ThousandEyes announced the appointments of Prabha Krishna as VP of People Operations and Ashwin Kedia as VP of Business Development.
These experienced technology executives will lead the company's growth and success by attracting and developing exceptional talent and driving strategic partner alliances to meet the growing global demand for Network Intelligence.
"In the past year, ThousandEyes has experienced rapid growth and momentum. As we continue on this trajectory, two of our biggest priorities are scaling our incredible talent and unique company culture and expanding the value we deliver to our customers through strategic partnerships," said Mohit Lad, ThousandEyes CEO and co-founder. "Prabha has lived and thrived at the intersection of culture and innovation, and she brings a wealth of experience developing high performing teams. Ashwin has a history of building successful alliances with global systems integrators and technology partners that deliver significant customer value. Both are bringing relevant first-hand experience and perspectives from some of the most pioneering technology companies in the world, which strengthen the executive team in areas that are strategic for long-term growth."
Krishna will be responsible for human resources, recruiting and strategic organizational design, applying her technical and engineering leadership experience toward building the organization. Before joining ThousandEyes, Krishna served as VP of Operations at Jaunt, Inc., a virtual reality startup. Prior to transitioning to the people side due to a strong interest in hiring and recognizing talent, she was an engineering director at Google, Adobe, Netflix and Oracle. Krishna was responsible for the design and delivery of various consumer and enterprise products as well as cloud and server-side infrastructure. She holds master's degrees in Computer Science and Physics from the University of California, San Diego and Brown University.
"From the top down, ThousandEyes has built a highly innovative team of experts and created an exceptional culture that is also incredibly collaborative and customer-focused. I'm thrilled to be joining the company to help meet its strategic business goals and develop the greatest asset of any organization — talented and committed employees," said Krishna. "People operations needs to continually innovate and adapt to business needs while creating an environment where the brightest minds can put their knowledge and experience to good use, do their best work and realize their full potential."
Kedia will be responsible for business development, building strategic alliances with global system integrators, cloud service providers, technology and enterprise ecosystem partners. Before joining ThousandEyes, Kedia led the business development team at Splunk that was responsible for developing their global partner ecosystem of systems integrators (SIs), managed service providers (MSPs), original equipment manufacturers (OEMs) and cloud partners. During Kedia's nearly five-year tenure, business development revenue grew 100 percent year-over-year and the company's full year total revenues grew from $66 million to more than $668 million. Prior to Splunk, Kedia held various founder and executive roles at technology and technology consulting companies. Kedia holds a bachelor's degree in computer science from Mumbai University, India.
"ThousandEyes is in a unique position to build on existing strategic collaborations while pursuing new partnerships to address the growing global demand for Network Intelligence as organizations become Internet-centric," said Kedia. "Hybrid cloud has reached an inflection point as applications and services are predominantly deployed off-premises and delivered through the Internet. We look forward to working with strategic partners to expand the value we deliver to our growing list of notable customers."
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.