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Kentik Introduces SaaS BGP Peering Analysis Solution

Kentik announced a major expansion of Kentik Detect, its flagship solution for cloud-based network visibility and analytics, which now offers Border Gateway Protocol (BGP) peering analytics, giving network operators deep, flexible and actionable insight into how their traffic traverses the Internet.

Using Kentik Detect’s BGP path-aware visualizations, network operators can see how much Internet traffic is flowing from their network to neighboring networks, through intermediate transit networks, and ultimately to destination networks and countries. Content providers can identify the geographical regions receiving the most traffic and choose optimal peering and transit agreements. Network managers can see anomalies that might indicate costly configuration errors. Large ISPs and wholesale providers can even identify business prospects by examining downstream traffic.

“Practical peering analytics has simply been inaccessible for the vast majority of network operators,” said Avi Freedman, Kentik CEO. “At best, legacy home-grown and commercial solutions could generate some pretty pictures, but which were static and useless for operational purposes. We designed Kentik Detect for the cloud era, with scale-out technology that ingests and queries billions of non-summarized traffic and routing records per customer. Kentik Detect’s new peering analysis lets network operators rapidly assemble vast sets of network data and perform real-time, recursive analytics to answer real-world questions.”

“Kentik’s team knows firsthand the problems involved in managing large-scale networks, and has designed a SaaS platform specifically tailored for that target market,” said Jennifer P. Clark, Vice President of Network Research for 451 Research. “The addition of peering analytics broadens the use cases and value of Kentik’s Big Data engine and cloud-scale approach to network traffic monitoring and showcases the company’s real-world know-how in network operations.”

Kentik Detect’s Peering Analytics capabilities include:

- Big Data Infrastructure: Complete analytical access to at least 90 days of raw NetFlow/sFlow/IPFIX records fused with BGP routing and GeoIP data

- Customizable Datasets: Easy-to-use wizards for setting key filter criteria via field text search plus an advanced configuration tool.

- Intuitive and Interactive Visualizations: Sankey diagrams make it easy to understand relative traffic volumes via visual depictions of top BGP paths, Next-Hop ASNs, Transit ASNs, Last-Hop ASNs, and Destination Countries.

- Responsive Tools: Analytics can be quickly narrowed or adjusted through a variety of real-time tools including ASN drill-downs, exporting device selection, ASN and interface filters and a convenient ‘ignore first hop’ checkbox.

- Practical Reports: For any particular ASN, network operators can see tables and charts depicting total traffic transiting the ASN, as well as top traffic paths, devices, top source, destination and countries.

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Kentik Introduces SaaS BGP Peering Analysis Solution

Kentik announced a major expansion of Kentik Detect, its flagship solution for cloud-based network visibility and analytics, which now offers Border Gateway Protocol (BGP) peering analytics, giving network operators deep, flexible and actionable insight into how their traffic traverses the Internet.

Using Kentik Detect’s BGP path-aware visualizations, network operators can see how much Internet traffic is flowing from their network to neighboring networks, through intermediate transit networks, and ultimately to destination networks and countries. Content providers can identify the geographical regions receiving the most traffic and choose optimal peering and transit agreements. Network managers can see anomalies that might indicate costly configuration errors. Large ISPs and wholesale providers can even identify business prospects by examining downstream traffic.

“Practical peering analytics has simply been inaccessible for the vast majority of network operators,” said Avi Freedman, Kentik CEO. “At best, legacy home-grown and commercial solutions could generate some pretty pictures, but which were static and useless for operational purposes. We designed Kentik Detect for the cloud era, with scale-out technology that ingests and queries billions of non-summarized traffic and routing records per customer. Kentik Detect’s new peering analysis lets network operators rapidly assemble vast sets of network data and perform real-time, recursive analytics to answer real-world questions.”

“Kentik’s team knows firsthand the problems involved in managing large-scale networks, and has designed a SaaS platform specifically tailored for that target market,” said Jennifer P. Clark, Vice President of Network Research for 451 Research. “The addition of peering analytics broadens the use cases and value of Kentik’s Big Data engine and cloud-scale approach to network traffic monitoring and showcases the company’s real-world know-how in network operations.”

Kentik Detect’s Peering Analytics capabilities include:

- Big Data Infrastructure: Complete analytical access to at least 90 days of raw NetFlow/sFlow/IPFIX records fused with BGP routing and GeoIP data

- Customizable Datasets: Easy-to-use wizards for setting key filter criteria via field text search plus an advanced configuration tool.

- Intuitive and Interactive Visualizations: Sankey diagrams make it easy to understand relative traffic volumes via visual depictions of top BGP paths, Next-Hop ASNs, Transit ASNs, Last-Hop ASNs, and Destination Countries.

- Responsive Tools: Analytics can be quickly narrowed or adjusted through a variety of real-time tools including ASN drill-downs, exporting device selection, ASN and interface filters and a convenient ‘ignore first hop’ checkbox.

- Practical Reports: For any particular ASN, network operators can see tables and charts depicting total traffic transiting the ASN, as well as top traffic paths, devices, top source, destination and countries.

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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.