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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

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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.

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

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.