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Sauce Labs Partners with Sumo Logic

Sauce Labs and Sumo Logic announced the formation of a new joint initiative designed to help enterprise organizations drive increased engineering efficiency by delivering greater visibility into the health and security of applications throughout the entire development lifecycle.

The combination will enable customers to leverage their test data from the Sauce Labs Continuous Testing Cloud with Sumo Logic’s Software Development Optimization solution and correlate it with data from other parts of the software development pipeline to automatically derive actionable insights that help improve the quality and reliability of applications.

The new joint initiative, which builds on the companies’ long history of partnership, collaboration, and the already strong integration between their respective platforms, empowers customers to view rich test data from Sauce Labs within Sumo Logic’s cloud-native, Continuous Intelligence Platform™ in order to make better, more informed decisions about the efficacy of their engineering and development processes.

“As testing increasingly becomes a continuous initiative, the ability to find and remedy defects before deployment plays a critical role in building digital confidence,” said Matt Wyman, CPO, Sauce Labs. “The combination of Sauce Labs and Sumo Logic gives customers complete visibility into the health of their applications throughout the entire product development lifecycle, both pre-production, and post. We’re excited to work with the Sumo Logic team to help organizations get more from their engineering investments.”

"As companies move from traditional business models to digital business models, they become software companies and their success depends on the speed and reliability of their digital services. Modern software development lifecycle (SDLC) and associated tools generate data that can be used to optimize and improve the SDLC in turn improving a company's agility and rate of innovation and thus making it more competitive," said Bruno Kurtic, founding VP of Strategy and Solutions, Sumo Logic. "Our partnership with Sauce Labs and our bi-directional integrations that correlate test results with code changes, environmental or deployment changes, and production incidents will help software development teams improve release velocity, application reliability, and customer experience all while providing security intelligence.”

This new initiative comes as organizations accelerate their digital transformations to create a competitive advantage. The combination of the Sauce Labs Continuous Testing Cloud with Sumo Logic’s suite of Observability solutions including the Software Development Optimization solution, ensures customers have comprehensive visibility across their entire CI/CD pipelines to securely develop and release new applications or modernize existing applications faster and with higher quality and reliability than ever before.

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Sauce Labs Partners with Sumo Logic

Sauce Labs and Sumo Logic announced the formation of a new joint initiative designed to help enterprise organizations drive increased engineering efficiency by delivering greater visibility into the health and security of applications throughout the entire development lifecycle.

The combination will enable customers to leverage their test data from the Sauce Labs Continuous Testing Cloud with Sumo Logic’s Software Development Optimization solution and correlate it with data from other parts of the software development pipeline to automatically derive actionable insights that help improve the quality and reliability of applications.

The new joint initiative, which builds on the companies’ long history of partnership, collaboration, and the already strong integration between their respective platforms, empowers customers to view rich test data from Sauce Labs within Sumo Logic’s cloud-native, Continuous Intelligence Platform™ in order to make better, more informed decisions about the efficacy of their engineering and development processes.

“As testing increasingly becomes a continuous initiative, the ability to find and remedy defects before deployment plays a critical role in building digital confidence,” said Matt Wyman, CPO, Sauce Labs. “The combination of Sauce Labs and Sumo Logic gives customers complete visibility into the health of their applications throughout the entire product development lifecycle, both pre-production, and post. We’re excited to work with the Sumo Logic team to help organizations get more from their engineering investments.”

"As companies move from traditional business models to digital business models, they become software companies and their success depends on the speed and reliability of their digital services. Modern software development lifecycle (SDLC) and associated tools generate data that can be used to optimize and improve the SDLC in turn improving a company's agility and rate of innovation and thus making it more competitive," said Bruno Kurtic, founding VP of Strategy and Solutions, Sumo Logic. "Our partnership with Sauce Labs and our bi-directional integrations that correlate test results with code changes, environmental or deployment changes, and production incidents will help software development teams improve release velocity, application reliability, and customer experience all while providing security intelligence.”

This new initiative comes as organizations accelerate their digital transformations to create a competitive advantage. The combination of the Sauce Labs Continuous Testing Cloud with Sumo Logic’s suite of Observability solutions including the Software Development Optimization solution, ensures customers have comprehensive visibility across their entire CI/CD pipelines to securely develop and release new applications or modernize existing applications faster and with higher quality and reliability than ever before.

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.