Skip to main content

New Versions of Splunk IT Service Intelligence and Splunk App for Infrastructure Released

Splunk announced enhancements to Splunk’s modern portfolio for IT Operations and to its portfolio for real-time observability for cloud infrastructures and microservices.

Enhancements to IT Operations include new versions of Splunk IT Service Intelligence (ITSI), Splunk App for Infrastructure (SAI), and integrations with Splunk VictorOps and Splunk Phantom.

“For organizations to succeed in today’s data-driven world, the IT department must function at the forefront of innovation,” said Rick Fitz, SVP and GM of IT Markets, Splunk. “With Splunk’s modern IT operations solutions, organizations can manage emerging complexities, and drive monitoring, collaboration and automation to solve long-standing IT problems while also tackling new challenges presented by digital transformation.”

Powered by artificial intelligence (AI) and machine learning (ML) capabilities, the IT operations portfolio enables new, fast ways of working that take advantage of an organization’s data. Splunk’s IT solutions help enable any organization make the shift from traditional to modern IT Operations, and help customers turn data into doing by delivering key services and valuable business outcomes.

The new version of Splunk IT Service Intelligence 4.4 (ITSI) gives everyone from administrators to the CIO, the same capability to monitor, investigate, and act in order to work faster and better together. Organizations that are in the cloud, on-premises or hybrid can use Splunk ITSI to get a unified view across organizational silos, and predict and prevent problems in order to deliver exceptional customer experiences.

Packaged with Splunk ITSI, Splunk App for Infrastructure 2.0 (SAI) enhancements include VMware vSphere Monitoring, multi-cloud monitoring (beta) and enhanced monitoring for Windows, Unix and Linux, providing customers with monitoring. troubleshooting and alerting across both physical and virtual environments.

Additionally, enhanced integrations and new ML-capabilities from Splunk VictorOps intelligently routes alerts to the right on-call teams for even faster problem resolution, enhanced cross-team collaboration, and seamlessly integrates with the Splunk Data-to-Everything Platform bridging IT Operations and observability.

With the acquisitions of SignalFx, a leader in real-time observability for cloud infrastructure and microservices, and Omnition, an innovator in open-source distributed tracing, Splunk provides a best-in-class portfolio for real-time observability of cloud-native environments. Splunk’s observability portfolio enables DevOps teams to process metrics, traces and logs with AI-driven analytics that enable deeper insights into critical systems in seconds.

Splunk is announcing:

- Integration between SignalFx and Splunk Cloud. With built-in deep linking capabilities from SignalFx to Splunk Cloud, DevOps and observability teams can seamlessly go from problem detection to root cause by leveraging metrics, traces and logs without context switching.

- Integration of SignalFx and VictorOps to reduce mean time to detect and streamline remediation. With real-time alerts from SignalFx and ML-driven Suggested Responders from VictorOps, problems are automatically routed to the right on-call teams based on previous similar incidents.

- Splunk Investigate, a collaborative, cloud-native solution for investigation across multiple data sources and with reliable scalability and zero administration.

“As organizations evolve, they move farther away from a manufacturing model of specialization, and silos will be broken down, particularly between DevOps and IT Ops. Teams will have to get better at reacting to speed and complexity,” said Fitz. “We are excited to deliver innovative, data-driven solutions that provide real-time insights into our customers’ entire technology stack and application lifecycle, for every kind of IT organization, anywhere in their journey of transformation.”

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

New Versions of Splunk IT Service Intelligence and Splunk App for Infrastructure Released

Splunk announced enhancements to Splunk’s modern portfolio for IT Operations and to its portfolio for real-time observability for cloud infrastructures and microservices.

Enhancements to IT Operations include new versions of Splunk IT Service Intelligence (ITSI), Splunk App for Infrastructure (SAI), and integrations with Splunk VictorOps and Splunk Phantom.

“For organizations to succeed in today’s data-driven world, the IT department must function at the forefront of innovation,” said Rick Fitz, SVP and GM of IT Markets, Splunk. “With Splunk’s modern IT operations solutions, organizations can manage emerging complexities, and drive monitoring, collaboration and automation to solve long-standing IT problems while also tackling new challenges presented by digital transformation.”

Powered by artificial intelligence (AI) and machine learning (ML) capabilities, the IT operations portfolio enables new, fast ways of working that take advantage of an organization’s data. Splunk’s IT solutions help enable any organization make the shift from traditional to modern IT Operations, and help customers turn data into doing by delivering key services and valuable business outcomes.

The new version of Splunk IT Service Intelligence 4.4 (ITSI) gives everyone from administrators to the CIO, the same capability to monitor, investigate, and act in order to work faster and better together. Organizations that are in the cloud, on-premises or hybrid can use Splunk ITSI to get a unified view across organizational silos, and predict and prevent problems in order to deliver exceptional customer experiences.

Packaged with Splunk ITSI, Splunk App for Infrastructure 2.0 (SAI) enhancements include VMware vSphere Monitoring, multi-cloud monitoring (beta) and enhanced monitoring for Windows, Unix and Linux, providing customers with monitoring. troubleshooting and alerting across both physical and virtual environments.

Additionally, enhanced integrations and new ML-capabilities from Splunk VictorOps intelligently routes alerts to the right on-call teams for even faster problem resolution, enhanced cross-team collaboration, and seamlessly integrates with the Splunk Data-to-Everything Platform bridging IT Operations and observability.

With the acquisitions of SignalFx, a leader in real-time observability for cloud infrastructure and microservices, and Omnition, an innovator in open-source distributed tracing, Splunk provides a best-in-class portfolio for real-time observability of cloud-native environments. Splunk’s observability portfolio enables DevOps teams to process metrics, traces and logs with AI-driven analytics that enable deeper insights into critical systems in seconds.

Splunk is announcing:

- Integration between SignalFx and Splunk Cloud. With built-in deep linking capabilities from SignalFx to Splunk Cloud, DevOps and observability teams can seamlessly go from problem detection to root cause by leveraging metrics, traces and logs without context switching.

- Integration of SignalFx and VictorOps to reduce mean time to detect and streamline remediation. With real-time alerts from SignalFx and ML-driven Suggested Responders from VictorOps, problems are automatically routed to the right on-call teams based on previous similar incidents.

- Splunk Investigate, a collaborative, cloud-native solution for investigation across multiple data sources and with reliable scalability and zero administration.

“As organizations evolve, they move farther away from a manufacturing model of specialization, and silos will be broken down, particularly between DevOps and IT Ops. Teams will have to get better at reacting to speed and complexity,” said Fitz. “We are excited to deliver innovative, data-driven solutions that provide real-time insights into our customers’ entire technology stack and application lifecycle, for every kind of IT organization, anywhere in their journey of transformation.”

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...