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Kentik Launches The Cloud Latency Map

Kentik launched The Cloud Latency Map, a free public tool that allows anyone to explore the latencies measured between 100 different cloud regions located around the world.

For the first time, users can identify recent changes in latencies globally between various public clouds and data center regions.

“The Cloud Latency Map benefits anyone trying to determine if there is a connectivity issue impacting the latencies to particular clouds and cloud regions,” said Doug Madory, Director of Internet Analysis at Kentik and the creator of The Cloud Latency Map. “And since the public clouds rely on the same physical infrastructure as the rest of the global Internet, the map can often pick up on the latency impacts of a variety of failures of core infrastructure, such as the loss of a major submarine cable.”

The Cloud Latency Map uses live measurement data generated by a full mesh of measurements between over 100 cloud agents hosted by Amazon Web Services (AWS), Microsoft Azure, Google Cloud (GCP), Oracle Cloud Infrastructure (OCI), and IBM. As an agnostic third party, Kentik is in a unique position to publish latency data across all major public clouds that has never been continuously available before. This newfound data can help digital businesses optimize their multi-cloud networks and identify impacts to cloud connectivity.

Kentik Cloud is also available for enterprises that want to go beyond the broad insights of The Cloud Latency Map. Kentik Cloud is a paid platform for enterprises with complex networks that need a best-in-class network observability platform with sophisticated insights into cloud performance. By connecting directly to an enterprise’s specific cloud setup, network teams are able to further optimize cost, performance, and security.

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Kentik Launches The Cloud Latency Map

Kentik launched The Cloud Latency Map, a free public tool that allows anyone to explore the latencies measured between 100 different cloud regions located around the world.

For the first time, users can identify recent changes in latencies globally between various public clouds and data center regions.

“The Cloud Latency Map benefits anyone trying to determine if there is a connectivity issue impacting the latencies to particular clouds and cloud regions,” said Doug Madory, Director of Internet Analysis at Kentik and the creator of The Cloud Latency Map. “And since the public clouds rely on the same physical infrastructure as the rest of the global Internet, the map can often pick up on the latency impacts of a variety of failures of core infrastructure, such as the loss of a major submarine cable.”

The Cloud Latency Map uses live measurement data generated by a full mesh of measurements between over 100 cloud agents hosted by Amazon Web Services (AWS), Microsoft Azure, Google Cloud (GCP), Oracle Cloud Infrastructure (OCI), and IBM. As an agnostic third party, Kentik is in a unique position to publish latency data across all major public clouds that has never been continuously available before. This newfound data can help digital businesses optimize their multi-cloud networks and identify impacts to cloud connectivity.

Kentik Cloud is also available for enterprises that want to go beyond the broad insights of The Cloud Latency Map. Kentik Cloud is a paid platform for enterprises with complex networks that need a best-in-class network observability platform with sophisticated insights into cloud performance. By connecting directly to an enterprise’s specific cloud setup, network teams are able to further optimize cost, performance, and security.

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 ...