BlazeMeter announced BlazeMeter version 3.0 at AWS re:Invent. The next-generation back-end automated testing framework facilitates continuous testing as part of the product delivery cycle. This allows users to run an unlimited number of tests with “zero time to test”, resulting in faster release time without compromising the software quality.
BlazeMeter version 3.0 delivers several key features to BlazeMeter users, including generating load using real browsers by utilizing Selenium web driver technology. Customers can now reuse existing Selenium scripts or record a new Selenium script using the open source Selenium builder and then run a load based on the recorded script.
BlazeMeter also added multi-cloud vendors HP, Google, Joyent and Rackspace to the existing Amazon Web Services (AWS) locations. New cloud platforms will be added on an ongoing basis to provide better flexibility in selecting the load origin. Users can also run tests using the multi-cloud feature to load test their CDN. A brand-new API driven architecture enables complete functionality through a simple REST API allowing users to fully automate testing through the continuous delivery process. The new User Interface (UI) facilitates fast and effortless performance testing so that users can easily access and enjoy the vast array of version 3.0’s new features and functionalities.
With its Multi-Instance tests, BlazeMeter 3.0 significantly expedites the user’s ability to seamlessly build a collection of tests from disparate testing tools and orchestrate them to run together on every build and release. This allows users to run an unlimited number of tests in parallel -- tests generated by various open source tools -- by reducing the efforts associated in running regression, post-build and pre-production tests.
“BlazeMeter 3.0 provides noteworthy advantages to our customers and existing users, enabling them to get closer to their continuous integration and delivery process,” said Alon Girmonsky, Founder and CEO of BlazeMeter. “The new features and functionality, in particular the REST API and Multi-Instance tests, allow users to run an unlimited number of tests from various open source tools with ‘zero time to test’, resulting in a significantly reduced time-to-release and better software quality.”
Another key functionality in version 3.0 is the new Mobile Dashboard that is available on iPhone and Android native apps. The Mobile Dashboard is also available today on the Play and Apple stores. To support mobile productivity, the Mobile Dashboard enables users to constantly stay in control by launching tests, viewing reports and ending test runs while ‘on the go’ from their mobile devices.
In summary, users can now run an unlimited number of tests created by a variety of open source tools to run in parallel in a fully automated fashion. In addition, the reporting data can be consumed in real-time using the same API to determine the fate of the build.
All new BlazeMeter users are automatically given access to version 3.0, while all existing BlazeMeter users can opt-in to start using the new version immediately.
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.