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BlazeMeter Unveils New Test Automation for DevOps Features

BlazeMeter announced new Test Automation Platform for DevOps.

These new capabilities such as KPI Trends, Organizations and Projects, On-Premise Load Generator and Single Sign-On address the performance testing needs of organizations that are aligned with the DevOps culture.

BlazeMeter’s Test Automation for DevOps solution delivers several key features, including KPI Trends. KPI Trends automatically provide reports that graphically display Key Performance Indicators (KPIs) - allowing users to easily identify trends over time across all of a test’s results. KPI metrics include Average Response Time from all recent reports and many more KPIs, such as Errors, Average Hits, Average Latency and Test Duration.

The Organizations and Projects feature allows companies to arrange their testing efforts to be in-line with their existing internal departmental and project structures, adhering to their corporate governance. This enables improved collaboration and sharing within project teams and organizations. Users can put their tests into Project folders and collaborate with colleagues within designated "Organizations". Members of a specific Organization will be able to view, edit and execute all tests and reports created within it. Another important benefit is that users no longer need to share passwords.

“We are pleased that our new ‘Test Automation for DevOps’ features provide our customers easy-to-use and powerful tools for modern companies that are closely assimilated with the DevOps movement,” said, Alon Girmonsky, CEO and Founder of BlazeMeter. “As companies iterate faster every day, they need continuous performance testing solutions that sit across the whole development lifecycle and seamlessly integrate into a company’s Continuous Integration and Continuous Deployment process and workflows.”

The improved On Premise Load Generator with newer Docker-based technology provides increased reliability and an easier installation process. Additionally, clients can now install multiple load generators on a single server, greatly reducing on-premise server costs.

The new Single Sign-On feature provides both the access and authentication control that companies need for third party services, as well as enabling new users within the organization to start using the service immediately.

BlazeMeter’s ‘Test Automation for DevOps’ new features are immediately available.

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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 ...

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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 ...

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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.

BlazeMeter Unveils New Test Automation for DevOps Features

BlazeMeter announced new Test Automation Platform for DevOps.

These new capabilities such as KPI Trends, Organizations and Projects, On-Premise Load Generator and Single Sign-On address the performance testing needs of organizations that are aligned with the DevOps culture.

BlazeMeter’s Test Automation for DevOps solution delivers several key features, including KPI Trends. KPI Trends automatically provide reports that graphically display Key Performance Indicators (KPIs) - allowing users to easily identify trends over time across all of a test’s results. KPI metrics include Average Response Time from all recent reports and many more KPIs, such as Errors, Average Hits, Average Latency and Test Duration.

The Organizations and Projects feature allows companies to arrange their testing efforts to be in-line with their existing internal departmental and project structures, adhering to their corporate governance. This enables improved collaboration and sharing within project teams and organizations. Users can put their tests into Project folders and collaborate with colleagues within designated "Organizations". Members of a specific Organization will be able to view, edit and execute all tests and reports created within it. Another important benefit is that users no longer need to share passwords.

“We are pleased that our new ‘Test Automation for DevOps’ features provide our customers easy-to-use and powerful tools for modern companies that are closely assimilated with the DevOps movement,” said, Alon Girmonsky, CEO and Founder of BlazeMeter. “As companies iterate faster every day, they need continuous performance testing solutions that sit across the whole development lifecycle and seamlessly integrate into a company’s Continuous Integration and Continuous Deployment process and workflows.”

The improved On Premise Load Generator with newer Docker-based technology provides increased reliability and an easier installation process. Additionally, clients can now install multiple load generators on a single server, greatly reducing on-premise server costs.

The new Single Sign-On feature provides both the access and authentication control that companies need for third party services, as well as enabling new users within the organization to start using the service immediately.

BlazeMeter’s ‘Test Automation for DevOps’ new features are immediately available.

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.