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BlazeMeter Introduces Continuous-Testing-as-a-Service

BlazeMeter launched a continuous testing as a service platform, expanding the company’s portfolio options to meet the full spectrum of performance to mobile testing demands.

The new platform, using cloud and open source tools, reduces the barriers to easily create and run tests and integrate testing into the existing development environment. By facilitating continuous testing throughout the continuous delivery process, the platform enables enterprises to ensure a fast and error free user experience that provides shorter release cycles without compromising software quality.

As software increases in complexity, the number of required tests grows exponentially. While software development and deployment environments have advanced rapidly, testing is often still executed by people in different departments using different tools. To-date, testing has been a complex and resource intensive process with a workflow that is time consuming and extremely hard to maintain. The current process results in inadequate testing, poor software quality and an increased time to release, often compounded by human error.

“This ongoing shift in the industry is forcing developers to adopt agile continuous deployment and integration methodologies and move from perpetual to subscription-based solutions to keep up with the pace of the development cycles,” said Alon Girmonsky, BlazeMeter Founder and CEO. “Our open source and cloud based testing platform helps developers facilitate continuous testing as part of the product delivery cycle.”

BlazeMeter has broken through the barrier with its fully automated continuous testing that can now support dozens of engineering teams committing code, integrating and deploying continuously without compromising software quality. Now, testing can be done in a matter of hours, not days, eliminating friction and human error, and allowing teams to immediately identify and address any problems.

Continuous testing as a service using BlazeMeter’s testing platform, as well as industry standard tools and services such as Sauce Labs, Jenkins, GitHub and New Relic, employ the steps outlined below. Together with Sauce Labs, the two companies provide a complete continuous testing as a service solution for the entire testing lifecycle based on open source software and cloud based services.

“Automated testing is now a critical part of continuous delivery pipelines that are expected to run through complete test suites many times a day, with little tolerance for manual intervention, false failures, or reliability issues related to the test infrastructure," said Steven Hazel, Sauce Labs' Co-Founder and Chief Product Officer. “Combining functional testing from Sauce Labs and performance testing from BlazeMeter gives software teams a modern testing platform capable of meeting the demands of continuous delivery."

Testing starts by the developer using BlazeMeter and/or Sauce Labs to develop a comprehensive set of performance and functional tests that completely validate the quality of the developed module. This set of tests will be used in later stages to continue the validation of the module in the continuous delivery process.

BlazeMeter’s performance and Sauce Labs’ functional testing platforms support reading scripts and running tests with ‘DevOps’ friendly leading open source tools (e.g. JMeter, Selenium) and languages, such as Python, Ruby, or even Shell Scripts. The testing solutions provide libraries and APIs to create “homegrown” tests using common DSLs. With the new continuous testing service, the test creation and the running of the tests can be done with a robust API – everything can be automated regardless of the system you are using. Additionally, developers can replace scripts and use industry standard JSON files, that all developers are familiar with, to configure the load test. This reduces the developer’s need to learn a new language and allows them to perform tests much more quickly. Ideally, test scenarios and accompanying test codes are pulled directly from a source control tool such as GitHub.

A set of tests can be run with different provisioning. For example, a developer can run a module test from behind the firewall with local resources. On production, the same test can come from multi-geographies from the public cloud with or without load. When a test configuration is executed in an environment (e.g. Dev, CI, Pre-Prod, Post-Prod), it can easily adopt a different provisioning scheme. With BlazeMeter’s continuous testing as a service, users can now use the same tool and test definitions across the board, regardless of how they want to interact with the system. The company provides a single common platform regardless of how a user wants to interface with it, while accommodating both GUI and API access.

For an organization to run all required tests in parallel with zero time to test, sufficient resources need to be allocated for all teams according to their use case. BlazeMeter’s continuous testing on any test configuration, as well as on-demand, using simple API calls and running in parallel supports:

● Unlimited resources in public clouds (e.g. GCE, AWS, Azure, HP, Rackspace, Joyent)

● Private Cloud

● Running in the developer’s local environment or data center behind the firewall

BlazeMeter and Sauce Labs’ testing environments seamlessly integrate into existing reporting solutions such as Jenkins Performance Trend and existing standard open source and cloud-based tools in the continuous delivery lifecycle. The combined solutions provide “pass”/”fail” trend reports and “deep dive” reports for diagnostics and analysis. The testing solutions also overlay data from 3rd party systems such as New Relic to present a comprehensive picture.
Alerts In Case of Failures

The testing environments can indicate a “failure”, gather all test artifacts and immediately send them to the group of interested people (e.g. the developers). The solutions provide an alerting scheme per developer, module and project that allows running the exact failed tests all over again to identify the cause for failure.

Taking a snapshot of a build, release or production, provides different modules at different releases. To be able to comprehensively test every such snapshot, BlazeMeter can combine different test configuration fragments into one test configuration. The continuous testing solution now also granularly defines test result thresholds with pass/fail results saved in XML format and consumed by JUnit.
Version Control Friendly Incremental Testing

BlazeMeter’s continuous testing as a service framework is version control friendly and supports incremental testing to associate test configurations, set of tests and tests with versions (e.g code, build, RC, releases).

