
Kentik announced the launch of Kentik Synthetic Monitoring, proactive network monitoring that simulates an end-user’s experience with infrastructure, applications or services.
The Kentik Network Intelligence Platform is now the only fully integrated network traffic and synthetic monitoring analytics solution on the market, and the only solution to enable autonomous testing ― for both cloud and hybrid networks.
With Kentik Synthetic Monitoring, network teams have a fully integrated solution that can autonomously configure their tests, present the full network context, and make the resulting insights actionable immediately.
Synthetic testing integrated with actual network traffic and device data gives Kentik trillions of even better eyes on the network.
“Lack of understanding of network usage and state has led to the massive failure of synthetic monitoring,” said Avi Freedman, co-founder and CEO of Kentik. “Kentik already has real-time visibility into over 1 trillion traffic measurements per day across billions of users and sees every network connected to the internet. Synthetic testing integrated with actual network traffic and device data gives Kentik trillions of even better eyes on the network. We are changing the game with synthetic monitoring that’s exponentially more valuable.”
Kentik Synthetic Monitoring uses private agents that deploy quickly and easily and a network of global agents that are strategically positioned in internet cities around the world and in every cloud region within AWS, Google Cloud, Microsoft Azure and IBM Cloud. The service feeds into the Kentik Data Engine (KDE), a patented hybrid columnar and streaming data engine for distributed ingest, enrichment, learning and analytics, which uses machine learning to analyze, predict and respond in real time, at internet scale.
“Data from Kentik Synthetic Monitoring allows us to continue to extend our already insurmountable lead in volume, velocity and quality of network measurement, leveraging the telemetry to build even better models of network, application, and user behavior,” added Freedman.
Kentik Synthetic Monitoring frequently and autonomously measures performance and availability metrics of essential infrastructure, applications and services including:
- SaaS solutions
- Applications hosted in the public cloud
- Internal applications
- Transit and peer networks
- Content delivery networks
- Streaming video, social, gaming and other content providers
- Site-to-site performance across traditional WAN and SD-WANs
- Service provider connectivity and customer SLAs
“Our customers have been vocal for some time that the existing approaches to synthetic network testing are falling short because they are too manual, too static and too expensive,” said Christoph Pfister, CPO of Kentik. “We designed Kentik Synthetics to test autonomously, taking into account the dynamic nature of modern networks and the internet. In addition, we believe the industry has been held back for too long by a lack of affordability, forcing customers to trade off testing needs with cost constraints. Kentik is doing away with all this today by introducing a price point that allows customers to monitor frequently, monitor autonomously, and monitor everything that matters.”
Kentik Synthetic Monitoring is available now in preview, with GA planned for this quarter.
The Latest
Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...
Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...
When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...
If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...
Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...
AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...
Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...
Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...
Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...
In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...