Guide to OpenTelemetry - Part 1

Start with: A Guide to OpenTelemetry - Part 2: When Will OTel Be Ready?

Universal Observability Tool

"One specification to rule them all — Companies will be able to rely on OTel for all languages and types of telemetry (logs, metrics, traces, etc) rather than distribute these capabilities among several tools" says Michael Haberman, CTO and Co-Founder of Aspecto.

Standardized Instrumentation

"Working with distributed systems is confusing enough; we need to simplify it by standardizing on a consistent set of tools," explains Mike Loukides, VP of Emerging Tech Content at O'Reilly Media. "What happens if your IT group develops part of a product, but buys several important components from a vendor? You're going to have to debug and maintain the whole system. That's going to be a nightmare if the different components don't speak the same language when saving information about their activity."

"Opentelemetry is an instrumentation standard," says Pranay Prateek, Co-Founder of SigNoz. "You can use any backend and storage layer to store telemetry data, and any front end to visualize that data. So as long as these components support the OTLP format (OpenTelemetry's format), they can process and visualize OTel data."

Interoperability

"OpenTelemetry will be valuable for the same reason that other standards are: interoperability," says Loukides from O'Reilly. "It will make it easier for developers to write software that is observable by using a single standard API and being able to plug in standard libraries. It will make it easier for people responsible for operations to integrate with existing observability platforms. If the protocol that applications use to talk to observability platforms is standardized, operations staff can mix and match dashboards, debugging tools, automation tools (AIOps), and much more."

Automated Instrumentation

"Companies no longer need their developers to spend a lot of time and headache on manually instrumenting their stack," explains Torsten Volk, Managing Research Director, Containers, DevOps, Machine Learning and Artificial Intelligence, at Enterprise Management Associates (EMA). "Instead developers can augment the automatically instrumented app stack by adding telemetry variables to their own code to tie together application behavior and infrastructure performance. DevOps engineers and SREs automatically receive a more comprehensive and complete view of their app environment and its context. DevOps, Ops and dev all will benefit from the more consistent instrumentation through OpenTelemetry compared to manual instrumentation, as this consistency lowers the risk of blind spots within the observability dashboard."

"Instrumentation can now be shifted left by making auto instrumentation part of any type of artifact used throughout the DevOps process," he continues. "Container images, VMs, software libraries, machine learning models, and database can all come pre-instrumented to simplify the DevOps toolchain and lower the risk of critical parts of the stack flying 'under the radar' in terms of observability and visibility."

Future-Proof Instrumentation

"The main business benefit that we see from using OpenTelemetry is that it is future-proof," says Prateek from SigNoz. "OpenTelemetry is an open standard and open source implementation with contributors from companies like AWS, Microsoft, Splunk, etc. It provides instrumentation libraries in almost all major programming languages and covers most of the popular open source frameworks. If tomorrow your team decides to use a new open source library in the tech stack, you can have the peace of mind that OpenTelemetry will provide instrumentation for it."

"In a hyper-dynamic environment where services come and go, and instances can be scaled in a reactive fashion, the OpenTelemetry project aims to provide a single path for full stack visibility which is future proof and easy to apply," adds Cedric Ziel, Grafana Labs Senior Product Manager.

Cost-Effective Observability

OpenTelemetry makes observability more cost-effective in several ways.

First, it provides cost control because it is open source.

"Organizations had large opportunity-costs in the past when they switched observability providers that forced them to use proprietary SDKs and APIs," says Ziel from Grafana Labs. "Customers are demanding compatibility and a path with OpenTelemetry and are less likely to accept proprietary solutions than a few years ago."

"No vendor lock-in means more control over observability costs," Prateek from SigNoz elaborates. "The freedom to choose an observability vendor of your choice while having access to world-class instrumentation is a huge advantage to the business."

"OpenTelemetry can also help reduce the cost associated with ramping up your engineering team," he continues. "Using an open source standard helps engineering teams to create a knowledge base that is consistent and improves with time."

Second, OpenTelemetry reduces cost because it is easy to use and reduces development time.

"Standardizing generation and exporting signals provides consistency across the development organization and leads to less development cost/time," says Nitin Navare, CTO of LogicMonitor.

Go to: A Guide to OpenTelemetry - Part 4: The Results

Pete Goldin is Editor and Publisher of APMdigest

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A Guide to OpenTelemetry - Part 3: The Advantages

Pete Goldin
APMdigest

One of the reasons OpenTelemetry is becoming so popular is because of the many advantages. In A Guide to OpenTelemetry, APMdigest breaks these advantages down into two groups: the beneficial capabilities of OpenTelemetry and the results users can expect from OpenTelemetry. In Part 3, we cover the capabilities.

