
Kentik Technologies has raised $23 million in a Series B funding round led by Third Point Ventures, with participation by existing investors August Capital, Data Collective (DCVC), First Round Capital, and Engineering Capital, and new investors Glynn Capital and David Ulevitch.
The new investment will enable Kentik to meet strong demand for its big data-based network traffic and performance visibility solutions. Kentik plans to use the additional capital to increase headcount in the next year to accelerate product capabilities and expand market reach.
“We are thrilled to bring Kentik into our portfolio of cloud technology companies. The exceptional team, high-value technology and resulting aggressive customer adoption of Kentik’s offering made this a compelling investment for us,” said Robert Schwartz, Third Point Ventures Managing Partner. “There is a big gap in network visibility for digital business operations, and Kentik’s disruptive big data analytics fills that gap in a way that is far more powerful and easier to use than legacy options. We look forward to helping Kentik with their market and product expansion as the world continues to shift to digital business models that are critically dependent on network traffic delivery.”
Kentik ingests network data at Internet scale – hundreds of billions of records per day – in real-time, and allows users to run multi-dimensional queries and receive visualizations in a few seconds. This allows Kentik to support key network visibility use cases at greater scale and detail than previously possible.
“We founded this company because we believe in the value of data to make businesses run smarter, faster, and with more innovation. We’ve been extremely gratified to see customers respond so positively to our vision and solution for network analytics,” said Avi Freedman, Kentik CEO. “This latest round of funding validates the traction we’ve achieved with our approach to the market, and will help us continue to revolutionize what network traffic intelligence means for numerous use cases across network operations, engineering, and security.”
"We've supported Kentik from the beginning, and have seen how strongly they've executed on their goals, including on-boarding an amazing, who's who customer list" said Howard Morgan, partner at First Round Capital. "Avi and his team have consistently delivered on their promises, and we're excited to participate in this round to accelerate Kentik's growth.”
“Kentik has fundamentally altered the value you can get from network data. Old methods were so slow and costly that they couldn't solve real-world problems like figuring out what's really behind application performance issues,” said Matt Ocko, co-Managing Partner of DCVC. “Kentik's depth and speed of insight means that network teams can find and fix issues that APM solutions don't see; security teams can be a step ahead of DDoS attacks instead of always behind the curve; planning teams can improve user experience while reducing costs; and marketing teams can offer customers more insight into their critical traffic than they've ever had before. This adds up to a massively compelling ROI for Kentik's customers. Together with Kentik delivering this industry-beating ROI via a defensible big data platform that can ingest and index trillions of packets a day, fully SQL-query-responsive in real-time, made us enthusiastic to far exceed our pro-rata share in this investment round.”
Kentik is aggressively hiring in sales, systems engineering, marketing, software development and operations.
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.