BlazeMeter’s Continuous Testing as a Service platform is 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 Introduces Continuous-Testing-as-a-Service

BlazeMeter launched a continuous testing as a service platform, expanding the company’s portfolio options to meet the full spectrum of performance to mobile testing demands.

The new platform, using cloud and open source tools, reduces the barriers to easily create and run tests and integrate testing into the existing development environment. By facilitating continuous testing throughout the continuous delivery process, the platform enables enterprises to ensure a fast and error free user experience that provides shorter release cycles without compromising software quality.

As software increases in complexity, the number of required tests grows exponentially. While software development and deployment environments have advanced rapidly, testing is often still executed by people in different departments using different tools. To-date, testing has been a complex and resource intensive process with a workflow that is time consuming and extremely hard to maintain. The current process results in inadequate testing, poor software quality and an increased time to release, often compounded by human error.

“This ongoing shift in the industry is forcing developers to adopt agile continuous deployment and integration methodologies and move from perpetual to subscription-based solutions to keep up with the pace of the development cycles,” said Alon Girmonsky, BlazeMeter Founder and CEO. “Our open source and cloud based testing platform helps developers facilitate continuous testing as part of the product delivery cycle.”

BlazeMeter has broken through the barrier with its fully automated continuous testing that can now support dozens of engineering teams committing code, integrating and deploying continuously without compromising software quality. Now, testing can be done in a matter of hours, not days, eliminating friction and human error, and allowing teams to immediately identify and address any problems.

Continuous testing as a service using BlazeMeter’s testing platform, as well as industry standard tools and services such as Sauce Labs, Jenkins, GitHub and New Relic, employ the steps outlined below. Together with Sauce Labs, the two companies provide a complete continuous testing as a service solution for the entire testing lifecycle based on open source software and cloud based services.

“Automated testing is now a critical part of continuous delivery pipelines that are expected to run through complete test suites many times a day, with little tolerance for manual intervention, false failures, or reliability issues related to the test infrastructure," said Steven Hazel, Sauce Labs' Co-Founder and Chief Product Officer. “Combining functional testing from Sauce Labs and performance testing from BlazeMeter gives software teams a modern testing platform capable of meeting the demands of continuous delivery."

Testing starts by the developer using BlazeMeter and/or Sauce Labs to develop a comprehensive set of performance and functional tests that completely validate the quality of the developed module. This set of tests will be used in later stages to continue the validation of the module in the continuous delivery process.

BlazeMeter’s performance and Sauce Labs’ functional testing platforms support reading scripts and running tests with ‘DevOps’ friendly leading open source tools (e.g. JMeter, Selenium) and languages, such as Python, Ruby, or even Shell Scripts. The testing solutions provide libraries and APIs to create “homegrown” tests using common DSLs. With the new continuous testing service, the test creation and the running of the tests can be done with a robust API – everything can be automated regardless of the system you are using. Additionally, developers can replace scripts and use industry standard JSON files, that all developers are familiar with, to configure the load test. This reduces the developer’s need to learn a new language and allows them to perform tests much more quickly. Ideally, test scenarios and accompanying test codes are pulled directly from a source control tool such as GitHub.

A set of tests can be run with different provisioning. For example, a developer can run a module test from behind the firewall with local resources. On production, the same test can come from multi-geographies from the public cloud with or without load. When a test configuration is executed in an environment (e.g. Dev, CI, Pre-Prod, Post-Prod), it can easily adopt a different provisioning scheme. With BlazeMeter’s continuous testing as a service, users can now use the same tool and test definitions across the board, regardless of how they want to interact with the system. The company provides a single common platform regardless of how a user wants to interface with it, while accommodating both GUI and API access.

For an organization to run all required tests in parallel with zero time to test, sufficient resources need to be allocated for all teams according to their use case. BlazeMeter’s continuous testing on any test configuration, as well as on-demand, using simple API calls and running in parallel supports:

● Unlimited resources in public clouds (e.g. GCE, AWS, Azure, HP, Rackspace, Joyent)

● Private Cloud

● Running in the developer’s local environment or data center behind the firewall

BlazeMeter and Sauce Labs’ testing environments seamlessly integrate into existing reporting solutions such as Jenkins Performance Trend and existing standard open source and cloud-based tools in the continuous delivery lifecycle. The combined solutions provide “pass”/”fail” trend reports and “deep dive” reports for diagnostics and analysis. The testing solutions also overlay data from 3rd party systems such as New Relic to present a comprehensive picture.
Alerts In Case of Failures

The testing environments can indicate a “failure”, gather all test artifacts and immediately send them to the group of interested people (e.g. the developers). The solutions provide an alerting scheme per developer, module and project that allows running the exact failed tests all over again to identify the cause for failure.

Taking a snapshot of a build, release or production, provides different modules at different releases. To be able to comprehensively test every such snapshot, BlazeMeter can combine different test configuration fragments into one test configuration. The continuous testing solution now also granularly defines test result thresholds with pass/fail results saved in XML format and consumed by JUnit.
Version Control Friendly Incremental Testing

BlazeMeter’s continuous testing as a service framework is version control friendly and supports incremental testing to associate test configurations, set of tests and tests with versions (e.g code, build, RC, releases).

BlazeMeter’s Continuous Testing as a Service platform is 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.