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Splunk Announces Improved OpenTelemetry Experience

Splunk announced new innovations to deliver a seamless OpenTelemetry experience that offers organizations flexibility, customization, and freedom from vendor lock-in with no manual setup. 

With the introduction of these new capabilities, Splunk provides a more intuitive, simplified OpenTelemetry experience – ensuring organizations can seamlessly integrate open-source observability into their software delivery and build a leading observability practice.

Recognizing the great advantages of OpenTelemetry, Splunk made it the foundation of its Splunk Observability Cloud solution and continued to innovate to ensure organizations benefit from the framework's vendor-neutral telemetry with unmatched ease of use. These new solutions include Service Inventory, enhanced capabilities for Kubernetes troubleshooting, and expanded support for automatic setup of OpenTelemetry instrumentation for applications.

Service Inventory addresses the operational challenges of large-scale observability building on infrastructure inventory, automatic discovery and automatic configuration. This new capability:

  • Provides end-to-end visibility – Automatically detects all third-party applications, like databases and message queues.
  • Guides users through configuration – Provides step-by-step recommendations for seamless OpenTelemetry setup.
  • Identifies and resolves visibility gaps – Highlights missing instrumentation, allowing enterprises to proactively address blind spots across their infrastructure.  

"Splunk customers benefit from OpenTelemetry's full power, with a frictionless experience that ensures complete visibility, so they can attain flexibility and ownership of their data," said Morgan McLean, OpenTelemetry Co-founder and Senior Director of Product Management at Splunk, a Cisco company. "OpenTelemetry should be effortless to use, and Splunk is committed to making that a reality with the introduction of Service Inventory and expanded automation. These enhancements give customers the power to build a leading observability practice with the ability to instrument and monitor their environments without the complexity of manual configuration."

Splunk enhanced its Kubernetes monitoring and troubleshooting capabilities to further enrich visibility and empower teams to quickly detect and resolve issues within Kubernetes clusters, reducing downtime and improving performance.

Further strengthening Splunk's OpenTelemetry offering, the company is rolling out OpenTelemetry Python 2.0 and Node.js 3.0, delivering greater flexibility and improved performance for cloud-native applications.

"OpenTelemetry is rapidly becoming the industry standard for building an effective observability practice, but complex instrumentation remains a significant barrier. Splunk's latest innovations have the potential to make its adoption more seamless than ever," said Archana Venkatraman, Senior Research Director, Cloud Data Management, IDC Europe. "Cloud-native and Kubernetes strategies are becoming mainstream, requiring enterprise-grade management. Amid this, by automating instrumentation and deepening Kubernetes troubleshooting, Splunk continues to make OpenTelemetry a practical and powerful solution for digital enterprises."

As part of Cisco, Splunk continues to champion OpenTelemetry adoption. Cisco ThousandEyes and Splunk AppDynamics natively support OpenTelemetry, enabling a seamless observability experience across the entire IT stack. As the first assurance solution to support OpenTelemetry, Cisco ThousandEyes allows customers to infer correlations between digital experience health and observability metrics for end-to-end visibility that helps solve problems quicker, and get services restored and running faster. Splunk AppDynamics provides an OpenTelemetry-compatible backend to ingest trace data using OpenTelemetry components and leverages Splunk AppDynamics agents to produce OpenTelemetry data for easy consumption by Splunk Observability Cloud.

Service Inventory is available globally for all Splunk Observability Cloud customers.

Kubernetes troubleshooting enhancements and expanded OpenTelemetry language support are available globally.

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Splunk Announces Improved OpenTelemetry Experience

Splunk announced new innovations to deliver a seamless OpenTelemetry experience that offers organizations flexibility, customization, and freedom from vendor lock-in with no manual setup. 

With the introduction of these new capabilities, Splunk provides a more intuitive, simplified OpenTelemetry experience – ensuring organizations can seamlessly integrate open-source observability into their software delivery and build a leading observability practice.

Recognizing the great advantages of OpenTelemetry, Splunk made it the foundation of its Splunk Observability Cloud solution and continued to innovate to ensure organizations benefit from the framework's vendor-neutral telemetry with unmatched ease of use. These new solutions include Service Inventory, enhanced capabilities for Kubernetes troubleshooting, and expanded support for automatic setup of OpenTelemetry instrumentation for applications.

Service Inventory addresses the operational challenges of large-scale observability building on infrastructure inventory, automatic discovery and automatic configuration. This new capability:

  • Provides end-to-end visibility – Automatically detects all third-party applications, like databases and message queues.
  • Guides users through configuration – Provides step-by-step recommendations for seamless OpenTelemetry setup.
  • Identifies and resolves visibility gaps – Highlights missing instrumentation, allowing enterprises to proactively address blind spots across their infrastructure.  

"Splunk customers benefit from OpenTelemetry's full power, with a frictionless experience that ensures complete visibility, so they can attain flexibility and ownership of their data," said Morgan McLean, OpenTelemetry Co-founder and Senior Director of Product Management at Splunk, a Cisco company. "OpenTelemetry should be effortless to use, and Splunk is committed to making that a reality with the introduction of Service Inventory and expanded automation. These enhancements give customers the power to build a leading observability practice with the ability to instrument and monitor their environments without the complexity of manual configuration."

Splunk enhanced its Kubernetes monitoring and troubleshooting capabilities to further enrich visibility and empower teams to quickly detect and resolve issues within Kubernetes clusters, reducing downtime and improving performance.

Further strengthening Splunk's OpenTelemetry offering, the company is rolling out OpenTelemetry Python 2.0 and Node.js 3.0, delivering greater flexibility and improved performance for cloud-native applications.

"OpenTelemetry is rapidly becoming the industry standard for building an effective observability practice, but complex instrumentation remains a significant barrier. Splunk's latest innovations have the potential to make its adoption more seamless than ever," said Archana Venkatraman, Senior Research Director, Cloud Data Management, IDC Europe. "Cloud-native and Kubernetes strategies are becoming mainstream, requiring enterprise-grade management. Amid this, by automating instrumentation and deepening Kubernetes troubleshooting, Splunk continues to make OpenTelemetry a practical and powerful solution for digital enterprises."

As part of Cisco, Splunk continues to champion OpenTelemetry adoption. Cisco ThousandEyes and Splunk AppDynamics natively support OpenTelemetry, enabling a seamless observability experience across the entire IT stack. As the first assurance solution to support OpenTelemetry, Cisco ThousandEyes allows customers to infer correlations between digital experience health and observability metrics for end-to-end visibility that helps solve problems quicker, and get services restored and running faster. Splunk AppDynamics provides an OpenTelemetry-compatible backend to ingest trace data using OpenTelemetry components and leverages Splunk AppDynamics agents to produce OpenTelemetry data for easy consumption by Splunk Observability Cloud.

Service Inventory is available globally for all Splunk Observability Cloud customers.

Kubernetes troubleshooting enhancements and expanded OpenTelemetry language support are available globally.

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...