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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.

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For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...

FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...

Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...

While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...

A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...