Start with: A Guide to OpenTelemetry - Part 1

Start with: A Guide to OpenTelemetry - Part 2: When Will OTel Be Ready?

Universal Observability Tool

"One specification to rule them all — Companies will be able to rely on OTel for all languages and types of telemetry (logs, metrics, traces, etc) rather than distribute these capabilities among several tools" says Michael Haberman, CTO and Co-Founder of Aspecto.

Standardized Instrumentation

"Working with distributed systems is confusing enough; we need to simplify it by standardizing on a consistent set of tools," explains Mike Loukides, VP of Emerging Tech Content at O'Reilly Media. "What happens if your IT group develops part of a product, but buys several important components from a vendor? You're going to have to debug and maintain the whole system. That's going to be a nightmare if the different components don't speak the same language when saving information about their activity."

"Opentelemetry is an instrumentation standard," says Pranay Prateek, Co-Founder of SigNoz. "You can use any backend and storage layer to store telemetry data, and any front end to visualize that data. So as long as these components support the OTLP format (OpenTelemetry's format), they can process and visualize OTel data."

Interoperability

"OpenTelemetry will be valuable for the same reason that other standards are: interoperability," says Loukides from O'Reilly. "It will make it easier for developers to write software that is observable by using a single standard API and being able to plug in standard libraries. It will make it easier for people responsible for operations to integrate with existing observability platforms. If the protocol that applications use to talk to observability platforms is standardized, operations staff can mix and match dashboards, debugging tools, automation tools (AIOps), and much more."

Automated Instrumentation

"Companies no longer need their developers to spend a lot of time and headache on manually instrumenting their stack," explains Torsten Volk, Managing Research Director, Containers, DevOps, Machine Learning and Artificial Intelligence, at Enterprise Management Associates (EMA). "Instead developers can augment the automatically instrumented app stack by adding telemetry variables to their own code to tie together application behavior and infrastructure performance. DevOps engineers and SREs automatically receive a more comprehensive and complete view of their app environment and its context. DevOps, Ops and dev all will benefit from the more consistent instrumentation through OpenTelemetry compared to manual instrumentation, as this consistency lowers the risk of blind spots within the observability dashboard."

"Instrumentation can now be shifted left by making auto instrumentation part of any type of artifact used throughout the DevOps process," he continues. "Container images, VMs, software libraries, machine learning models, and database can all come pre-instrumented to simplify the DevOps toolchain and lower the risk of critical parts of the stack flying 'under the radar' in terms of observability and visibility."

Future-Proof Instrumentation

"The main business benefit that we see from using OpenTelemetry is that it is future-proof," says Prateek from SigNoz. "OpenTelemetry is an open standard and open source implementation with contributors from companies like AWS, Microsoft, Splunk, etc. It provides instrumentation libraries in almost all major programming languages and covers most of the popular open source frameworks. If tomorrow your team decides to use a new open source library in the tech stack, you can have the peace of mind that OpenTelemetry will provide instrumentation for it."

"In a hyper-dynamic environment where services come and go, and instances can be scaled in a reactive fashion, the OpenTelemetry project aims to provide a single path for full stack visibility which is future proof and easy to apply," adds Cedric Ziel, Grafana Labs Senior Product Manager.

Cost-Effective Observability

OpenTelemetry makes observability more cost-effective in several ways.

First, it provides cost control because it is open source.

"Organizations had large opportunity-costs in the past when they switched observability providers that forced them to use proprietary SDKs and APIs," says Ziel from Grafana Labs. "Customers are demanding compatibility and a path with OpenTelemetry and are less likely to accept proprietary solutions than a few years ago."

"No vendor lock-in means more control over observability costs," Prateek from SigNoz elaborates. "The freedom to choose an observability vendor of your choice while having access to world-class instrumentation is a huge advantage to the business."

"OpenTelemetry can also help reduce the cost associated with ramping up your engineering team," he continues. "Using an open source standard helps engineering teams to create a knowledge base that is consistent and improves with time."

Second, OpenTelemetry reduces cost because it is easy to use and reduces development time.

"Standardizing generation and exporting signals provides consistency across the development organization and leads to less development cost/time," says Nitin Navare, CTO of LogicMonitor.

Go to: A Guide to OpenTelemetry - Part 4: The Results

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...