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Datadog Products Available on AWS Australia and New Zealand

Datadog launched its full range of products and services on the Amazon Web Services’ (AWS) Asia-Pacific (Sydney) Region.

The launch adds to existing locations in North America, Asia, and Europe, strengthening Datadog’s comprehensive observability platform that enables customers to monitor their entire technology stack across their deployment environments.

The new local availability zone enables Datadog, its customers and partners to store and process data locally, enabling faster observability and in-region capacity to meet applicable Australian privacy, security and data storage requirements. This is crucial for an increasing number of organizations, and particularly those operating in regulated environments such as government, banking, healthcare and higher education.

“This milestone reinforces Datadog’s commitment to supporting the region’s advanced digital capabilities—especially the Australian Government’s ambition to make the country a leading digital economy,” said Yanbing Li, Chief Product Officer at Datadog. “With strong momentum across public and private sectors, our investment enhances trust in Datadog’s unified and cloud-agnostic observability and security platform, and positions us to meet the evolving needs of agencies and enterprises alike.”

“Australian organizations are on track to spend nearly A$26.6 billion on public cloud services alone in 2025. For organizations in highly regulated industries, it isn’t just the cloud provider that needs to have local data storage capacity – it should be all layers of the tech stack,” said Rob Thorne, Vice President for Asia-Pacific and Japan (APJ) at Datadog.

“This milestone reflects Datadog’s priority to support these investments. It’s the latest step in our expansion down under, and follows the continued addition of headcount to support our more than 1,100 A/NZ customers, as well as the recent appointments of Field CTO for APJ, Yadi Narayana, and Vice President of Commercial Sales for APJ, Adrian Towsey, to our leadership team,” said Thorne.

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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 ...

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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 ... 

Datadog Products Available on AWS Australia and New Zealand

Datadog launched its full range of products and services on the Amazon Web Services’ (AWS) Asia-Pacific (Sydney) Region.

The launch adds to existing locations in North America, Asia, and Europe, strengthening Datadog’s comprehensive observability platform that enables customers to monitor their entire technology stack across their deployment environments.

The new local availability zone enables Datadog, its customers and partners to store and process data locally, enabling faster observability and in-region capacity to meet applicable Australian privacy, security and data storage requirements. This is crucial for an increasing number of organizations, and particularly those operating in regulated environments such as government, banking, healthcare and higher education.

“This milestone reinforces Datadog’s commitment to supporting the region’s advanced digital capabilities—especially the Australian Government’s ambition to make the country a leading digital economy,” said Yanbing Li, Chief Product Officer at Datadog. “With strong momentum across public and private sectors, our investment enhances trust in Datadog’s unified and cloud-agnostic observability and security platform, and positions us to meet the evolving needs of agencies and enterprises alike.”

“Australian organizations are on track to spend nearly A$26.6 billion on public cloud services alone in 2025. For organizations in highly regulated industries, it isn’t just the cloud provider that needs to have local data storage capacity – it should be all layers of the tech stack,” said Rob Thorne, Vice President for Asia-Pacific and Japan (APJ) at Datadog.

“This milestone reflects Datadog’s priority to support these investments. It’s the latest step in our expansion down under, and follows the continued addition of headcount to support our more than 1,100 A/NZ customers, as well as the recent appointments of Field CTO for APJ, Yadi Narayana, and Vice President of Commercial Sales for APJ, Adrian Towsey, to our leadership team,” said Thorne.

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 ...