
LambdaTest announced an integration with Datadog, Inc.
This integration is now available in the Datadog Marketplace.
With the LambdaTest and Datadog integration, users can purchase a subscription to LambdaTest through the Datadog Marketplace. Once the LambdaTest account is set up (or existing customers of LambdaTest), users can now go to the integration tile to start sending data into Datadog. In addition, users can log bugs while performing cross-browser testing of websites (and web apps) from the LambdaTest platform to Datadog. LambdaTest will automatically include test run details like testing environment, browser version, OS, resolution, screenshots, as well as add custom comments.
"Since our inception, we have consistently worked towards ensuring that LambdaTest platform remains a part of the integrated testing ecosystem. Developers and testers use multiple tools to do their jobs and it's important that these tools talk with each other. This new integration, which is now part of the 120+ integrations stable of LambdaTest, will enable smooth issue reporting and collaboration among testing and development teams. We will continuously look to increase our integrations in the times to come to ensure ease of work for our end-users," said Asad Khan, CEO, LambdaTest.
"Offering LambdaTest's integration will enable seamless incident management for users, so that they can send any bug reported on LambdaTest directly to the Datadog incident dashboard easily and without added steps." said Michael Gerstenhaber, Senior Director of Product Management at Datadog.
LambdaTest offers automation testing on a scalable, secure, and reliable cloud grid. Users can test builds continuously on its cloud infrastructure for fast and comprehensive feedback. Users can cut commit-to-deploy time by 10x with parallel test execution and can even debug on the go.
The Latest
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 ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...