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AIOps startup Senser launches with $9.5M in fundingAIOps startup Senser launches with $9.5M in funding

Startup Senser emerged from stealth with $9.5 million in seed funding to launch an AIOps platform that leverages Extended Berkeley Packet Filter (eBPF) technology to provide enterprises with insights into their cloud and IT environments.#pressrelease

September 27, 2023

3 Min Read

TEL AVIV – Senser, the pioneer of zero-instrumentation production intelligence, emerged from stealth with $9.5 million in seed funding to launch an AIOps platform that leverages Extended Berkeley Packet Filter (eBPF) technology to provide enterprises with deep insights into their complex cloud and IT environments. The seed round was led by Eclipse with participation by Amdocs and other private investors.

Harnessing the power of groundbreaking eBPF technology, Senser's platform enables dynamic, non-intrusive data collection with zero instrumentation or configuration required. The platform also leverages machine learning (ML) to deliver automated insights into root cause and business impact when issues like outages or service degradations arise. Senser's AI-powered chatbot enables users from a variety of technical backgrounds to quickly answer critical questions about their production environment using only natural language prompts.

As adoption of cloud computing, microservices and APIs has grown, cloud environments have become highly complex and interconnected, businesses have increasingly turned to the observability sector to monitor these distributed service webs. But traditional observability technology places a massive burden on organizations to implement, configure and manage these tools – and decode what a flood of alerts means for their bottom line.

That's because legacy observability tools were designed to look at specific parts of a company's environment (e.g., infrastructure, applications, or networks) in isolation – without an overall view of business or user flows. As a result, getting a complete picture of how services behave atop the environment requires tedious manual instrumentation or extensive configuration of modules and dashboards. And understanding the root cause and business impact of a system issue requires lengthy manual analysis, often while the clock is ticking on a critical failure.

"Industrial 4.0 apps — such as warehouse automation or robotics — and outdoor mapping apps like drones must run reliably on both clients and edge locations in order to power the foundation of physical industries," said Lior Susan, founding partner of Eclipse. "Through the underlying use of eBPF and machine learning, Senser is ensuring these apps are performing at the highest levels, while solving problems — like the staggering cost of configuration and maintenance, and the lack of useful insights — in a unique way."

Traditional application and performance monitoring (APM) and observability technology costs enterprises an average of 20-30% of overall infrastructure costs. This does not include the expense of dedicated engineering resources to configure, customize, analyze, and maintain observability technology as a company's cloud environment evolves.

"Downtime costs enterprises an average of $1 million per hour and a long-term loss of customer trust," said Amir Krayden, Co-Founder and CEO of Senser. "We built Senser to make it easy for SRE and DevOps leaders to go past alerts and dashboards and solve issues quickly. The average Senser customer reduces mean time to detect (MTTD) by 83% – an outcome made possible because eBPF enables immediate visibility across production systems with no overhead, and ML delivers answers, rather than alert floods."

In addition to identifying service degradations, Senser's technology helps companies manage the health and performance of their production environment more broadly. This includes identifying hidden drivers of cloud computing costs and leakage of potentially sensitive assets.

Read the full press release here.


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