Kentik Blog: AI
In large-scale AI model training, network performance is no longer a supporting actor — it’s center stage. Job Completion Time (JCT), the key metric for measuring training efficiency, is heavily influenced by the network interconnecting thousands of GPUs. In this post, learn why JCT matters, how microbursts and GPU synchronization delays inflate it, and how platforms like Kentik give network engineers the visibility and intelligence they need to keep training jobs on schedule.
At this year’s AWS Summit in New York, agentic AI took center stage with Amazon’s launch of Bedrock AgentCore — a powerful step toward turning AI prototypes into scalable, production-ready applications. From low-code workflows to turnkey infrastructure, a new generation of tools is enabling teams of all skill levels to build, deploy, and monitor AI agents faster than ever. In this post, learn about the shift from experimental AI to enterprise-ready systems, and why network intelligence is the glue holding it together.
Kentik transforms real-time network telemetry into actionable alerts for AI-optimized data centers. By converting database queries into custom alerts, engineers can detect issues like elephant flows, idle links, and packet loss before performance suffers and triggers alerts in systems like ServiceNow or PagerDuty.
AI is a hot topic right now, but it’s only valuable when it drives action. The real goal is business intelligence—insight that’s timely, trustworthy, and tied to decisions that move the business forward.
While AI offers powerful benefits for network operations, building an in-house AI solution presents major challenges, particularly around complex data engineering, staffing specialized roles, and maintaining models over time. The effort required to handle real-time telemetry, retrain models, and manage evolving environments is often too great for most IT teams. For enterprise networks, partnering with a vendor that specializes in AI and network operations is typically a more efficient, scalable, and sustainable approach.
AI can be a transformative tool in network operations — but only when it’s tied to clear, measurable outcomes. Rather than chasing hype, IT and NetOps teams should focus on solving specific operational challenges like reducing MTTR, cutting costs, and stabilizing infrastructure. AI has real potential when strategically applied, and when aligned with business goals, it becomes a powerful ally in modern network operations.
Cloud Pathfinder simplifies cloud troubleshooting by visually mapping connectivity paths between cloud endpoints and integrating the power of AI, identifying where and why traffic is being blocked. By analyzing cloud configuration metadata, it provides instant, actionable insights into routing and security issues — saving engineers hours of manual work.
Today, we’re excited to launch Cloud Pathfinder, an AI-powered path assessment service built into Kentik Journeys. Read on to learn how Cloud Pathfinder gives you instant, turn-by-turn insight into cloud routing—mapping out every hop, gateway, VPC/VNet, and attachment along the way.
AI has the power to optimize network performance, detect anomalies, and improve capacity planning, but its effectiveness depends on high-quality data. Without a strong data foundation, AI remains a theoretical tool rather than delivering real operational value.
In this post, discover how Kentik Journeys integrates large language models to revolutionize network observability. By enabling anyone in IT to query and analyze network telemetry in plain language, regardless of technical expertise, Kentik breaks down silos and democratizes access to critical insights simplifying complex workflows, enhancing collaboration, and ensuring secure, real-time access to your network data.
When network issues strike, every second matters. Latency or packet loss can frustrate users and hurt revenue. Learn how Kentik AI uses natural language to speed up troubleshooting and isolate problems quickly.
Balancing cost efficiency and high performance in cloud networks is a constant challenge, especially when misconfigurations or inefficient routing lead to inflated costs or degraded performance. Learn how Kentik Journeys simplifies traffic analysis, helping cloud engineers identify inefficiencies like unnecessary Transit Gateway routing.
Kentik Journeys is an AI-powered user experience that helps you investigate your network. It combines knowledge about your network with deep GenAI integration to help you answer network questions and solve problems faster than ever. Since launch, we’ve been innovating on Journeys’ capabilities and skills with customer feedback. Here’s a peek at what’s new.
The 2024 EMA Network Megatrends surveyed hundreds of IT professionals about their approach to managing, monitoring, and troubleshooting their networks. In this post, we examine the report’s findings to learn the business and technology trends shaping network operations strategy.
From infrastructure and chipsets to data solutions and development tools, it’s clear that artificial intelligence will play a massive role in the future of the cloud. Read Justin Ryburn’s latest recap to learn about many of the exciting unveilings at Google Cloud Next 2024.
AI has been on a decades-long journey to revolutionize technology by emulating human intelligence in computers. Recently, AI has extended its influence to areas of practical use with natural language processing and large language models. Today, LLMs enable a natural, simplified, and enhanced way for people to interact with data, the lifeblood of our modern world. In this extensive post, learn the history of LLMs, how they operate, and how they facilitate interaction with information.
Kentik Journeys uses an AI-based, large language model to explore data from your network and troubleshoot problems in real time. Using natural language queries, Kentik Journeys is a huge step forward in leveraging AI to democratize data and make it simple for any engineer at any level to analyze network telemetry at scale.
We’re bringing AI to network observability. And adding new products to Kentik, including a modern NMS, to simplify troubleshooting complex networks.
Traditional data center networking can’t meet the needs of today’s AI workload communication. We need a different networking paradigm to meet these new challenges. In this blog post, learn about the technical changes happening in data center networking from the silicon to the hardware to the cables in between.
Artificial intelligence is certainly a hot topic right now, but what does it mean for the networking industry? In this post, Phil Gervasi looks at the role of AI and LLM in networking and separates the hype from the reality.