BlazeMeter announced the acquisition of Loadosophia, which provides state-of-the-art analytics technology for JMeter users.
Andrey Pokhilko, Loadosophia and JMeter-Plugins.org founder, joins BlazeMeter’s executive team as Chief Scientist.
The acquisition of Loadosophia and the addition of Pokhilko’s technological expertise and performance testing experience enhance BlazeMeter’s product innovation and next generation performance testing solutions. BlazeMeter users will be able to utilize Loadosophia’s technology, and further innovations are on the product roadmap. Loadosophia’s customers will continue to be fully supported by BlazeMeter.
“Andrey is one of the most skilled performance testing experts I know. It's a real honor to have him join my team. I'm certain that together we will accomplish great things that will benefit the entire performance testing community,” said Alon Girmonsky, Founder and CEO of BlazeMeter. “I’m constantly looking to add new technologies that complement BlazeMeter’s platform. Andrey and his team’s insights in JMeter development and performance testing, as well as his deep understanding of the needs of large scale testing in an agile development environment will give an invaluable boost to our technological advancements.”
As BlazeMeter’s Chief Scientist, Pokhilko will be responsible for developing, expanding and managing the company’s product portfolio to address the growing needs of its prospects and customers. He will also continue to give back to the community by leading educational activities and launching open source initiatives.
“I am extremely passionate about the Apache JMeter community and that’s why I’ve dedicated so many years to supporting the community and understanding their challenges,” said Andrey Pokhilko, BlazeMeter’s Chief Scientist. “Joining BlazeMeter will enable me to make a huge impact on the load performance testing community. My vision is to make BlazeMeter the center of excellence for load testing and where the community comes to resolve all of their performance testing issues.”
Pokhilko is a renowned thought-leader and innovator within the Apache JMeter and performance testing community. He has worked extensively to create and develop tools that enhance the Apache JMeter protocol coverage and analytical reporting capabilities. In 2009, Pokhilko founded JMeterPlugins.org and led this open source project to its undisputed status as the premier source of JMeter Plugins in the world. Loadosophia was similarly born from Pokhilko’s desire to complete JMeter’s offerings. Pokhilko has world-class experience with large scale performance testing, having led the load testing team for five years at search engine giant Yandex.
The Latest
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.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...