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Kentik Market Intelligence Launched

Kentik announced the launch of Kentik Market Intelligence (KMI), a new product that gives internet service providers (ISPs) and digital businesses a way to navigate and interpret the global internet ecosystem.

With KMI, users gain competitive intelligence to benchmark how their network stacks up against others, establish cost-reducing peering relationships, understand the most widely connected providers in different geographies, and prospect for new sales opportunities.

“The launch of Kentik Market Intelligence comes from popular demand. For too long, ISPs have relied on a mix of oral tradition and the manual process of crunching vast amounts of routing-table data to try to understand how networks connect to each other,” said Avi Freedman, Co-founder and CEO of Kentik.

The benefits of KMI for engineering, marketing and sales teams include:

- Benchmarking against competitors and the internet: Monitor how competitors’ rankings are evolving. See which service providers connect to the most customer networks in any market. Get alerts when there are changes to relationships.

- Cost-reducing, data-driven peering and interconnection decisions: Learn about customers, providers and peer relationships of any autonomous system (AS) in any country to support network expansion and interconnection.

- Prospecting for new sales opportunities: Identify competitors’ customers in any market, including whether or not they are critically dependent on a single upstream provider.

- Market expansion: Understand the reach and relative market share of each autonomous system number (ASN) across geographies of interest. Market network connectivity based on objective ranking data in a geographical market.

KMI processes hundreds of millions of BGP routing announcements every day and classifies AS relationships into peering and transit. As a result, KMI users can identify the relative size of providers, peers and customers for any AS in any geography. The information is presented in easy to interpret rankings and charts.

Some of the top networks and internet-traffic carriers across various geographies include: Arelion (formerly Telia Carrier), Cogent, GTT Communications, Hurricane Electric, Lumen, NTT America, PCCW Global, Tata Communications, Telecom Italia Sparkle, and Zayo.

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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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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 ...

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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.

Kentik Market Intelligence Launched

Kentik announced the launch of Kentik Market Intelligence (KMI), a new product that gives internet service providers (ISPs) and digital businesses a way to navigate and interpret the global internet ecosystem.

With KMI, users gain competitive intelligence to benchmark how their network stacks up against others, establish cost-reducing peering relationships, understand the most widely connected providers in different geographies, and prospect for new sales opportunities.

“The launch of Kentik Market Intelligence comes from popular demand. For too long, ISPs have relied on a mix of oral tradition and the manual process of crunching vast amounts of routing-table data to try to understand how networks connect to each other,” said Avi Freedman, Co-founder and CEO of Kentik.

The benefits of KMI for engineering, marketing and sales teams include:

- Benchmarking against competitors and the internet: Monitor how competitors’ rankings are evolving. See which service providers connect to the most customer networks in any market. Get alerts when there are changes to relationships.

- Cost-reducing, data-driven peering and interconnection decisions: Learn about customers, providers and peer relationships of any autonomous system (AS) in any country to support network expansion and interconnection.

- Prospecting for new sales opportunities: Identify competitors’ customers in any market, including whether or not they are critically dependent on a single upstream provider.

- Market expansion: Understand the reach and relative market share of each autonomous system number (ASN) across geographies of interest. Market network connectivity based on objective ranking data in a geographical market.

KMI processes hundreds of millions of BGP routing announcements every day and classifies AS relationships into peering and transit. As a result, KMI users can identify the relative size of providers, peers and customers for any AS in any geography. The information is presented in easy to interpret rankings and charts.

Some of the top networks and internet-traffic carriers across various geographies include: Arelion (formerly Telia Carrier), Cogent, GTT Communications, Hurricane Electric, Lumen, NTT America, PCCW Global, Tata Communications, Telecom Italia Sparkle, and Zayo.

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.