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Splunk Observability Cloud Available on AWS in Australia and New Zealand

Splunk announced Splunk Observability Cloud is now available in Australia and New Zealand (ANZ) via the Amazon Web Services (AWS) Asia Pacific (Sydney) Region.

With the Splunk Observability Cloud, DevOps, IT Operations and software development teams can answer any question about the performance and health of their applications, services and infrastructure with metrics, traces and logs - all data collected in real time, without sampling and at any scale. By providing services on AWS in ANZ, enterprises now have options to retain data closer to home with low latency, which is an important requirement for observability. This also helps Australian organizations meet compliance requirements associated with data residency.

“... Splunk Observability Cloud provides DevOps, IT Operations and software development teams with all the capabilities they need for observability in one integrated interface, enabling organizations to monitor and analyse their applications and infrastructure in real time." said Mark Troselj, Group VP of Australia and New Zealand, Splunk.

“AWS supports hundreds of thousands of organisations across Australia and New Zealand to continuously innovate, succeed, and grow globally,” said Sumal Karunanayake, Head of Partner Success, Amazon Web Services, Australia and New Zealand. “With Splunk Observability Cloud on AWS, local enterprises who are increasingly focused on user experience, app modernisation, and innovation, can quickly gain visibility of their technology infrastructure and IT applications, and accelerate improvements that support digital transformation – all with the data staying local
to Australia and New Zealand.”

The Splunk Observability Cloud brings together the world’s best-in-class solutions for infrastructure monitoring, application performance monitoring, real user monitoring, synthetic monitoring, log investigation and incident response. Backed by Splunk’s NoSample full-fidelity data ingestion, real-time streaming analytics and massive scalability, Splunk Observability Cloud delivers unprecedented capabilities for monitoring, troubleshooting and resolution of business-critical incidents. The full observability portfolio from Splunk includes Splunk Infrastructure Monitoring, Splunk APM, Splunk RUM, Splunk Synthetic Monitoring, Splunk Log Observer, Splunk IT Service Intelligence and Splunk On-Call.

Splunk announced the following new features to its unified security and observability platform:

- Splunk Log Observer Connect allows customers to visualize all their data in one place by combining the power of Splunk Cloud Platform, Splunk Enterprise and Splunk Observability Cloud, enabling site reliability engineers and DevOps engineers to access their metrics, traces, and Splunk Cloud or Splunk Enterprise logs in a single interface for faster, in-context debugging.

- Splunk Incident Intelligence, now in preview, helps DevOps teams investigate incidents and take action to ensure better system resilience by providing event correlation, incident response and on-call routing, collaboration, and automation - all within a unified workflow.

The Splunk Observability Cloud is optimized and designed to consume and manage OpenTelemetry data at scale enabling customers to unlock their data through open source standards. Splunk Observability Cloud is OpenTelemetry-native allowing customers to unify data ingestion without vendor lock-in and reduce resource consumption with the lightweight, open-source OpenTelemetry instrumentation.

As data volume and organizational complexities increase, Splunk wants to keep pricing simple and this bundle is designed to do that. With the Splunk Observability Cloud, Splunk is integrating these capabilities under one clear host-based pricing metric directly tied to the value DevOps, IT Operations and software development teams may gain.

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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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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.

Splunk Observability Cloud Available on AWS in Australia and New Zealand

Splunk announced Splunk Observability Cloud is now available in Australia and New Zealand (ANZ) via the Amazon Web Services (AWS) Asia Pacific (Sydney) Region.

With the Splunk Observability Cloud, DevOps, IT Operations and software development teams can answer any question about the performance and health of their applications, services and infrastructure with metrics, traces and logs - all data collected in real time, without sampling and at any scale. By providing services on AWS in ANZ, enterprises now have options to retain data closer to home with low latency, which is an important requirement for observability. This also helps Australian organizations meet compliance requirements associated with data residency.

“... Splunk Observability Cloud provides DevOps, IT Operations and software development teams with all the capabilities they need for observability in one integrated interface, enabling organizations to monitor and analyse their applications and infrastructure in real time." said Mark Troselj, Group VP of Australia and New Zealand, Splunk.

“AWS supports hundreds of thousands of organisations across Australia and New Zealand to continuously innovate, succeed, and grow globally,” said Sumal Karunanayake, Head of Partner Success, Amazon Web Services, Australia and New Zealand. “With Splunk Observability Cloud on AWS, local enterprises who are increasingly focused on user experience, app modernisation, and innovation, can quickly gain visibility of their technology infrastructure and IT applications, and accelerate improvements that support digital transformation – all with the data staying local
to Australia and New Zealand.”

The Splunk Observability Cloud brings together the world’s best-in-class solutions for infrastructure monitoring, application performance monitoring, real user monitoring, synthetic monitoring, log investigation and incident response. Backed by Splunk’s NoSample full-fidelity data ingestion, real-time streaming analytics and massive scalability, Splunk Observability Cloud delivers unprecedented capabilities for monitoring, troubleshooting and resolution of business-critical incidents. The full observability portfolio from Splunk includes Splunk Infrastructure Monitoring, Splunk APM, Splunk RUM, Splunk Synthetic Monitoring, Splunk Log Observer, Splunk IT Service Intelligence and Splunk On-Call.

Splunk announced the following new features to its unified security and observability platform:

- Splunk Log Observer Connect allows customers to visualize all their data in one place by combining the power of Splunk Cloud Platform, Splunk Enterprise and Splunk Observability Cloud, enabling site reliability engineers and DevOps engineers to access their metrics, traces, and Splunk Cloud or Splunk Enterprise logs in a single interface for faster, in-context debugging.

- Splunk Incident Intelligence, now in preview, helps DevOps teams investigate incidents and take action to ensure better system resilience by providing event correlation, incident response and on-call routing, collaboration, and automation - all within a unified workflow.

The Splunk Observability Cloud is optimized and designed to consume and manage OpenTelemetry data at scale enabling customers to unlock their data through open source standards. Splunk Observability Cloud is OpenTelemetry-native allowing customers to unify data ingestion without vendor lock-in and reduce resource consumption with the lightweight, open-source OpenTelemetry instrumentation.

As data volume and organizational complexities increase, Splunk wants to keep pricing simple and this bundle is designed to do that. With the Splunk Observability Cloud, Splunk is integrating these capabilities under one clear host-based pricing metric directly tied to the value DevOps, IT Operations and software development teams may gain.

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.