
groundcover raised $35 Million in Series B funding led by Zeev Ventures with follow-up participation from Angular Ventures, Heavybit, and Jibe Ventures.
This brings the company's total funding to $60 Million USD, which will be used to aggressively expand in the USA.
"Our platform offers much better coverage and value than the legacy application monitoring solutions that have been around for over a decade," said Shahar Azulay, CEO and Co-Founder of groundcover. "We are the only solution built with eBPF at the forefront from day one, and we are now pioneering the 'bring your own cloud' approach to observability that enables organizations to keep their data on premise while maintaining all of the benefits of the SaaS experience."
groundcover is a "Bring Your Own Cloud" (BYOC) observability solution, redefining the architecture of a modern observability platform by enabling customers to host their observability data on-prem, while still being fully managed by groundcover. This approach is the X-factor behind groundcover's velocity, maximizing the security and privacy needs of customers, while unlocking coverage tradeoffs with unlimited data, and providing a full observability suite with a simple, predictable pricing model. groundcover also utilizes eBPF to collect observability data straight from the Linux kernel, providing engineers with super-granular visibility into their entire environment including traces, application-level metrics, infrastructure performance and application logs.
"groundcover is fundamentally reshaping the observability landscape. With its eBPF-driven platform and 'Bring Your Own Cloud' approach, it's setting a new standard for depth of observability, cost efficiency, and security," said Oren Zev, Founder of Zeev Ventures. "As the industry continues to shift to richer experiences, such as AI, around observability data, groundcover with its unique and modern architecture is positioned to outpace legacy solutions and dominate the space."
The Latest
FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...
Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...
While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...
A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...