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Kentik AI Advisor Released

Kentik launched Kentik AI Advisor – an artificial intelligence that deeply understands enterprise and service provider networks, thinks critically, and provides guidance for designing, operating, and protecting infrastructure at scale.

Kentik AI Advisor drives massive efficiencies across network, cloud, and infrastructure teams, and revolutionizes how companies approach network management and performance. The AI can interpret intent, build a plan based on rich telemetry from the Kentik platform, and then execute that plan reliably and securely.

Kentik AI Advisor leverages the proprietary Kentik Data Engine – an ultra-scalable, real-time data platform that ingests a trillion telemetry points per day – in order to unify cloud, device, flow, and internet data. AI Advisor combines advanced LLM and reasoning model capabilities with Kentik’s deep network expertise and engineering context to interpret intent, plan investigations, and explain its logic every step of the way.

Benefits of using the Kentik AI Advisor include:

  • Cost Optimization: Kentik AI Advisor automates work needed to identify cost efficiencies across your entire network. Whether on-prem, in the cloud, or across both – Kentik AI Advisor can automatically sift through all your network data to uncover opportunities to reduce VPC and transit costs, optimize peering and interconnects, and evaluate high-cost routes to improve your overall cost structure.
  • Capacity Planning: Kentik AI Advisor automatically analyzes utilization trends, forecasts run-out scenarios, and recommends optimal infrastructure investments. This innovation transforms capacity planning from reactive guesswork into proactive, data-driven intelligence.
  • Rapid Troubleshooting and DDoS Investigation: Kentik AI Advisor accelerates incident response and cuts MTTR by correlating flow, device, and cloud data to quickly pinpoint root causes and separate real threats from background noise. It then delivers clear, expert recommendations to guide fast mitigation and restore service stability.
  • Integrated Institutional Knowledge: Kentik AI Advisor goes beyond foundational LLM knowledge, leveraging internal runbooks and custom network context to deliver tailored value and insights based on unique business needs.

“Kentik AI Advisor is designed for modern infrastructure teams that are tasked with scaling operations, optimizing costs, and safeguarding digital assets — all while facing talent shortages,” said Avi Freedman, CEO and Co-Founder of Kentik. “Unlike traditional AIOps which correlates and reduces only across the alerts generated for it, AI Advisor leverages its deep contextual understanding of every network to research and identify issues, analyze trends, and recommend precise actions.”

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.

Kentik AI Advisor Released

Kentik launched Kentik AI Advisor – an artificial intelligence that deeply understands enterprise and service provider networks, thinks critically, and provides guidance for designing, operating, and protecting infrastructure at scale.

Kentik AI Advisor drives massive efficiencies across network, cloud, and infrastructure teams, and revolutionizes how companies approach network management and performance. The AI can interpret intent, build a plan based on rich telemetry from the Kentik platform, and then execute that plan reliably and securely.

Kentik AI Advisor leverages the proprietary Kentik Data Engine – an ultra-scalable, real-time data platform that ingests a trillion telemetry points per day – in order to unify cloud, device, flow, and internet data. AI Advisor combines advanced LLM and reasoning model capabilities with Kentik’s deep network expertise and engineering context to interpret intent, plan investigations, and explain its logic every step of the way.

Benefits of using the Kentik AI Advisor include:

  • Cost Optimization: Kentik AI Advisor automates work needed to identify cost efficiencies across your entire network. Whether on-prem, in the cloud, or across both – Kentik AI Advisor can automatically sift through all your network data to uncover opportunities to reduce VPC and transit costs, optimize peering and interconnects, and evaluate high-cost routes to improve your overall cost structure.
  • Capacity Planning: Kentik AI Advisor automatically analyzes utilization trends, forecasts run-out scenarios, and recommends optimal infrastructure investments. This innovation transforms capacity planning from reactive guesswork into proactive, data-driven intelligence.
  • Rapid Troubleshooting and DDoS Investigation: Kentik AI Advisor accelerates incident response and cuts MTTR by correlating flow, device, and cloud data to quickly pinpoint root causes and separate real threats from background noise. It then delivers clear, expert recommendations to guide fast mitigation and restore service stability.
  • Integrated Institutional Knowledge: Kentik AI Advisor goes beyond foundational LLM knowledge, leveraging internal runbooks and custom network context to deliver tailored value and insights based on unique business needs.

“Kentik AI Advisor is designed for modern infrastructure teams that are tasked with scaling operations, optimizing costs, and safeguarding digital assets — all while facing talent shortages,” said Avi Freedman, CEO and Co-Founder of Kentik. “Unlike traditional AIOps which correlates and reduces only across the alerts generated for it, AI Advisor leverages its deep contextual understanding of every network to research and identify issues, analyze trends, and recommend precise actions.”

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.